VOC2010 PRELIMINARY RESULTS

Classification Results: VOC2010 data

Competition "comp1" (train on VOC2010 data)

Average Precision (AP %)

	aero plane	bicycle	bird	boat	bottle	bus	car	cat	chair	cow	dining table	dog	horse	motor bike	person	potted plant	sheep	sofa	train	tv/ monitor
BONN_FGT_SEGM	88.0	61.6	53.1	63.3	34.8	77.5	72.3	71.1	41.1	56.0	39.6	64.3	68.9	75.4	87.5	32.5	59.3	40.8	78.7	61.4
BUPT_LPBETA_MULTFEAT	82.1	38.6	39.5	46.5	15.5	55.0	46.4	46.5	39.9	21.3	31.2	37.6	45.8	41.4	75.5	15.6	41.7	25.0	62.5	44.3
BUPT_SPM_SC_HOG	79.6	47.0	42.9	52.3	21.3	66.6	50.1	58.7	44.3	21.8	32.7	46.0	49.7	51.7	72.4	13.2	44.1	28.1	61.5	48.8
BUPT_SVM_MULTFEAT	81.1	45.3	47.3	46.3	20.1	42.3	36.4	49.1	37.5	20.6	38.5	43.8	44.9	54.4	68.6	18.0	48.2	26.0	57.7	40.3
BUT_FU_SVM_SIFT	89.7	63.9	64.5	68.3	36.8	77.9	68.5	72.0	57.2	47.2	56.7	63.5	66.8	74.2	85.0	32.8	54.3	49.1	82.6	66.8
CVC_FLAT	89.4	57.6	63.0	68.5	32.0	76.7	64.7	66.9	51.5	48.4	50.0	54.8	63.1	69.9	83.5	33.6	54.8	46.1	82.2	65.9
CVC_PLUS	91.0	61.8	66.7	71.1	37.7	78.9	67.8	72.2	55.8	51.0	55.8	59.4	65.3	73.0	84.0	39.9	56.9	48.5	83.9	68.1
CVC_PLUSDET	91.7	70.0	66.8	71.3	49.0	81.4	77.5	71.2	60.0	52.6	55.7	61.0	70.9	76.7	88.4	43.2	59.7	53.8	84.7	71.3
HIT_PROTOLEARN_2	60.7	22.1	22.7	29.0	15.0	34.9	27.8	31.6	31.9	14.1	17.4	28.9	24.0	20.6	55.8	9.2	22.0	16.8	30.9	24.6
LIG_MSVM_FUSE_CONCEPT	74.4	43.0	37.5	50.4	22.0	60.7	47.1	46.8	47.5	22.2	35.0	42.1	42.9	48.4	73.8	15.6	31.8	28.9	63.8	46.6
LIP6UPMC_KSVM_BASELINE	78.4	54.1	49.9	61.1	24.6	68.3	58.0	59.9	50.7	35.7	42.5	55.0	60.8	63.1	71.1	25.9	51.5	39.9	74.1	59.6
LIP6UPMC_MKL_L1	78.5	55.9	54.6	62.5	25.0	69.3	59.5	60.0	51.3	37.9	46.7	54.0	60.5	64.0	72.8	32.8	52.6	38.5	72.7	61.1
LIP6UPMC_RANKING	78.8	51.3	46.1	58.2	19.5	68.6	55.6	59.4	46.8	30.7	36.0	49.3	52.3	60.0	76.3	17.8	49.1	35.3	66.3	56.6
LIRIS_MKL_TRAINVAL	87.5	57.0	61.7	68.2	29.9	76.6	61.9	67.5	56.9	35.1	50.6	55.1	62.2	69.3	83.6	35.9	52.9	42.7	79.8	66.3
NEC_V1_HOGLBP_NONLIN_SVM	93.3	71.7	69.9	76.9	42.0	85.3	77.4	79.3	60.0	55.8	60.6	71.1	75.7	77.7	86.8	33.5	61.5	55.8	87.5	69.9
NEC_V1_HOGLBP_NONLIN_SVMDET	93.3	72.9	69.9	77.2	47.9	85.6	79.7	79.4	61.7	56.6	61.1	71.1	76.7	79.3	86.8	38.1	63.9	55.8	87.5	72.9
NII_SVMSIFT	69.3	40.3	27.3	44.1	19.5	54.1	23.9	44.4	42.9	20.3	31.1	37.5	36.6	40.5	68.8	9.3	24.6	20.2	55.6	43.9
NLPR_VSTAR_CLS_DICTLEARN	90.3	77.0	65.3	75.0	53.7	85.9	80.4	74.6	62.9	66.2	54.1	66.8	76.1	81.7	89.9	41.6	66.3	57.0	85.0	74.3
NTHU_LINSPARSE_2	77.9	44.0	37.4	48.5	19.0	63.6	49.0	51.0	45.5	27.6	32.1	41.7	46.9	49.7	68.5	13.2	40.3	30.1	61.7	46.3
NUDT_SVM_LDP_SIFT_PMK_SPMK	86.1	59.3	60.2	68.7	28.7	74.8	63.5	68.0	52.5	41.4	47.1	57.5	60.9	68.2	81.5	29.4	52.1	44.5	79.1	4.7
NUDT_SVM_WHGO_SIFT_CENTRIST_LLM	83.5	54.2	55.2	66.8	28.5	72.1	65.4	64.2	51.9	36.1	49.3	55.6	58.0	66.5	82.1	25.3	48.1	41.7	78.4	59.5
NUSPSL_EXCLASSIFIER	91.3	77.0	70.0	75.6	50.7	83.2	77.1	75.4	62.5	62.6	62.7	64.6	77.9	81.8	91.1	44.8	64.2	53.2	86.3	77.1
NUSPSL_KERNELREGFUSING	93.0	79.0	71.6	77.8	54.3	85.2	78.6	78.8	64.5	64.0	62.7	69.6	82.0	84.4	91.6	48.6	64.9	59.6	89.4	76.4
NUSPSL_MFDETSVM	91.9	77.1	69.5	74.7	52.5	84.3	77.3	76.2	63.0	63.5	62.9	65.0	79.5	83.2	91.2	45.5	65.4	55.0	87.0	77.2
RITSU_CBVR_WKF	85.6	57.2	54.9	64.5	29.2	71.2	57.1	63.2	53.9	37.6	49.6	54.7	58.7	67.9	80.1	29.2	52.1	43.5	76.4	60.9
SURREY_MK_KDA	90.6	66.1	67.2	70.6	36.0	79.7	69.8	73.4	58.4	50.7	60.1	65.2	69.8	76.9	87.0	42.5	59.6	49.9	85.2	71.3
TIT_SIFT_GMM_MKL	87.2	56.6	59.6	66.0	32.6	72.7	63.1	64.8	54.6	41.2	49.3	58.8	59.1	68.2	82.9	31.2	49.2	43.2	75.0	63.4
UC3M_GENDISC	85.5	51.6	55.4	64.8	25.9	74.4	60.6	66.0	51.0	45.9	43.9	55.0	59.0	65.2	80.3	24.0	51.4	47.0	76.4	58.6
UVA_BW_NEWCOLOURSIFT	91.5	71.0	67.3	69.9	43.9	80.6	75.3	73.4	59.3	57.8	60.8	64.0	70.6	80.0	88.6	50.8	65.6	56.1	83.0	76.2
UVA_BW_NEWCOLOURSIFT_SRKDA	90.6	66.9	63.4	70.2	49.4	81.8	76.7	70.9	60.0	57.1	60.5	64.5	67.4	79.1	90.2	53.3	63.5	58.0	81.9	74.4
WLU_SPM_EMDIST	75.8	48.9	36.8	44.3	21.2	65.8	52.1	52.1	45.4	28.2	35.0	45.3	47.8	54.2	71.0	14.7	39.8	32.7	62.2	48.0
XRCE_IFV	87.1	59.6	59.9	69.7	31.3	76.4	62.9	64.3	52.5	42.4	55.1	59.7	64.3	70.4	83.9	32.6	53.3	50.4	80.0	67.6

Precision/Recall Curves

Classification Results: VOC2010 data

Competition "comp2" (train on own data)

Average Precision (AP %)

	aero plane	bicycle	bird	boat	bottle	bus	car	cat	chair	cow	dining table	dog	horse	motor bike	person	potted plant	sheep	sofa	train	tv/ monitor
BIT_LINSVM_PHOW	59.7	28.8	17.4	29.7	12.5	25.3	28.3	32.2	34.3	15.7	24.5	26.3	31.2	21.5	43.8	7.4	15.6	18.2	37.9	27.3
UCI_LSVM_MDPM_10X	-	65.1	-	-	-	78.1	-	-	-	43.8	16.9	-	64.0	60.4	-	-	53.1	25.0	-	58.7
XRCE_IFV_1M	92.7	68.0	69.0	79.9	29.3	81.4	60.0	78.0	45.0	62.9	31.6	69.2	71.2	78.6	78.0	34.0	67.3	-	82.7	-
XRCE_IFV_FUSE_OPT	92.7	68.4	68.5	80.4	38.2	81.8	66.9	77.8	55.0	62.1	56.5	70.1	71.4	79.4	85.0	40.0	67.2	51.8	84.6	67.6

Precision/Recall Curves

Detection Results: VOC2010 data

Competition "comp3" (train on VOC2010 data)

Average Precision (AP %)

	aero plane	bicycle	bird	boat	bottle	bus	car	cat	chair	cow	dining table	dog	horse	motor bike	person	potted plant	sheep	sofa	train	tv/ monitor
BONN_FGT_SEGM	52.7	33.7	13.2	11.0	14.2	43.1	31.9	35.6	5.7	25.4	14.4	20.6	38.1	41.7	25.0	5.8	26.3	18.1	37.6	28.1
BONN_SVR_SEGM	50.5	24.4	17.1	13.3	10.9	39.5	32.9	36.5	5.6	16.0	6.6	22.3	24.9	29.0	29.8	6.7	28.4	13.3	32.1	27.2
CMIC_SYNTHTRAIN	-	28.9	-	-	-	30.2	13.3	-	-	-	-	-	26.2	28.1	13.2	-	-	-	18.8	25.7
CMIC_VARPARTS	-	28.2	-	-	-	26.9	13.7	-	-	-	-	-	23.5	24.7	16.1	-	-	-	18.8	24.5
CMU_RANDPARTS	23.8	31.7	1.2	3.4	11.1	29.7	19.5	14.2	0.8	11.1	7.0	4.7	16.4	31.5	16.0	1.1	15.6	10.2	14.7	21.0
CMU_RANDPARTS_MAXSCORE	-	-	2.7	-	-	-	-	16.2	-	10.6	8.5	-	-	-	17.9	-	-	-	15.7	-
LJKINPG_HOG_LBP_LTP_PLS2ROOTS	32.7	29.7	0.8	1.1	19.8	39.4	27.5	8.6	4.5	8.1	6.3	11.0	22.9	34.1	24.6	3.1	24.0	2.0	23.5	27.0
MITUCLA_HIERARCHY	54.2	48.5	15.7	19.2	29.2	55.5	43.5	41.7	16.9	28.5	26.7	30.9	48.3	55.0	41.7	9.7	35.8	30.8	47.2	40.8
NLPR_HOGLBP_MC_LCEGCHLC	53.3	55.3	19.2	21.0	30.0	54.4	46.7	41.2	20.0	31.5	20.7	30.3	48.6	55.3	46.5	10.2	34.4	26.5	50.3	40.3
NUS_HOGLBP_CTX_CLS_RESCORE_V2	49.1	52.4	17.8	12.0	30.6	53.5	32.8	37.3	17.7	30.6	27.7	29.5	51.9	56.3	44.2	9.6	14.8	27.9	49.5	38.4
TIT_SIFT_GMM_MKL	10.5	1.6	1.2	0.9	0.1	2.8	1.6	6.7	0.1	2.0	0.4	3.0	2.0	4.4	2.0	0.3	1.1	1.2	2.1	1.9
TIT_SIFT_GMM_MKL2	20.0	14.5	3.8	1.2	0.5	17.6	8.1	28.5	0.1	2.9	3.1	17.5	7.2	18.8	3.3	0.8	2.9	6.3	7.6	1.1
UC3M_GENDISC	15.8	5.5	5.6	2.3	0.3	10.2	5.4	12.6	0.5	5.6	4.5	7.7	11.3	12.6	5.3	1.5	2.0	5.9	9.1	3.2
UCI_DPM_SP	46.1	52.6	13.8	15.5	28.3	53.2	44.5	26.6	17.6	-	16.1	20.4	45.5	51.2	43.5	11.6	30.9	20.3	47.6	-
UMNECUIUC_HOGLBP_DHOGBOW_SVM	40.4	34.7	2.7	8.4	26.0	43.1	33.8	17.2	11.2	14.3	14.4	14.9	31.8	37.3	30.0	6.4	25.2	11.6	30.0	35.7
UMNECUIUC_HOGLBP_LINSVM	37.9	33.7	2.7	6.5	25.3	37.5	33.1	15.5	10.9	12.3	12.5	13.7	29.6	34.5	33.8	7.2	22.9	9.9	28.9	34.1
UOCTTI_LSVM_MDPM	52.4	54.3	13.0	15.6	35.1	54.2	49.1	31.8	15.5	26.2	13.5	21.5	45.4	51.6	47.5	9.1	35.1	19.4	46.6	38.0
UVA_DETMONKEY	56.7	39.8	16.8	12.2	13.8	44.9	36.9	47.7	12.1	26.9	26.5	37.2	42.1	51.9	25.7	12.1	37.8	33.0	41.5	41.7
UVA_GROUPLOC	58.4	39.6	18.0	13.3	11.1	46.4	37.8	43.9	10.3	27.5	20.8	36.0	39.4	48.5	22.9	13.0	36.8	30.5	41.2	41.9

Precision/Recall Curves

Detection Results: VOC2010 data

Competition "comp4" (train on own data)

Average Precision (AP %)

	aero plane	bicycle	bird	boat	bottle	bus	car	cat	chair	cow	dining table	dog	horse	motor bike	person	potted plant	sheep	sofa	train	tv/ monitor
BERKELEY_POSELETS	33.2	51.9	8.5	8.2	34.8	39.0	48.8	22.2	-	20.6	-	18.5	48.2	44.1	48.5	9.1	28.0	13.0	22.5	33.0
CVITVGG_HEADDETSEG	-	-	-	-	-	-	-	41.7	-	-	-	-	-	-	-	-	-	-	-	-
UCI_LSVM_MDPM_10X	-	48.1	-	-	-	54.7	-	-	-	25.1	6.0	-	46.6	41.1	-	-	31.2	17.7	-	32.3

Precision/Recall Curves

Segmentation Results (VOC2010 data)

Competition "comp5" (train on VOC2010 data)

Accuracy (%)

- Entries in parentheses are synthesized from detection results.

	[mean]	back ground	aero plane	bicycle	bird	boat	bottle	bus	car	cat	chair	cow	dining table	dog	horse	motor bike	person	potted plant	sheep	sofa	train	tv/ monitor
BONN_FGT_SEGM	36.5	82.5	54.6	22.5	25.1	27.6	40.0	60.2	48.3	39.4	7.3	30.8	21.3	25.3	34.9	54.1	36.6	22.5	45.0	17.6	33.5	37.0
BONN_SVR_SEGM	39.7	84.2	52.5	27.4	32.3	34.5	47.4	60.6	54.8	42.6	9.0	32.9	25.2	27.1	32.4	47.1	38.3	36.8	50.3	21.9	35.2	40.9
BROOKES_AHCRF	30.3	70.1	31.0	18.8	19.5	23.9	31.3	53.5	45.3	24.4	8.2	31.0	16.4	15.8	27.3	48.1	31.1	31.0	27.5	19.8	34.8	26.4
CVC_HARMONY	35.4	80.8	56.7	20.6	31.0	33.9	20.8	57.6	51.4	35.8	7.1	28.1	22.6	24.3	29.3	49.4	37.8	23.3	37.6	18.1	45.6	30.7
CVC_HARMONY_DET	40.1	81.1	58.3	23.1	39.0	37.8	36.4	63.2	62.4	31.9	9.1	36.8	24.6	29.4	37.5	60.6	44.9	30.1	36.8	19.4	44.1	35.9
STANFORD_REGLABEL	29.1	80.0	38.8	21.5	13.6	9.2	31.1	51.8	44.4	25.7	6.7	26.0	12.5	12.8	31.0	41.9	44.4	5.7	37.5	10.0	33.2	32.3
UC3M_GENDISC	27.8	73.4	45.9	12.3	14.5	22.3	9.3	46.8	38.3	41.7	0.0	35.9	20.7	34.1	34.8	33.5	24.6	4.7	25.6	13.0	26.8	26.1
UOCTTI_LSVM_MDPM	31.8	80.0	36.7	23.9	20.9	18.8	41.0	62.7	49.0	21.5	8.3	21.1	7.0	16.4	28.2	42.5	40.5	19.6	33.6	13.3	34.1	48.5
(CMU_RANDPARTS)	12.2	4.3	7.9	10.1	4.7	5.4	20.5	38.9	12.1	11.9	1.7	15.1	3.8	5.7	12.5	25.4	7.6	2.7	19.7	12.1	11.8	23.1
(LJKINPG_HOG_LBP_LTP_PLS2ROOTS)	6.7	0.5	18.6	10.2	0.0	0.0	12.5	26.1	10.2	1.5	0.5	0.0	0.0	3.9	2.9	7.7	5.6	0.0	2.9	0.8	8.5	28.0
(MITUCLA_HIERARCHY)	15.4	0.5	14.2	6.6	10.5	5.9	39.9	30.9	26.8	21.8	4.2	12.0	15.5	10.8	18.1	24.1	11.7	8.0	21.0	11.5	16.0	13.5
(NLPR_HOGLBP_MC_LCEGCHLC)	14.4	2.2	9.6	7.3	11.5	5.8	10.8	39.8	24.4	18.5	5.3	12.4	10.5	15.0	15.5	20.2	21.8	3.2	22.0	7.7	20.0	17.9
(NUS_HOGLBP_CTX_CLS_RESCORE_V2)	9.5	1.4	7.9	12.0	4.2	7.0	4.3	42.5	27.6	2.9	0.3	19.5	5.9	2.4	10.5	16.3	1.5	2.0	0.1	6.0	14.2	11.2
(TIT_SIFT_GMM_MKL)	12.3	13.7	12.7	7.5	6.8	6.9	18.7	29.3	14.5	16.9	6.2	7.4	11.0	11.6	10.4	17.0	9.7	6.4	11.6	8.1	14.3	18.6
(TIT_SIFT_GMM_MKL2)	14.9	21.7	16.6	10.5	9.3	13.8	17.9	42.2	14.9	17.6	6.3	9.9	2.5	11.1	12.5	20.7	8.5	6.2	21.8	7.6	19.9	20.5
(UMNECUIUC_HOGLBP_DHOGBOW_SVM)	11.1	4.8	6.9	6.7	3.2	4.7	20.8	37.3	13.6	10.4	3.4	12.6	8.4	5.8	8.4	14.8	12.0	5.1	14.5	7.3	13.8	19.1
(UMNECUIUC_HOGLBP_LINSVM)	9.7	2.8	4.3	9.5	1.3	0.5	21.9	33.1	17.0	6.8	3.2	1.7	4.6	5.1	8.6	14.5	8.1	1.0	15.6	5.6	14.0	25.3
(UVA_DETMONKEY)	14.7	2.4	13.8	7.8	3.8	6.3	18.5	48.8	36.2	19.2	3.8	11.0	4.5	14.5	10.7	23.6	11.1	6.4	22.7	10.1	14.8	19.9
(UVA_GROUPLOC)	13.8	3.1	15.9	7.3	6.6	5.5	14.4	43.9	24.0	19.0	3.1	11.8	6.2	11.4	11.3	25.0	13.2	3.3	17.9	9.0	15.7	21.8

Segmentation Results (VOC2010 data)

Competition "comp6" (train on own data)

Accuracy (%)

- Entries in parentheses are synthesized from detection results.

	[mean]	back ground	aero plane	bicycle	bird	boat	bottle	bus	car	cat	chair	cow	dining table	dog	horse	motor bike	person	potted plant	sheep	sofa	train	tv/ monitor
BERKELEY_POSELETS_ALIGN_PB	34.7	82.0	49.7	23.3	20.6	19.0	47.1	58.1	53.6	32.5	0.0	31.1	0.0	29.5	42.9	41.9	43.8	16.6	39.0	18.4	38.0	41.5

Person Layout Results: VOC2010 data

Competition "comp7" (train on VOC2010 data)

Average Precision (AP %)

	Head	Hand	Foot

Precision/Recall Curves

head
hand
foot

Person Layout Results: VOC2010 data

Competition "comp8" (train on own data)

Average Precision (AP %)

	Head	Hand	Foot
BCNPCL_HumanLayout	74.4	3.3	1.2
OXFORD_SBD	52.7	10.4	0.0

Precision/Recall Curves

Action Classification Results: VOC2010 data

Competition "comp9" (train on VOC2010 data)

Average Precision (AP %)

	phoning	playing instrument	reading	riding bike	riding horse	running	taking photo	using computer	walking
BONN_ACTION	47.5	51.1	31.9	64.5	69.1	78.5	32.4	53.9	61.1
CVC_BASE	56.2	56.5	34.7	75.1	83.6	86.5	25.4	60.0	69.2
CVC_SEL	49.8	52.8	34.3	74.2	85.5	85.1	24.9	64.1	72.5
INRIA_SPM_HT	53.2	53.6	30.2	78.2	88.4	84.6	30.4	60.9	61.8
NUDT_SVM_WHGO_SIFT_CENTRIST_LLM	47.2	47.9	24.5	74.2	81.0	79.5	24.9	58.6	71.5
SURREY_MK_KDA	52.6	53.5	35.9	81.0	89.3	86.5	32.8	59.2	68.6
UCLEAR_SVM_DOSP_MULTFEATS	47.0	57.8	26.9	78.8	89.7	87.3	32.5	60.0	70.1
UMCO_DHOG_KSVM	53.5	43.0	32.0	67.9	68.8	83.0	34.1	45.9	60.4
WILLOW_A_SVMSIFT_1-A_LSVM	49.2	37.7	22.2	73.2	77.1	81.7	24.3	53.7	56.9
WILLOW_LSVM	40.4	29.9	32.2	53.5	62.2	73.6	17.6	45.8	41.5
WILLOW_SVMSIFT	47.9	29.1	21.7	53.5	76.7	78.3	26.0	42.9	56.4

Precision/Recall Curves

Action Classification Results: VOC2010 data

Competition "comp10" (train on own data)

Average Precision (AP %)

	phoning	playing instrument	reading	riding bike	riding horse	running	taking photo	using computer	walking
BERKELEY_POSELETS_ACTION	45.9	45.8	23.7	79.9	87.6	83.1	26.2	44.9	66.6

Precision/Recall Curves

Classification Results: VOC2009 data

Competition "comp1" (train on VOC2010 data)

Average Precision (AP %)

	aero plane	bicycle	bird	boat	bottle	bus	car	cat	chair	cow	dining table	dog	horse	motor bike	person	potted plant	sheep	sofa	train	tv/ monitor
BONN_FGT_SEGM	84.4	61.2	56.7	61.0	38.5	73.6	68.6	69.4	44.6	55.9	39.9	58.5	70.1	73.0	86.9	35.3	53.2	42.3	78.9	61.6
BUPT_LPBETA_MULTFEAT	79.1	41.5	45.5	45.5	22.3	52.7	44.2	44.1	41.3	26.4	34.2	33.4	47.4	42.7	77.0	16.6	37.5	30.0	65.5	43.8
BUPT_SPM_SC_HOG	77.3	47.4	48.0	51.7	24.6	62.6	46.6	55.3	46.1	27.7	37.0	43.0	52.8	51.8	73.8	19.0	39.4	33.7	64.1	49.8
BUPT_SVM_MULTFEAT	78.4	47.4	51.9	46.1	20.4	39.4	35.1	49.3	38.5	24.7	41.4	39.3	48.3	53.2	71.9	18.9	42.5	29.7	63.4	41.2
BUT_FU_SVM_SIFT	85.2	63.0	67.0	66.2	37.6	73.0	65.1	68.4	57.0	48.8	55.5	56.7	67.7	71.4	84.3	33.4	50.0	51.1	82.6	65.8
CVC_FLAT	85.5	57.4	65.8	66.2	34.5	71.9	61.2	64.3	51.8	48.5	50.1	48.2	64.6	66.7	83.2	34.4	49.5	48.6	82.8	65.6
CVC_PLUS	87.3	61.1	68.7	69.2	39.5	74.3	64.2	69.1	55.8	52.1	55.3	52.4	67.8	70.1	83.8	39.2	51.3	50.3	84.0	67.5
CVC_PLUSDET	88.6	68.7	68.6	68.3	50.2	76.1	74.0	68.6	60.3	53.2	56.2	54.6	72.1	73.9	87.2	42.9	54.1	53.6	84.7	70.5
HIT_PROTOLEARN_2	60.0	23.1	29.1	26.5	17.0	31.9	28.7	27.1	31.5	15.0	16.8	23.1	27.0	21.5	60.8	9.5	17.3	18.9	32.6	24.6
LIG_MSVM_FUSE_CONCEPT	71.9	44.0	42.9	50.2	28.1	59.5	44.1	45.4	48.3	23.8	35.2	38.5	46.6	47.8	75.1	20.2	29.7	33.5	66.0	47.9
LIP6UPMC_KSVM_BASELINE	76.4	52.8	54.1	59.8	26.0	64.1	53.9	56.2	52.0	39.6	43.5	49.9	62.7	61.7	72.9	27.4	44.3	43.2	75.7	59.9
LIP6UPMC_MKL_L1	75.7	55.6	57.6	60.5	29.5	64.3	55.6	57.6	51.7	42.3	46.5	47.4	62.6	62.3	74.5	32.3	46.2	42.4	74.3	61.6
LIP6UPMC_RANKING	76.3	50.7	48.8	56.4	24.9	64.8	51.3	55.7	48.7	34.0	37.9	44.5	54.8	58.5	77.4	17.1	42.1	38.7	68.8	56.6
LIRIS_MKL_TRAINVAL	83.2	56.9	64.1	65.6	33.2	70.6	58.0	64.4	56.9	37.8	50.1	48.6	64.3	66.1	83.3	36.5	46.4	47.1	80.5	65.5
NEC_V1_HOGLBP_NONLIN_SVM	89.6	71.0	70.8	74.1	42.7	80.4	73.5	75.8	60.0	59.0	60.5	64.4	75.4	74.0	86.2	35.6	55.5	57.2	86.7	68.1
NEC_V1_HOGLBP_NONLIN_SVMDET	89.6	71.7	70.8	74.3	48.9	80.5	75.7	76.0	61.2	59.5	61.1	64.5	76.3	75.7	86.2	39.9	57.6	57.2	86.7	71.1
NII_SVMSIFT	66.8	42.9	32.8	43.8	20.6	52.4	26.6	41.9	44.2	21.3	30.2	34.1	40.7	41.0	71.5	17.3	18.4	24.6	57.8	45.6
NLPR_VSTAR_CLS_DICTLEARN	86.6	74.2	67.7	71.9	54.4	81.1	76.3	71.7	62.4	65.8	55.8	60.9	76.1	77.7	88.3	43.5	59.8	57.7	85.4	72.0
NTHU_LINSPARSE_2	75.8	43.9	42.1	47.4	19.8	59.2	46.8	47.5	46.1	34.1	35.3	39.4	50.3	49.9	70.9	16.1	34.1	34.0	62.7	47.9
NUDT_SVM_LDP_SIFT_PMK_SPMK	83.0	59.3	62.9	66.7	31.7	70.2	58.8	65.6	54.0	44.7	47.1	50.4	63.7	65.2	81.6	31.1	45.4	47.1	79.3	5.0
NUDT_SVM_WHGO_SIFT_CENTRIST_LLM	80.8	54.7	59.0	65.0	31.7	67.6	61.5	61.8	51.9	40.0	50.2	49.0	60.4	63.6	81.9	27.5	43.5	44.4	79.0	59.7
NUSPSL_EXCLASSIFIER	86.8	74.8	71.1	72.4	53.2	77.8	73.8	73.1	62.0	61.0	62.0	59.7	77.5	78.3	89.5	45.6	60.7	53.5	86.9	74.2
NUSPSL_KERNELREGFUSING	88.1	77.1	73.2	74.8	55.3	80.4	74.6	76.3	63.3	64.0	62.4	64.2	81.3	79.9	89.8	47.7	60.2	59.2	87.6	73.8
NUSPSL_MFDETSVM	88.3	74.8	71.1	71.6	54.1	79.2	74.2	73.2	62.5	62.7	61.7	59.4	78.7	79.0	89.5	45.6	60.1	55.4	86.2	74.6
RITSU_CBVR_WKF	82.0	57.0	58.6	62.6	33.2	66.2	53.3	60.4	54.3	40.3	47.5	48.4	61.5	65.6	80.6	30.9	47.3	45.4	77.7	59.5
SURREY_MK_KDA	86.4	65.3	68.3	68.3	38.1	74.6	66.3	70.7	58.2	53.3	57.6	58.0	70.7	73.3	86.0	41.5	52.8	53.2	85.5	69.9
TIT_SIFT_GMM_MKL	83.6	56.8	62.2	64.2	36.0	69.6	59.0	61.9	54.3	45.7	49.5	53.5	61.7	66.8	83.0	32.6	43.1	45.2	76.6	61.9
UC3M_GENDISC	81.7	52.4	58.3	63.5	29.5	69.5	56.6	63.5	50.6	47.5	44.9	50.4	61.9	62.1	80.1	24.6	47.8	47.6	78.1	58.5
UVA_BW_NEWCOLOURSIFT	88.1	69.4	68.5	67.5	45.3	75.0	71.7	69.4	58.8	58.5	58.9	57.6	72.1	75.1	87.3	49.7	59.2	56.6	83.2	75.2
UVA_BW_NEWCOLOURSIFT_SRKDA	86.7	65.7	64.6	67.0	50.2	77.2	72.9	67.6	60.0	57.6	59.1	57.6	70.2	73.8	88.5	52.9	57.1	58.2	83.4	72.8
WLU_SPM_EMDIST	73.2	49.2	42.6	44.7	26.5	61.9	48.9	49.1	46.7	31.7	39.1	42.5	51.9	53.4	73.0	16.9	34.7	37.4	64.3	48.8
XRCE_IFV	83.2	59.4	62.2	67.1	34.1	70.7	58.4	61.1	52.4	45.8	52.5	53.5	66.3	66.9	83.5	32.7	46.2	53.2	80.4	66.3

Precision/Recall Curves

Classification Results: VOC2009 data

Competition "comp2" (train on own data)

Average Precision (AP %)

	aero plane	bicycle	bird	boat	bottle	bus	car	cat	chair	cow	dining table	dog	horse	motor bike	person	potted plant	sheep	sofa	train	tv/ monitor
BIT_LINSVM_PHOW	64.7	31.6	22.9	33.2	13.9	28.2	27.7	34.3	38.4	20.0	30.0	28.9	38.3	24.9	51.0	7.4	15.5	25.7	42.5	29.1
UCI_LSVM_MDPM_10X	-	64.3	-	-	-	73.8	-	-	-	44.8	21.6	-	66.1	59.8	-	-	47.3	29.3	-	58.0
XRCE_IFV_1M	88.0	68.2	69.6	76.0	30.7	75.4	55.6	74.8	46.7	62.9	35.7	62.2	72.7	74.2	79.1	34.6	61.9	-	82.6	-
XRCE_IFV_FUSE_OPT	88.0	68.6	69.6	76.8	40.4	76.8	62.7	74.6	54.8	62.5	54.4	63.2	72.3	75.1	84.6	38.4	61.8	54.5	84.2	66.3

Precision/Recall Curves

Detection Results: VOC2009 data

Competition "comp3" (train on VOC2010 data)

Average Precision (AP %)

	aero plane	bicycle	bird	boat	bottle	bus	car	cat	chair	cow	dining table	dog	horse	motor bike	person	potted plant	sheep	sofa	train	tv/ monitor
BONN_FGT_SEGM	52.7	35.7	17.0	15.4	17.4	40.9	30.5	37.9	11.5	26.6	19.6	22.5	41.2	40.5	28.0	12.2	26.8	21.4	42.5	29.4
BONN_SVR_SEGM	50.3	25.2	21.0	18.0	13.1	39.3	31.1	39.3	10.3	17.4	11.8	23.2	26.0	26.2	31.4	11.8	28.8	16.7	36.7	28.9
CMIC_SYNTHTRAIN	-	30.9	-	-	-	28.8	17.2	-	-	-	-	-	30.0	30.1	18.6	-	-	-	25.0	27.0
CMIC_VARPARTS	-	29.8	-	-	-	28.2	16.7	-	-	-	-	-	27.7	28.1	20.7	-	-	-	24.5	25.8
CMU_RANDPARTS	27.4	33.0	2.4	11.0	15.1	30.1	21.4	16.0	0.9	14.8	9.3	4.3	23.7	32.6	21.1	2.6	18.2	14.2	19.3	22.7
CMU_RANDPARTS_MAXSCORE	-	-	7.2	-	-	-	-	20.0	-	16.6	15.0	-	-	-	22.2	-	-	-	22.5	-
LJKINPG_HOG_LBP_LTP_PLS2ROOTS	34.0	31.6	4.8	5.8	22.8	36.8	26.7	13.6	10.6	14.8	13.7	13.8	27.2	35.6	27.3	10.0	23.9	9.4	28.8	28.3
MITUCLA_HIERARCHY	54.3	48.1	18.4	21.0	31.7	50.8	40.7	41.6	19.8	29.0	32.2	29.3	50.8	52.3	43.0	13.9	35.4	31.1	51.5	39.4
NLPR_HOGLBP_MC_LCEGCHLC	52.2	54.5	21.9	23.6	32.3	48.8	44.4	41.1	23.6	33.1	25.7	29.6	50.7	52.3	47.0	15.8	33.4	30.6	53.2	39.2
NUS_HOGLBP_CTX_CLS_RESCORE_V2	49.1	52.2	21.2	16.3	33.5	49.0	32.4	37.7	21.5	28.9	32.2	27.9	53.7	54.1	45.3	14.2	20.0	29.9	53.6	37.8
TIT_SIFT_GMM_MKL	17.0	1.9	1.4	0.9	0.2	4.6	1.3	7.2	0.4	3.6	0.5	2.5	3.3	4.6	10.1	9.1	2.8	9.6	10.5	2.5
TIT_SIFT_GMM_MKL2	24.4	18.0	6.3	2.9	4.5	19.7	11.5	28.4	0.3	6.0	5.6	18.0	10.3	18.1	10.7	9.1	9.1	10.1	9.1	4.5
UC3M_GENDISC	24.2	10.3	11.6	9.1	9.1	17.2	11.1	26.1	9.1	11.5	11.7	17.6	18.0	19.4	12.6	9.1	9.4	12.1	16.7	10.1
UCI_DPM_SP	46.2	52.4	16.8	17.8	31.6	49.0	42.3	27.7	20.9	-	21.5	20.6	48.2	49.4	44.5	15.2	29.7	20.7	50.8	-
UMNECUIUC_HOGLBP_DHOGBOW_SVM	41.8	37.4	9.8	12.6	28.5	39.4	33.3	20.3	15.3	19.0	19.7	16.9	34.9	38.1	34.0	10.3	24.7	15.7	33.4	34.5
UMNECUIUC_HOGLBP_LINSVM	39.2	35.9	9.8	9.4	27.6	34.6	32.1	17.9	14.9	17.5	18.2	16.0	33.6	35.8	35.8	10.9	22.6	14.9	33.5	34.2
UOCTTI_LSVM_MDPM	49.9	52.7	16.6	18.5	37.1	50.0	47.3	31.8	19.3	28.9	18.8	20.9	48.2	49.9	47.9	13.6	32.4	20.9	49.4	37.3
UVA_DETMONKEY	56.3	42.2	20.1	17.4	17.4	42.3	36.1	46.3	17.6	29.6	31.5	34.8	44.4	50.0	28.3	16.9	35.7	36.0	45.5	40.9
UVA_GROUPLOC	57.6	40.0	22.1	17.9	15.0	43.7	36.5	45.4	15.7	27.5	25.3	33.3	42.6	46.3	26.1	17.1	35.6	32.7	46.8	40.5

Precision/Recall Curves

Competition "comp4" (train on own data)

Average Precision (AP %)

	aero plane	bicycle	bird	boat	bottle	bus	car	cat	chair	cow	dining table	dog	horse	motor bike	person	potted plant	sheep	sofa	train	tv/ monitor
BERKELEY_POSELETS	33.0	51.2	12.0	12.3	37.5	37.3	46.9	26.7	-	25.2	-	21.1	50.9	44.6	48.6	14.6	25.9	17.2	27.3	32.5
CVITVGG_HEADDETSEG	-	-	-	-	-	-	-	42.8	-	-	-	-	-	-	-	-	-	-	-	-
UCI_LSVM_MDPM_10X	-	47.6	-	-	-	49.8	-	-	-	26.8	11.8	-	49.2	40.4	-	-	30.0	21.6	-	32.9

Precision/Recall Curves

Segmentation Results (VOC2009 data)

Competition "comp5" (train on VOC2010 data)

Accuracy (%)

- Entries in parentheses are synthesized from detection results.

	[mean]	back ground	aero plane	bicycle	bird	boat	bottle	bus	car	cat	chair	cow	dining table	dog	horse	motor bike	person	potted plant	sheep	sofa	train	tv/ monitor
BONN_FGT_SEGM	36.3	82.6	61.1	21.1	22.9	30.2	36.8	50.0	50.9	33.9	7.9	27.9	22.4	21.5	38.3	52.0	37.8	25.0	44.3	20.4	35.6	39.0
BONN_SVR_SEGM	38.9	84.4	52.8	25.3	31.5	39.5	45.7	50.7	55.5	35.5	9.8	31.7	26.4	19.0	34.7	40.2	39.7	39.6	48.1	26.9	37.4	42.9
BROOKES_AHCRF	31.0	70.2	30.7	19.0	19.5	30.2	36.9	50.0	43.9	21.9	7.5	31.7	16.5	17.3	30.6	45.6	32.1	32.9	29.2	21.4	35.5	29.5
CVC_HARMONY	34.5	80.6	56.5	21.1	30.1	38.5	22.5	50.0	49.1	30.6	6.2	30.3	22.8	18.9	30.0	44.0	37.6	20.8	35.0	21.2	46.4	32.9
CVC_HARMONY_DET	39.7	80.9	62.1	22.8	37.0	42.1	37.9	58.1	62.3	25.9	7.9	39.5	25.4	23.2	39.0	56.6	44.8	29.9	34.5	20.1	45.5	37.9
STANFORD_REGLABEL	29.0	80.1	39.4	21.5	16.0	9.8	32.4	44.5	43.7	21.2	6.8	24.1	13.8	12.0	34.3	38.6	45.9	3.9	40.7	12.2	33.6	34.4
UC3M_GENDISC	26.9	73.5	48.3	11.3	11.8	26.5	12.6	40.3	41.4	37.4	0.0	31.9	20.7	28.6	31.8	29.1	23.3	1.4	25.7	15.9	25.8	26.8
UOCTTI_LSVM_MDPM	32.0	80.8	42.1	23.6	22.5	21.6	43.6	59.7	52.0	12.7	6.6	19.6	6.1	15.7	29.6	43.3	41.4	24.0	27.3	16.6	34.2	49.6
(CMU_RANDPARTS)	12.4	4.1	7.6	10.2	5.2	5.7	19.0	33.4	10.5	8.9	1.4	17.3	4.1	5.2	13.5	27.7	7.8	3.4	22.3	14.5	13.2	25.4
(LJKINPG_HOG_LBP_LTP_PLS2ROOTS)	6.8	0.6	20.7	10.8	0.0	0.0	14.8	23.8	10.0	1.1	0.5	0.0	0.0	2.7	3.3	7.1	5.5	0.0	3.3	1.0	8.3	29.9
(MITUCLA_HIERARCHY)	14.9	0.6	12.5	6.6	10.6	7.0	43.1	25.6	24.0	18.0	3.2	11.7	15.8	9.7	20.3	20.7	11.6	8.1	19.2	11.9	16.1	16.2
(NLPR_HOGLBP_MC_LCEGCHLC)	13.8	2.3	7.8	7.4	11.9	6.8	11.4	34.6	21.0	15.1	4.9	14.5	11.0	12.9	16.2	17.0	22.3	3.2	20.6	8.3	20.1	21.8
(NUS_HOGLBP_CTX_CLS_RESCORE_V2)	9.6	1.4	7.5	12.9	5.1	8.1	4.4	37.4	28.6	2.2	0.4	18.0	3.8	0.6	10.8	19.9	1.6	1.9	0.2	7.5	16.5	12.9
(TIT_SIFT_GMM_MKL)	11.4	13.3	10.6	6.8	6.5	8.4	18.1	25.4	12.7	13.1	5.2	7.1	11.9	8.3	12.2	10.5	9.1	6.0	11.4	9.6	14.8	19.3
(TIT_SIFT_GMM_MKL2)	13.8	21.2	14.3	9.9	8.1	14.7	18.4	40.1	12.8	11.9	5.3	8.1	2.7	8.8	12.4	12.8	8.1	7.4	21.1	8.7	20.8	21.5
(UMNECUIUC_HOGLBP_DHOGBOW_SVM)	10.8	4.9	5.9	6.5	3.5	5.8	20.5	34.0	12.8	7.0	2.8	12.6	9.2	3.6	9.8	15.5	11.6	5.6	13.0	7.4	14.5	21.6
(UMNECUIUC_HOGLBP_LINSVM)	9.8	3.0	3.6	9.6	1.7	0.4	21.2	34.7	16.6	3.2	2.4	1.9	5.0	3.7	9.6	16.5	8.1	1.2	14.6	6.3	14.9	27.3
(UVA_DETMONKEY)	14.0	2.4	11.3	7.4	3.9	7.4	19.8	44.3	33.8	13.9	3.4	11.5	5.3	10.8	11.2	21.1	10.7	6.7	21.6	11.2	15.3	21.7
(UVA_GROUPLOC)	12.9	3.1	14.5	6.8	6.8	6.3	13.5	36.9	20.9	14.6	2.3	12.0	7.0	9.7	13.1	22.5	13.1	2.9	15.8	9.8	15.8	24.1

Segmentation Results (VOC2009 data)

Competition "comp6" (train on own data)

Accuracy (%)

- Entries in parentheses are synthesized from detection results.

	[mean]	back ground	aero plane	bicycle	bird	boat	bottle	bus	car	cat	chair	cow	dining table	dog	horse	motor bike	person	potted plant	sheep	sofa	train	tv/ monitor
BERKELEY_POSELETS_ALIGN_PB	36.0	82.9	61.2	24.2	22.9	19.9	47.6	52.8	54.0	30.5	0.0	30.9	0.0	27.8	46.0	50.2	45.8	16.7	38.8	22.5	39.8	41.8

Key to Abbreviations

Abbreviation	Title	Method	Affiliation	Contributors	Descriptiorn
BCNPCL_HumanLayout	BCNPCL_Human_Layout	Combining detectors for human layout analysis	Dept. de Matem�tica Aplicada i An�lisi, Facultat de Matem�tiques, Computer Vision Center Barcelona and Universitat Oberta de Catalunya	M. Drozdzal, A. Hern�ndez, S. Segu�, X. Bar�, S. Escalera, A. Lapedriza, D. Masip, P. Radeva, J. Vitri�	Combination of several detectors for a complex human layout detector. For details see BCNPCL_PASCAL2010.pptx
BERKELEY_POSELETS	BERKELEY POSELETS	Multiclass poselets	UC Berkeley / Adobe	Lubomir Bourdev, Subhransu Maji, Thomas Brox, Jitendra Malik	Poselets based on Bourdev et al ECCV 2010, extended for multiple categories.
BERKELEY_POSELETS_ACTION	BERKELEY_POSELETS_ACTION	Poselets trained on action categories	University of California, Berkeley	Subhransu Maji, Lubomir Bourdev, Jitendra Malik	Discriminatively selected poselets for action classification + context from object detections and other actions in the image.
BERKELEY_POSELETS_ALIGN_PB	Berkeley-poselets-align-pb	Segmentation on poselet detections	UC Berkeley	Thomas Brox, Lubomir Bourdev, Subhransu Maji, Jitendra Malik	Non-rigid alignment of poselet activations to UCM edges, filling of area between object edges by variational smoothing, competition among objects, and competitive refinement of masks
BIT_LINSVM_PHOW	LinearSVM-PHOW	LinearSVM-PHOW	Beijing Institute of Technology	Chunliang Lv, Lu Tian, Yuan Zhou, Xiumin Shi	Linear SVM classifier using spatial pyramid matching kernel.
BONN_ACTION	Bonn_action	action	Univeristy of Bonn	Jo�o Carreira, Adrian Ion, Fuxin Li, Cristian Sminchisescu	Segmentation-based recognition method, where multiple figure-ground image partitions constrained by the bounding box are extracted using the Constrained Parametric Min Cuts algorithm (CPMC), and classified using a regression-based framework.
BONN_FGT_SEGM	Bonn_FGT_Segm	FG Detection, FG Tiling	Univeristy of Bonn	Jo�o Carreira, Adrian Ion, Fuxin Li, Cristian Sminchisescu	For each image, multiple full image segmentations (tilings) are generated using a maximum clique formulation on a graph that connects all non-overlapping figure-ground segments obtained using the Constrained Parametric Min Cuts Algorithm (CPMC, CVPR10, http://sminchisescu.ins.uni-bonn.de/papers/cs-cvpr10.pdf). A unary + pairwise scoring is then learned using relevance optimization, by alternating between estimating labels for tiles and learning their scoring parameters against the VOC criteria.
BONN_SVR_SEGM	Svr-Segm	Svr-Segm	University of Bonn	Joao Carreira, Fuxin Li, Adrian Ion, Cristian Sminchisescu	Support vector regression on mulltiple descriptors extracted from figure-ground segmentations obtained using the Constrained Parametric Min Cuts Algorithm (CPMC, CVPR10, http://sminchisescu.ins.uni-bonn.de/papers/cs-cvpr10.pdf). Descriptors include SIFT, color SIFT and HOG on foreground and background. Sequential segment aggregation strategy to handle multiple objects and rank multiple figure ground hypotheses (CVPR10, http://sminchisescu.ins.uni-bonn.de/papers/cls-cvpr10.pdf). The winning method of the 2009 segmentation challenge.
BROOKES_AHCRF	AHCRF	Associative hierarchical CRF	Oxford Brookes University	Lubor Ladicky Christopher Russell Philip Torr	Associative hierarchical CRF with detectors and cooccurence. One pixel layer with dense feature boost potential, 6 segmentation with potentials based on histograms of features, detector potential based on part-based model sliding window detector, generatively trained cooccurence potential
BUPT_LPBETA_MULTFEAT	LPbeta-Multi features	LPbeta with multi features	Beijing University of Posts and Telecommunications	Cheng Lin, Qi Xianbiao, Li Chunguang, Guo Jun, Zhang Honggang, Chen Guang	LPbeta with multi features including SIFT-gray, SIFT-color and SSIM.Trained on full train+val set with default parameters.
BUPT_SPM_SC_HOG	SPM-SC-HOG	Linear SVM Classifier with dense HOG features	Beijing University of Posts and Telecommunications	Qi Xianbiao, Cheng Lin, Li Chunguang, Guo Jun, Zhang Honggang, Chen Guang	Liblinear classifier with dense HOG features. Trained on full train+val set with default parameters.
BUPT_SVM_MULTFEAT	SVM-Multi features	Svm classifier with multi features	Beijing University of Posts and Telecommunications	Cheng Lin, Qi Xianbiao, Li Chunguang, Guo Jun, Zhang Honggang, Chen Guang	Libsvm classifier with multi features. Re-trained the final result with default cross-validation and tuned parameters.
BUT_FU_SVM_SIFT	FU-SVM-SIFT	SVM kernel fusion with several SIFT	Brno University of Technology	Michal Hradi�, Ivo ?ezn�?ek, David Ba?ina, AdamVl?ek	Features: grayscale SIFT and color SIFT Sampling: dense, Harris-Laplace Codebook: k-means 4k BOW trans.: codeword uncertainty - whole image, three horizontal stripes, 2x2 grid Kernel: exp() of weigted X2 distances (fusion of 30 distances) SVM: libsvm
CMIC_SYNTHTRAIN	CMIC_SynthTrain	Synthetic Training of Deformable Part Models	Cairo Microsoft Innovation Lab, Microsoft Research	Osama Khalil, Yasmine Badr, Motaz El-Saban	This submission applies synthetic training for Deformable Part Models. Using the segmentation mask of the objects, we synthesized new training examples, by relocating the objects to different background. The idea was applied on top of the deformable models approach [1]. [1] P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan; Object detection with discriminatively trained part based models; PAMI 2009.
CMIC_VARPARTS	CMIC_VarParts	Deformable part models with variable sized parts	Cairo Microsoft Innovation Lab, Microsoft Research	Osama Khalil, Yasmine Badr, Motaz El-Saban	Our submission is based on the Deformable Part Models approach[1]. We allowed the model parts to have variable sizes accommodating for affine distortion that renders part sizes non-proportional. [1] P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan; Object detection with discriminatively trained part based models; PAMI 2009.
CMU_RANDPARTS	RandomParts	Unsupervised Parts-based Attributes	Carnegie Mellon University	Santosh Divvala (CMU) Larry Zitnick (MSR) Ashish Kapoor (MSR) Simon Baker (MSR)	http://www.cs.cmu.edu/~santosh/finalReport.pdf (unpublished work)
CMU_RANDPARTS_MAXSCORE	RandomParts_maxScore	Unsupervised Parts-based Attributes (max score)	Carnegie Mellon University	Santosh Divvala (CMU) Larry Zitnick (MSR) Ashish Kapoor (MSR) Simon Baker (MSR)	Updated version of earlier submission (http://www.cs.cmu.edu/~santosh/finalReport.pdf). Main update: inclusion of max score feature and one round of iterative training
CVC_BASE	CVC-BASE	SVM classifier with multiple features	Computer Vision Center, Universitat Autonoma de Barcelona, Spain	Nataliya Shapovalova, Wenjuan Gong, Fahad Shahbaz Khan, Josep M. Gonfaus, Marco Pedersoli, Andrew D. Bagdanov, Joost van de Weijer, Jordi Gonz�lez	Baseline CVC submission for action recognition. Standard BoW model over multiple features including PHOG, grayscale SIFT and (various) color SIFT descriptors. Foreground/background modeled separately, spatial pyramid over several features for foreground representation. Late fusion of feature-specific SVM outputs for final action score.
CVC_FLAT	CVC_Flat	Bag-of-words with Non-linear SVM	Computer Vision Center Barcelona	Fahad Shahbaz Khan Joost van de Weijer Andrew D. Bagdanov Noha Elfiky David Rojas Pep Gonfaus Jordi Gonzalez Maria Vanrell	We followed the standard bag-of-words pipeline with multiple detectors alongwith SIFT, ColorNames and HUE descriptors. To combine Color and SHape, we use our own Color Attention Algorithm. GIST descriptor is used to obtain the holistic representation of an image. Finally, we use a standard Non linear SVM for learning.
CVC_HARMONY	CVC_Harmony	Harmony Potentials	Computer Vision Center - Universitat Aut�noma de Barcelona	Josep Maria Gonfaus, Xavier Boix, Fahad Kahn, Joost van de Weijer, Andrew Bagdanov, Marco Pedersoli, Jordi Gonz�lez, Joan Serrat	Our submission is based on [1]. We use the CVC_flat classification submission as the observations for the global node. New Absolute Position Prior is added to the Superpixels Probabilities [1] Josep M. Gonfaus, Xavier Boix, Joost Van de Weijer, Andrew D. Bagdanov, Joan Serrat, and Jordi Gonz�lez, " Harmony Potentials for Joint Classification and Segmentation ", in Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, 2010.
CVC_HARMONY_DET	CVC_Harmony+Det	CVC_Harmony plus Detection Priors	Computer Vision Center - Universitat Aut�noma de Barcelona	Josep Maria Gonfaus, Xavier Boix, Fahad Kahn, Joost van de Weijer, Andrew Bagdanov, Marco Pedersoli, Joan Serrat, Xavier Roca, Jordi Gonz�lez	Our submission is based on [1] and the previous submission "CVC_Harmony". Here we add the detection scores of [2] as location prior in the image. [1] J.M. Gonfaus, X. Boix, J. Van de Weijer, A. D. Bagdanov, J. Serrat, and J. Gonz�lez, "Harmony Potentials for Joint Classification and Segmentation", in CVPR 2010. [2] Felzenszwalb, Girshick, McAllester, Ramanan, "Object Detection with Discriminatively Trained Part Based Models", PAMI 2010
CVC_PLUS	CVC_Plus	Bag-of-words with Non-linear SVM	Computer Vision Center Barcelona	Fahad Shahbaz Khan Joost van de Weijer Andrew D. Bagdanov Noha Elfiky David Rojas Pep Gonfaus Jordi Gonzalez Maria Vanrell	All of CVC_Flat with additional color features combined through averaging the kernel combinations.
CVC_PLUSDET	CVC_Plus_Det	CVC_Plus submission combined with Detection result	Computer Vision Center Barcelona	Fahad Shahbaz Khan Joost van de Weijer Andrew D. Bagdanov Noha Elfiky David Rojas Pep Gonfaus Jordi Gonzalez Maria Vanrell	Same as our CVC_Plus Submission combined with object localization scores.
CVC_SEL	CVC-SEL	SVM classifier with per-class feature selection	Computer Vision Center, Universitat Autonoma de Barcelona, Spain	Nataliya Shapovalova, Wenjuan Gong, Fahad Shahbaz Khan, Josep M. Gonfaus, Marco Pedersoli, Andrew D. Bagdanov, Joost van de Weijer, Jordi Gonz�lez	Enhanced CVC submission built upon CVC-BASE for action recognition. Standard BoW model over multiple features from CVC-BASE plus contextual object descriptors. Cross-validation procedure for action-specific feature and kernel selection. Foreground/background/neighborhood modeled separately, spatial pyramid over several features for foreground representation. Object detection based on deformable part-based detector incorporated. Late fusion of feature-specific SVM outputs for final action score.
CVITVGG_HEADDETSEG	Head-Detect-Segment	Cat-Cut	CVIT-IIIT,Hyderabad, VGG University of Oxford	Omkar M Parkhi, Andrea Vedaldi, C.V.Jawahar, Andrew Zisserman	Detector is trained to detect cat heads. The detections returned are used to initialize seeds for GrabCut which segments the cat. Bounding box is then inferred from these segmentations.
HIT_PROTOLEARN_2	ProtoLearn	Learning the prototype of image categories	Harbin Institute of Technology	Deyuan Zhang Bingquan Liu Chengjie Sun XIaolong Wang	Dense SIFT features, learning the prototype of images using large margin framework. Trains the classifier using 200 positive and 300 negative images selected randomly. The parameters is fixed.
INRIA_SPM_HT	SPM+HT	Spatial Pyramids and Hough Transform	INRIA	Norberto Adri�n Goussies, Arnau Ramisa, Cordelia Schmid	Spatial Pyramids on the bounding box, on the image and a hough transform for taking into account the object-person interactions for bicycle, horse and tvmonitor. Trained on trainval with 5-fold cross-validation.
LIG_MSVM_FUSE_CONCEPT	LIG_msvm_fuse_concept	Fusion of MSVMs with several features, concept opt	Laboratoire d'Informatique de Grenoble	Bahjat Safadi Georges Qu�not	Late fusion of multiple SVMs with multiple features. features include dense and Harris-Laplace filtered opponent SIFT, color histograms and Gabor transforms. Fusion is optimized by concept.
LIP6UPMC_KSVM_BASELINE	LIP6_KSVM_Baseline	Baseline with BOF, SPM and gaussian SVM	LIP6 UPMC	David Picard, Nicolas Thome, Matthieu Cord	Baseline with Bag Of Feature scheme, Spatial Pyramid and gaussian SVM
LIP6UPMC_MKL_L1	LIP6_MKL_L1	l1-MKL with sift, texture and color features	LIP6 UPMC	David Picard, Nicolas Thome, Matthieu Cord	l1-MKL with sift, texture (gabor) and color features.
LIP6UPMC_RANKING	LIP6_ranking	BOF scheme with ranking classifier	LIP6 UPMC	David Picard, Nicolas Thome, Matthieu Cord	Same as baseline, but with ranking classifier
LIRIS_MKL_TRAINVAL	LIRIS_Multi-Feature_MKL_trainval	MKL classifier with multiple features	LIRIS, Ecole Centrale Lyon, CNRS, UMR5205, France	Chao ZHU, Huanzhang FU, Charles-Edmond BICHOT, Emmanuel Dellandrea, Liming CHEN	Multiple Kernel Learning (MKL) classifier with multiple features: colorSIFT (dense+harris-laplace), colorLBP (see our paper in ICPR2010), PHOG, Self-Similarity, and Color Histogram. A vocabulary of 4000 codewords is created for SIFT. Spatial pyramid information is used. Trained on 'train + val' set.
LJKINPG_HOG_LBP_LTP_PLS2ROOTS	HOG+LBP+LTP+PLS2ROOTS	HOG+LBP+LTP+PLS2ROOTS	LJK,INPG	Sibt ul Hussain, Bill Triggs	This method consists of two roots based object detector. This detector is also trained using HOG+LBP+LTP as feature sets while PLS based linear SVM is used as learning algorithm.
MITUCLA_HIERARCHY	MITUCLA_Hierarchy	Latent hierarchical structural learning	MIT and UCLA	Long Zhu, Yuanhao Chen, William Freeman, Alan Yuille, Antonio Torralba	Latent hierarchical structural learning with dense HOG and HOW(SIFT) features.
NEC_V1_HOGLBP_NONLIN_SVM	hog, lbp, nonlinear coding, svm	v1_classdependent_nodection	NEC Labs, America	NEC: Yuanqing Lin, Fengjun Lv, Shenghuo Zhu, Ming Yang, Timothee Cour, Kai Yu UIUC: LiangLiang Cao, Thomas Huang Rutgers Univ.: Tong Zhang Univ. Missouri: Xiaoyu Wang, Tony Xu Han	Dense DHOG and LBP features are coded by both local linear and nonlinear methods. The resulting high-dimension features were then fed to linear SVMs. Class dependent cross-validation.
NEC_V1_HOGLBP_NONLIN_SVMDET	hog, lbp, nonlinear coding, svm, detection	v1_classdependent_withdetection	NEC Labs, America	NEC: Yuanqing Lin, Fengjun Lv, Shenghuo Zhu, Ming Yang, Timothee Cour, Kai Yu UIUC: LiangLiang Cao, Thomas Huang Rutgers Univ.: Tong Zhang Univ. Missouri: Xiaoyu Wang, Tony Xu Han	Dense DHOG and LBP features are coded by both local linear and nonlinear methods. The resulting high-dimension features were then fed to linear SVMs. Detection results were taken into account. Class dependent cross-validation.
NII_SVMSIFT	SVM-SIFT	SVM classifier with color sift features	NII Japan	Xiao Zhou, Cai-Zhi Zhu	libsvm classifier with color sift features. Trained using 5-fold cross-validation. Re-trained on val set with fixed parameters.
NLPR_HOGLBP_MC_LCEGCHLC	Boosted HOG-LBP and multi-context (LC, EGC, HLC)	NLPR_VSTAR_DET_4	National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences	Yinan Yu, Junge Zhang, Yongzhen Huang, Shuai Zheng, Weiqiang Ren, Chong Wang, Kaiqi Huang, Tieniu Tan	Deformable model with Boosted HOG-LBP and multi-context information, use location context, enhanced global context, HOG and LBP inter-class context.
NLPR_VSTAR_CLS_DICTLEARN	NLPR_VSTAR_CLS_DICTLEARN	Saliency coding and dictionary learning	National Laboratory of Pattern Recognition , Institute of Automation, Chinese Academy of Sciences	Yongzhen Huang, Shuai Zheng, Weiqiang Ren, Chong Wang, Yinan Yu, Junge Zhang, Kaiqi Huang, Tieniu Tan	Lib-SVM classifier with dense SIFT features, saliency coding, dictionary leanring and detection information.
NTHU_LINSPARSE_2	LINEAR-SPARSE	Linear SVM with spatial max pooling features.	NTHU	Tao Yen Tang, Jyun Yi Lin, Cheng Hao Kung, Meng Hua Wu, Chun Han Chien, Jia Yu Kuo, Hwann Tzong Chen	LIBLINEAR with spatial max pooling of sparse features. Sparse featues are determined by ScSPM and color descriptor. Sparse coding dictionary is learned by SPAMS.
NUDT_SVM_LDP_SIFT_PMK_SPMK	SVM_LDP_SIFT_PMK_SPMK	SVM classifier on PMK and SPMK approaches with lin	National University of Defense Technology	Hongping Cai, Krystian Mikolajczk, Dewen Hu	Local features are extracted on regular grids at multiple scales, then described by SIFT and 'SIFT+hue histogram'. To reduce the memory and computational cost, the linear discriminant projection (LDP) are applied, leading to significant feature dimensionality reduction and performance boosting. With the 30-dim LDP-projected features, the submitted results are obtained by fusing pyramid match kernel (PMK) and spatial pyramid match kernel (SPMK), with an SVM classifier for the final stage.
NUDT_SVM_WHGO_SIFT_CENTRIST_LLM	SVM_WHGO_SIFT_CENTRIST_low-level modeling	SVM classifier on low-level modeling based approac	Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology	Li Zhou, Zongtan Zhou, Dewen Hu	This method is based on a low-level modeling strategy. The approach works by creating multiple resolution images and partitioning them into sub-regions at different scales. We represent each sub-region with WHGO, CENTRIST and SIFT descriptors and combine the features of different descriptor and resolution channels through an SVM classifier to form the final decision function.
NUSPSL_EXCLASSIFIER	Exclusive-Classifier	classifier based on exclusive dense graph	National University of Singapore; Panasonic Singapore Laboratories;	NUS: Xiangyu Chen, Qiang Chen, Xiaotong Yuan, Zheng Song, Si Liu, Tat-Seng Chua, Shuicheng Yan; PSL: Yang Hua, Zhongyang Huang, Shengmei Shen	Exclusive calssifier with both visual features and exclusive contextual information. Trained on full train+val set using both visual and context information.
NUSPSL_KERNELREGFUSING	KernelRegFusing	kernel regression for all methods	National University of Singapore; Panasonic Singapore Laboratories;	NUS: Qiang Chen, Zheng Song, Si Liu, Xiangyu Chen, Tat-Seng Chua, Shuicheng Yan; PSL: Yang Hua, Zhongyang Huang, Shengmei Shen	kernel regression as a combination method to fuse all other submissions.
NUSPSL_MFDETSVM	MFDETSVM	SVM with multifeature and detection kernel	National University of Singapore; Panasonic Singapore Laboratories;	NUS: Qiang Chen, Zheng Song, Si Liu, Shuicheng Yan; PSL: Yang Hua, Zhongyang Huang, Shengmei Shen	SVM classifier with multiple feature and detection kernel.
NUS_HOGLBP_CTX_CLS_RESCORE_V2	HOGLBP_context_classification_rescore_v2	results refined by context and classification	National University of Singapore	Zheng Song, Qiang Chen, Shuicheng Yan	Use HOG+LBP trained part-based detector. The detection results are further reranked via the context information of other detect windows and image classificaton scores.
OXFORD_SBD	Oxford_SBD	Skin based layout detection	University of Oxford	Arpit Mittal, Andrew Zisserman, Philip H. S. Torr, Manuel J. Marin	Head localization is performed using a part-based upper body detector. A local color model of skin pixels is learned using face pixels obtained from the head bounding box. Using this color model, we perform skin detection on the image. Hand positions are then hypothesized from the skin regions so obtained. These hypotheses are verified using the RBF kernel SVM classifier and our own articulated model of the human upper body. It is to be noted that we do not localize feet in the image.
RITSU_CBVR_WKF	Ritsu_CBVR_WKF	SVM Classifier with dense and Harris BOF	Ritsumeikan University	Xian-Hua Han, Yen-Wei Chen, Xiang Ruan	We extracted Gray and color (Opponent and C_sift) BOF feature with dense and Harris sampling, and use SVM with normalized kernel fusion for classification
STANFORD_REGLABEL	REGION-LABEL	Optimizing regions and their labels	Stanford University	M. Pawan Kumar, Stephen Gould, Haithem Turki, Dan Preston, Daphne Koller	The method groups pixels into regions and assigns the regions a unique semantic label simultaneously by minimizing a global energy function. Features used include those obtained from an object detector (deformable parts-based model) as well as a bag of SIFT words computed over the region. Inference as described in CVPR 2010, learning as described in ICCV 2009.
SURREY_MK_KDA	Multikernel+KDA	Mulitkernel fusion with KDA	The University of Surrey	Piotr Koniusz, Muhammad Atif Tahir, Mark Barnard, Fei Yan, Krystian Mikolajczyk	Kernel-level fusion with Spatial Pyramid Grids, Soft Assignment and Kernel Discriminant Analysis using spectral regression. 18 kernels have been generated from 18 variants of SIFT.
TIT_SIFT_GMM_MKL	SIFT-GMM-MKL	Multiple kernel learning with SIFT GMMs	Tokyo Institute of Technology	Nakamasa Inoue, Yusuke Kamishima, Koichi Shinoda	We use multiple kernel learning and GMM supervector kernels with SIFT features.
TIT_SIFT_GMM_MKL2	SIFT-GMM-MKL2	Multiple kernel learning with SIFT GMMs	Tokyo Institute of Technology	Nakamasa Inoue, Yusuke Kamishima, Koichi Shinoda	Same as the SIFT-GMM-MKL run but the GrabCut is applied for detection.
UC3M_GENDISC	UC3M_Generative_Discriminative	Combination of Generative Discriminative Methods	Universidad Carlos III de Madrid	Iv�n Gonz�lez-D�az, Fernando D�az de Mar�a	Combination of Supervised Topic Models with SVM-based discriminative methods for concurrent image recognition and segmentation
UCI_DPM_SP	DPM-SP	parts based model and spatial pyramid features	University of California, Irvine	Ragib Morshed, Yi Yang, Charless Fowlkes	Parts model results rescored by combining with spatial pyramid based scene (global) classification results. Scene trained using svm with hist intersect kernel, rescoring trained using SVM on train+val+some_from_segmentations with parameters obtained via search.
UCI_LSVM_MDPM_10X	UCI_LSVM-MDPM-10X	10x train set for LSVM, mixtures, deformable parts	University of California, Irvine	Xiangxin Zhu, Carl Vondrick, Deva Ramanan, Charless Fowlkes	We downloaded additional images from Flickr that match the distribution of the testing set. We used Amazon's Mechanical Turk to annotate these training sets that are 10 times larger the standard trainval set. We used our larger training set to train models with the detector from Felzenswalb et. all.
UCLEAR_SVM_DOSP_MULTFEATS	SVM-DOSP-MULTFEATS	SVM & dense saptial pyramid w/ multiple features	University of Caen GREYC and INRIA LEAR	Gaurav Sharma, Frederic Jurie, Cordelia Schmid	Multiple chi squared kernels are computed: spatial pyramid (SP) w/ dense SIFT, dense overlapping SP w/ HOG, texture filter, LAB values (bag-of-words w/ the above features) and edge dir hists. They are computed on full images, person bounding boxes (BB) and BB of the lower part (simple stretch-scale of person BB) expected to contain horse, bike etc. They are combined with class specific binary weights based on their perf on val set. Finally, class specific SVMs trained on train+val.
UMCO_DHOG_KSVM	dhog-ksvm	kernel svm classifier with dhog feature	University of Missouri - Columbia	Xutao Lv, Xiaoyu Wang, Xi Zhou, Tony X. Han	train SVM model with different kernels on dhog feature.
UMNECUIUC_HOGLBP_DHOGBOW_SVM	HOG-LBP + DHOG bag of words, SVM	Linear svm classifier with bag of words method	The University of Missouri, NEC Labs America, The University of Illinois at Urbana-Champaign	Xiaoyu Wang, Xi Zhou, Tony X. Han, Shuai Tang, Guang Chen, Kai Yu, Thomas S. Huang	Liblinear SVM with HOG-LBP feature and DHOG bag of words approach
UMNECUIUC_HOGLBP_LINSVM	HOG-LBP Linear SVM	svm classifier with HOG LBP features	The University of Missouri, NEC Labs America, The University of Illinois at Urbana-Champaign	Xiaoyu Wang, Xi Zhou, Tony X. Han, Shuai Tang, Guang Chen, Kai Yu, Thomas S. Huang	Liblinear SVM with HOG-LBP features. All classes use the same default training parameters.
UOCTTI_LSVM_MDPM	LSVM-MDPM	LSVM Mixtures of deformable part models	University of Chicago and TTI-C	Pedro Felzenszwalb (UChicago), Ross Girshick (UChicago), David McAllester (TTI-C)	Deformable part models with HOG features. Based on [1,2]. Each model has 6 components with 8 parts. We associate a binary mask with each component to generate segmentations. The object detection models were trained from bounding boxes using LSVM. The segmentation masks were trained from segmentations. [1] P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan. Object Detection with Discriminatively Trained Part Based Models. PAMI 32(9), Sept 2010. [2] http://people.cs.uchicago.edu/~pff/latent-release4/
UVA_BW_NEWCOLOURSIFT	BW+New Colour SIFT	Best Window + New Colour SIFT	UvA	Jasper Uijlings Koen van de Sande Theo Gevers Arnold Smeulders	Best Window approach with new Colour SIFT trained with Multiple Kernel Learning SVM.
UVA_BW_NEWCOLOURSIFT_SRKDA	BW+New Colour SIFT-SRKDA	BW+New Colour SIFT-SRKDA	University of Amsterdam	Jasper Uijlings Koen van de Sande Theo Gevers Arnold Smeulders Remko Scha	Best Window Approach plus new color sift. Classification by SRKDA
UVA_DETMONKEY	Detection Monkey	Detection Monkey	University of Amsterdam	Koen van de Sande Jasper Uijlings Theo Gevers Arnold Smeulders	The detection monkey is trained with SVM, dense Color SIFT, spatial pyramid and multiple iterations.
UVA_GROUPLOC	GroupLoc	Localisation with grouping window selection	University of Amsterdam	Jasper Uijlings Koen van de Sande Theo Gevers Arnold Smeulders Remko Scha	Candidate windows are selected using hierarchical grouping. Classification is with SIFT, SVM-Histogram Intersection, Spatial Pyramid
WILLOW_A_SVMSIFT_1-A_LSVM	a * SVM-SIFT + (1-a) * LSVM	Combination of SVM and DPM with learned weights.	France, INRIA - Willow Project	Vincent Delaitre, Ivan Laptev, Josef Sivic	Combination of a SVM classifier(with dense SIFT features, trained using 5-fold cross-validation and re-trained on full train+val set with fixed parameters) and of the Felzenszwalb's deformable part-based model (trained on full train+val set with default parameters).The classification score is obtained by a linear combination of the scores of the two classifiers:the two classifiers are also trained on the train set and the weights of this combination are determined by optimizing over the val set.
WILLOW_LSVM	LSVM	Felzenszwalb's part-based model.	France, INRIA - Willow Project	Vincent Delaitre, Ivan Laptev, Josef Sivic	Felzenszwalb's part-based model trained on full train+val set with default parameters.
WILLOW_SVMSIFT	SVM-SIFT	SVM classifier with dense SIFT features.	France, INRIA - Willow Project	Vincent Delaitre, Ivan Laptev, Josef Sivic	SVM-Light classifier with dense SIFT features. Trained using 5-fold cross-validation. Re-trained on full train+val set with fixed parameters
WLU_SPM_EMDIST	WLU-SPM-EMDIST	SPM/lin.SVM, codebook using Earth Mover dist.	Washington and Lee University	Chen Zhong, William Richardson, Joshua Stough	Spatial Pyramid Match after Lazebnik. Linear SVM on LLC-coded dense SIFT features after Yang/Wang/Yu. Annotation-based codebook training. SPM levels trained separately and combined. Codebook generated using Earth Mover's distance.
XRCE_IFV	Improved Fisher Vector	Linear SVM on Improved Fisher vector	XRCE	Florent Perronnin Jorge Sanchez Thomas Mensink	Based on [PSM10]: F. Perronnin, J. Sanchez and T. Mensink, "Improving the Fisher kernel for Large-Scale Image Classification", ECCV, 2010.
XRCE_IFV_1M	Improved Fisher Vector	Linear SVM on Improved Fisher vector	XRCE	Florent Perronnin Jorge Sanchez Thomas Mensink	Based on [PSM10]: F. Perronnin, J. Sanchez and T. Mensink, "Improving the Fisher kernel for Large-Scale Image Classification", ECCV, 2010. Trained on close to 1M mono-tagged Flickr group images (non-overlapping with test set).
XRCE_IFV_FUSE_OPT	Improved Fisher Vector	Linear SVM on Improved Fisher vector	XRCE	Florent Perronnin, Jorge Sanchez, Thomas Mensink	Based on [PSM10]: F. Perronnin, J. Sanchez and T. Mensink, "Improving the Fisher kernel for Large-Scale Image Classification", ECCV, 2010. Late fusion of two systems trained respectively on i)voc10 trainval and ii) close to 1M mono-tagged Flickr group images. Optimal weights are learned per-class through cross-validation.