Referring Expression Segmentation On J Hmdb

Métriques

IoU mean

IoU overall

Precision@0.5

Precision@0.6

Precision@0.7

Precision@0.8

Precision@0.9

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	AP	IoU mean	IoU overall	Precision@0.5	Precision@0.6	Precision@0.7	Precision@0.8	Precision@0.9	Paper Title	Repository
MTTR (w=10)	0.392	0.698	0.701	0.939	0.852	0.616	0.166	0.001	End-to-End Referring Video Object Segmentation with Multimodal Transformers
Hui et al.	0.335	0.604	0.598	0.783	0.639	0.378	0.076	0.000	Collaborative Spatial-Temporal Modeling for Language-Queried Video Actor Segmentation	-
ClawCraneNet	-	0.655	0.644	0.880	0.796	0.566	0.147	0.002	ClawCraneNet: Leveraging Object-level Relation for Text-based Video Segmentation	-
ACGA	0.289	0.584	0.576	0.756	0.564	0.287	0.034	0.000	Asymmetric Cross-Guided Attention Network for Actor and Action Video Segmentation From Natural Language Query
Gavrilyuk et al.	0.233	0.542	0.541	0.699	0.460	0.173	0.014	0.000	Actor and Action Video Segmentation from a Sentence
CMPC-V	0.342	0.617	0.616	0.813	0.657	0.371	0.07	0.000	Cross-Modal Progressive Comprehension for Referring Segmentation
AAMN	0.321	0.576	0.583	0.773	0.627	0.360	0.044	0.000	Actor and Action Modular Network for Text-based Video Segmentation	-
SgMg (Video-Swin-B)	0.450	0.725	0.737	0.972	0.917	0.714	0.225	0.003	Spectrum-guided Multi-granularity Referring Video Object Segmentation
VLIDE	0.441	0.666	0.68	0.874	0.791	0.586	0.182	0.30	Deeply Interleaved Two-Stream Encoder for Referring Video Segmentation	-
VT-Capsule	0.261	0.550	0.535	0.677	0.513	0.283	0.051	0.000	Visual-Textual Capsule Routing for Text-Based Video Segmentation	-
MTTR (w=8)	0.366	0.679	0.674	0.91	0.815	0.57	0.144	0.001	End-to-End Referring Video Object Segmentation with Multimodal Transformers
Hu et al.	0.178	0.528	0.546	0.633	0.350	0.085	0.002	0.000	Segmentation from Natural Language Expressions
CMDy	0.301	0.576	0.554	0.742	0.587	0.316	0.047	0.000	Context Modulated Dynamic Networks for Actor and Action Video Segmentation with Language Queries	-
RefVOS	-	0.568	0.606	0.731	0.62	0.392	0.088	0.0	Hierarchical interaction network for video object segmentation from referring expressions	-
PRPE	0.294	-	-	0.572	0.690	0.319	0.06	0.001	Polar Relative Positional Encoding for Video-Language Segmentation	-
SOC (Video-Swin-B)	0.446	0.723	0.736	0.969	0.914	0.711	0.213	0.001	SOC: Semantic-Assisted Object Cluster for Referring Video Object Segmentation
SOC (Video-Swin-T)	0.397	0.701	0.707	0.947	0.864	0.627	0.179	0.001	SOC: Semantic-Assisted Object Cluster for Referring Video Object Segmentation
HINet	-	0.627	0.652	0.819	0.736	0.542	0.168	0.4	Hierarchical interaction network for video object segmentation from referring expressions	-
Li et al.	0.173	0.491	0.529	0.578	0.335	0.103	0.060	0.000	Tracking by Natural Language Specification	-
Gavrilyuk et al. (Optical flow)	0.267	0.570	0.555	0.712	0.518	0.264	0.030	0.000	Actor and Action Video Segmentation from a Sentence

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