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Referring Video Object Segmentation On Mevis

Métriques

F

J

Ju0026F

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	F	J	Ju0026F	Paper Title	Repository
DsHmp + MTCM	51.1	44.1	47.6	Multi-Context Temporal Consistent Modeling for Referring Video Object Segmentation
HTR	45.5	39.9	42.7	Temporally Consistent Referring Video Object Segmentation with Hybrid Memory
VRS-HQ (Chat-UniVi-13B)	53.7	48	50.9	The Devil is in Temporal Token: High Quality Video Reasoning Segmentation
InternVideo2.5	-	-	32	InternVideo2.5: Empowering Video MLLMs with Long and Rich Context Modeling	-
MPG-SAM 2	56.7	50.7	53.7	MPG-SAM 2: Adapting SAM 2 with Mask Priors and Global Context for Referring Video Object Segmentation	-
ReferFormer	32.2	29.8	31.0	Language as Queries for Referring Video Object Segmentation
DsHmp	49.8	43	46.4	Decoupling Static and Hierarchical Motion Perception for Referring Video Segmentation
SAMWISE	51.2	45.4	48.3	SAMWISE: Infusing wisdom in SAM2 for Text-Driven Video Segmentation
LBDT	30.8	27.8	29.3	Language-Bridged Spatial-Temporal Interaction for Referring Video Object Segmentation	-
VLT+TC	37.3	33.6	35.5	VLT: Vision-Language Transformer and Query Generation for Referring Segmentation
LMPM	40.2	34.2	37.2	MeViS: A Large-scale Benchmark for Video Segmentation with Motion Expressions
MTTR	31.2	28.8	30.0	End-to-End Referring Video Object Segmentation with Multimodal Transformers
URVOS	29.9	25.7	27.8	URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark
ReferDINO (Swin-B)	53.9	44.7	49.3	ReferDINO: Referring Video Object Segmentation with Visual Grounding Foundations	-
FindTrack	50.7	45.6	48.2	Find First, Track Next: Decoupling Identification and Propagation in Referring Video Object Segmentation

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