SOTAVerified

Video Grounding

Video grounding is the task of linking spoken language descriptions to specific video segments. In video grounding, the model is given a video and a natural language description, such as a sentence or a caption, and its goal is to identify the specific segment of the video that corresponds to the description. This can involve tasks such as localizing the objects or actions mentioned in the description within the video, or associating a specific time interval with the description.

Papers

Showing 61–70 of 114 papers

TitleStatusHype
Described Spatial-Temporal Video Detection—0
DiffusionVMR: Diffusion Model for Joint Video Moment Retrieval and Highlight Detection—0
End-to-End Dense Video Grounding via Parallel Regression—0
End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding—0
Enhancing Weakly Supervised Video Grounding via Diverse Inference Strategies for Boundary and Prediction Selection—0
EtC: Temporal Boundary Expand then Clarify for Weakly Supervised Video Grounding with Multimodal Large Language Model—0
EVOQUER: Enhancing Temporal Grounding with Video-Pivoted BackQuery Generation—0
Exploiting Feature Diversity for Make-up Temporal Video Grounding—0
G2L: Semantically Aligned and Uniform Video Grounding via Geodesic and Game Theory—0
Gaussian Kernel-based Cross Modal Network for Spatio-Temporal Video Grounding—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1InternVideo2-6BR@1,IoU=0.756.45—Unverified
2InternVideo2-1BR@1,IoU=0.754.45—Unverified
3LLMEPETR@1,IoU=0.749.94—Unverified
4QD-DETRR@1,IoU=0.744.98—Unverified
5DiffusionVMRR@1,IoU=0.744.49—Unverified
6UMTR@1,IoU=0.741.18—Unverified
7Moment-DETRR@1,IoU=0.733.02—Unverified
#ModelMetricClaimedVerifiedStatus
1DeCafNetR@1,IoU=0.113.25—Unverified
2DenoiseLocR@1,IoU=0.111.59—Unverified