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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–75 of 114 papers

TitleStatusHype
AutoTVG: A New Vision-language Pre-training Paradigm for Temporal Video Grounding—0
Cascaded Prediction Network via Segment Tree for Temporal Video Grounding—0
Co-Grounding Networks with Semantic Attention for Referring Expression Comprehension in Videos—0
Collaborative Static and Dynamic Vision-Language Streams for Spatio-Temporal Video Grounding—0
Contextual Self-paced Learning for Weakly Supervised Spatio-Temporal Video Grounding—0
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
Exploiting Auxiliary Caption for Video Grounding—0
Generation-Guided Multi-Level Unified Network for Video Grounding—0
Graph2Vid: Flow graph to Video Grounding for Weakly-supervised Multi-Step Localization—0
Hierarchical Semantic Correspondence Networks for Video Paragraph Grounding—0
Iterative Proposal Refinement for Weakly-Supervised Video Grounding—0
Language-free Training for Zero-shot Video Grounding—0
LLM4VG: Large Language Models Evaluation for Video Grounding—0
LocFormer: Enabling Transformers to Perform Temporal Moment Localization on Long Untrimmed Videos With a Feature Sampling Approach—0
Multi-Level Representation Learning With Semantic Alignment for Referring Video Object Segmentation—0
Multi-Modal Domain Adaptation Across Video Scenes for Temporal Video Grounding—0
Show:102550
← PrevPage 3 of 5Next →
All datasetsQVHighlightsMAD

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