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 125 of 114 papers

TitleStatusHype
InternVideo2: Scaling Foundation Models for Multimodal Video UnderstandingCode7
SnAG: Scalable and Accurate Video GroundingCode4
Tarsier2: Advancing Large Vision-Language Models from Detailed Video Description to Comprehensive Video UnderstandingCode4
PG-Video-LLaVA: Pixel Grounding Large Video-Language ModelsCode2
VTimeLLM: Empower LLM to Grasp Video MomentsCode2
Context-Guided Spatio-Temporal Video GroundingCode2
TimeZero: Temporal Video Grounding with Reasoning-Guided LVLMCode2
Query-Dependent Video Representation for Moment Retrieval and Highlight DetectionCode2
LLaVA-ST: A Multimodal Large Language Model for Fine-Grained Spatial-Temporal UnderstandingCode2
Prior Knowledge Integration via LLM Encoding and Pseudo Event Regulation for Video Moment RetrievalCode2
Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data EfficiencyCode2
UMT: Unified Multi-modal Transformers for Joint Video Moment Retrieval and Highlight DetectionCode2
OmniSTVG: Toward Spatio-Temporal Omni-Object Video GroundingCode1
CONE: An Efficient COarse-to-fiNE Alignment Framework for Long Video Temporal GroundingCode1
Grounded Question-Answering in Long Egocentric VideosCode1
Human-centric Spatio-Temporal Video Grounding With Visual TransformersCode1
Object-Shot Enhanced Grounding Network for Egocentric VideoCode1
Explore-And-Match: Bridging Proposal-Based and Proposal-Free With Transformer for Sentence Grounding in VideosCode1
Knowing Where to Focus: Event-aware Transformer for Video GroundingCode1
Dense Regression Network for Video GroundingCode1
Knowing Your Target: Target-Aware Transformer Makes Better Spatio-Temporal Video GroundingCode1
Detecting Moments and Highlights in Videos via Natural Language QueriesCode1
Localizing Moments in Long Video Via Multimodal GuidanceCode1
HawkEye: Training Video-Text LLMs for Grounding Text in VideosCode1
DeCafNet: Delegate and Conquer for Efficient Temporal Grounding in Long VideosCode1
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Benchmark Results

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