SOTAVerified

Temporal Sentence Grounding

Temporal sentence grounding (TSG) aims to locate a specific moment from an untrimmed video with a given natural language query. For this task, different levels of supervision are used. 1) Weak supervision: video-level action category set; 2) Semi-weak supervision: video-level action category set, and action annotations at several timestamps; 3) Full supervision: Action category and action interval annotations of all actions in untrimmed videos.

Papers

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Showing 1–43 of 43 papers

TitleStatusHype
Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language ModelsCode2
Uncovering Hidden Challenges in Query-Based Video Moment RetrievalCode1
D3G: Exploring Gaussian Prior for Temporal Sentence Grounding with Glance AnnotationCode1
Weakly Supervised Temporal Sentence Grounding With Gaussian-Based Contrastive Proposal LearningCode1
DeCafNet: Delegate and Conquer for Efficient Temporal Grounding in Long VideosCode1
Negative Sample Matters: A Renaissance of Metric Learning for Temporal GroundingCode1
BAM-DETR: Boundary-Aligned Moment Detection Transformer for Temporal Sentence Grounding in VideosCode1
Gaussian Mixture Proposals with Pull-Push Learning Scheme to Capture Diverse Events for Weakly Supervised Temporal Video GroundingCode1
Span-based Localizing Network for Natural Language Video LocalizationCode1
Learning Temporal Sentence Grounding From Narrated EgoVideosCode0
Semantic Conditioned Dynamic Modulation for Temporal Sentence Grounding in VideosCode0
Temporal Sentence Grounding in Streaming VideosCode0
Bias-Conflict Sample Synthesis and Adversarial Removal Debias Strategy for Temporal Sentence Grounding in VideoCode0
A Closer Look at Temporal Sentence Grounding in Videos: Dataset and MetricCode0
Transformer with Controlled Attention for Synchronous Motion CaptioningCode0
Diversifying Query: Region-Guided Transformer for Temporal Sentence GroundingCode0
Efficient Temporal Sentence Grounding in Videos with Multi-Teacher Knowledge DistillationCode0
Temporal Sentence Grounding in Videos: A Survey and Future Directions—0
Towards Debiasing Temporal Sentence Grounding in Video—0
Towards Visual-Prompt Temporal Answering Grounding in Medical Instructional Video—0
Tracking Objects and Activities with Attention for Temporal Sentence Grounding—0
Transform-Equivariant Consistency Learning for Temporal Sentence Grounding—0
Video sentence grounding with temporally global textual knowledge—0
Weakly Supervised Temporal Sentence Grounding With Uncertainty-Guided Self-Training—0
Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining—0
A Closer Look at Debiased Temporal Sentence Grounding in Videos: Dataset, Metric, and Approach—0
You Can Ground Earlier than See: An Effective and Efficient Pipeline for Temporal Sentence Grounding in Compressed Videos—0
Towards Weakly Supervised End-to-end Learning for Long-video Action Recognition—0
A Survey on Temporal Sentence Grounding in Videos—0
Constraint and Union for Partially-Supervised Temporal Sentence Grounding—0
Context-aware Biaffine Localizing Network for Temporal Sentence Grounding—0
Contrast-Unity for Partially-Supervised Temporal Sentence Grounding—0
Diversified Augmentation with Domain Adaptation for Debiased Video Temporal Grounding—0
Exploring Motion and Appearance Information for Temporal Sentence Grounding—0
Exploring Optical-Flow-Guided Motion and Detection-Based Appearance for Temporal Sentence Grounding—0
Grounding-Prompter: Prompting LLM with Multimodal Information for Temporal Sentence Grounding in Long Videos—0
Hierarchical Local-Global Transformer for Temporal Sentence Grounding—0
Learning to Focus on the Foreground for Temporal Sentence Grounding—0
Memory-Guided Semantic Learning Network for Temporal Sentence Grounding—0
Multi-Pair Temporal Sentence Grounding via Multi-Thread Knowledge Transfer Network—0
Progressively Guide to Attend: An Iterative Alignment Framework for Temporal Sentence Grounding—0
Reducing the Vision and Language Bias for Temporal Sentence Grounding—0
Rethinking the Video Sampling and Reasoning Strategies for Temporal Sentence Grounding—0
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All datasetsCharades-STAEgo4D-Goalstep

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeCafNetR1@0.747.55—Unverified
2AdaFocus (Full, MViT-Charades-Pretrain-feature, MMN model)R1@0.738.6—Unverified
3AdaFocus (Full, I3D-Charades-Pretrain-feature, MMN model)R1@0.735.6—Unverified
4MMN (Full, MViT-K400-Pretrain-feature, evaluated by AdaFocus)R1@0.732.2—Unverified
5MMN (Full, I3D-K400-Pretrain-feature, evaluated by AdaFocus)R1@0.729.8—Unverified
6AdaFocus (Weak, MViT-Charades-Pretrain-feature, CPL model)R1@0.723.2—Unverified
7AdaFocus (Weak, I3D-Charades-Pretrain-feature, CPL model)R1@0.722.4—Unverified
8CPL (Weak, MViT-K400-Pretrain-feature, evaluated by AdaFocus)R1@0.721.8—Unverified
9AdaFocus (Semi-weak, MViT-Charades-Pretrain-feature, D3G model)R1@0.721.8—Unverified
10AdaFocus (Semi-weak, I3D-Charades-Pretrain-feature, D3G model)R1@0.721.1—Unverified
#ModelMetricClaimedVerifiedStatus
1DeCafNet-100%R@1,IoU=0.323.2—Unverified
2DeCafNet-50%R@1,IoU=0.321.29—Unverified
3VSLNetR@1,IoU=0.311.7—Unverified