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

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Showing 51–100 of 114 papers

TitleStatusHype
Can I Trust Your Answer? Visually Grounded Video Question AnsweringCode1
DiffusionVMR: Diffusion Model for Joint Video Moment Retrieval and Highlight Detection—0
Knowing Where to Focus: Event-aware Transformer for Video GroundingCode1
ViGT: Proposal-free Video Grounding with Learnable Token in Transformer—0
G2L: Semantically Aligned and Uniform Video Grounding via Geodesic and Game Theory—0
No-frills Temporal Video Grounding: Multi-Scale Neighboring Attention and Zoom-in Boundary Detection—0
Dense Video Object Captioning from Disjoint SupervisionCode0
Boundary-Denoising for Video Activity LocalizationCode0
Query-Dependent Video Representation for Moment Retrieval and Highlight DetectionCode2
Generation-Guided Multi-Level Unified Network for Video Grounding—0
Text-Visual Prompting for Efficient 2D Temporal Video GroundingCode1
Localizing Moments in Long Video Via Multimodal GuidanceCode1
MINOTAUR: Multi-task Video Grounding From Multimodal QueriesCode0
Exploiting Auxiliary Caption for Video Grounding—0
WINNER: Weakly-Supervised hIerarchical decompositioN and aligNment for Spatio-tEmporal Video gRounding—0
Iterative Proposal Refinement for Weakly-Supervised Video Grounding—0
Collaborative Static and Dynamic Vision-Language Streams for Spatio-Temporal Video Grounding—0
Exploring the Effect of Primitives for Compositional Generalization in Vision-and-LanguageCode0
Hierarchical Semantic Correspondence Networks for Video Paragraph Grounding—0
A Simple Transformer-Based Model for Ego4D Natural Language Queries ChallengeCode0
Language-free Training for Zero-shot Video Grounding—0
Weakly-Supervised Temporal Article GroundingCode1
Graph2Vid: Flow graph to Video Grounding for Weakly-supervised Multi-Step Localization—0
On the Effects of Video Grounding on Language Models—0
Embracing Consistency: A One-Stage Approach for Spatio-Temporal Video GroundingCode1
Towards Parameter-Efficient Integration of Pre-Trained Language Models In Temporal Video GroundingCode0
CONE: An Efficient COarse-to-fiNE Alignment Framework for Long Video Temporal GroundingCode1
Video-Guided Curriculum Learning for Spoken Video GroundingCode0
Exploiting Feature Diversity for Make-up Temporal Video Grounding—0
Team PKU-WICT-MIPL PIC Makeup Temporal Video Grounding Challenge 2022 Technical Report—0
STVGFormer: Spatio-Temporal Video Grounding with Static-Dynamic Cross-Modal Understanding—0
Gaussian Kernel-based Cross Modal Network for Spatio-Temporal Video Grounding—0
Animal Kingdom: A Large and Diverse Dataset for Animal Behavior UnderstandingCode1
Position-aware Location Regression Network for Temporal Video Grounding—0
TubeDETR: Spatio-Temporal Video Grounding with TransformersCode1
UMT: Unified Multi-modal Transformers for Joint Video Moment Retrieval and Highlight DetectionCode2
End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding—0
Multi-Scale Self-Contrastive Learning with Hard Negative Mining for Weakly-Supervised Query-based Video Grounding—0
Explore-And-Match: Bridging Proposal-Based and Proposal-Free With Transformer for Sentence Grounding in VideosCode1
Unsupervised Temporal Video Grounding with Deep Semantic Clustering—0
Semi-Supervised Video Paragraph Grounding With Contrastive Encoder—0
Multi-Level Representation Learning With Semantic Alignment for Referring Video Object Segmentation—0
LocFormer: Enabling Transformers to Perform Temporal Moment Localization on Long Untrimmed Videos With a Feature Sampling Approach—0
Detecting Moments and Highlights in Videos via Natural Language QueriesCode1
End-to-End Dense Video Grounding via Parallel Regression—0
On Pursuit of Designing Multi-modal Transformer for Video Grounding—0
Negative Sample Matters: A Renaissance of Metric Learning for Temporal GroundingCode1
EVOQUER: Enhancing Temporal Grounding with Video-Pivoted BackQuery Generation—0
Support-Set Based Cross-Supervision for Video Grounding—0
VidLanKD: Improving Language Understanding via Video-Distilled Knowledge TransferCode1
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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