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 76–100 of 114 papers

TitleStatusHype
Generation-Guided Multi-Level Unified Network for Video Grounding—0
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
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
Iterative Proposal Refinement for Weakly-Supervised Video Grounding—0
A Simple Transformer-Based Model for Ego4D Natural Language Queries ChallengeCode0
Language-free Training for Zero-shot Video Grounding—0
Graph2Vid: Flow graph to Video Grounding for Weakly-supervised Multi-Step Localization—0
On the Effects of Video Grounding on Language Models—0
Towards Parameter-Efficient Integration of Pre-Trained Language Models In Temporal Video GroundingCode0
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
Position-aware Location Regression Network for Temporal Video Grounding—0
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
Unsupervised Temporal Video Grounding with Deep Semantic Clustering—0
Multi-Level Representation Learning With Semantic Alignment for Referring Video Object Segmentation—0
Semi-Supervised Video Paragraph Grounding With Contrastive Encoder—0
LocFormer: Enabling Transformers to Perform Temporal Moment Localization on Long Untrimmed Videos With a Feature Sampling Approach—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