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
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
Multi-Scale Contrastive Learning for Video Temporal Grounding—0
Multi-Scale Self-Contrastive Learning with Hard Negative Mining for Weakly-Supervised Query-based Video Grounding—0
Multi-sentence Video Grounding for Long Video Generation—0
No-frills Temporal Video Grounding: Multi-Scale Neighboring Attention and Zoom-in Boundary Detection—0
Not All Frames Are Equal: Weakly-Supervised Video Grounding With Contextual Similarity and Visual Clustering Losses—0
Object-Aware Multi-Branch Relation Networks for Spatio-Temporal Video Grounding—0
On Pursuit of Designing Multi-modal Transformer for Video Grounding—0
On the Effects of Video Grounding on Language Models—0
Parallel Attention Network with Sequence Matching for Video Grounding—0
Position-aware Location Regression Network for Temporal Video Grounding—0
SAMA: Towards Multi-Turn Referential Grounded Video Chat with Large Language Models—0
Semi-Supervised Video Paragraph Grounding With Contrastive Encoder—0
Seq2Time: Sequential Knowledge Transfer for Video LLM Temporal Grounding—0
SimBase: A Simple Baseline for Temporal Video Grounding—0
Simplify Implant Depth Prediction as Video Grounding: A Texture Perceive Implant Depth Prediction Network—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