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
AutoTVG: A New Vision-language Pre-training Paradigm for Temporal Video Grounding—0
Cascaded Prediction Network via Segment Tree for Temporal Video Grounding—0
Co-Grounding Networks with Semantic Attention for Referring Expression Comprehension in Videos—0
Collaborative Static and Dynamic Vision-Language Streams for Spatio-Temporal Video Grounding—0
Contextual Self-paced Learning for Weakly Supervised Spatio-Temporal Video Grounding—0
Described Spatial-Temporal Video Detection—0
DiffusionVMR: Diffusion Model for Joint Video Moment Retrieval and Highlight Detection—0
End-to-End Dense Video Grounding via Parallel Regression—0
End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding—0
Enhancing Weakly Supervised Video Grounding via Diverse Inference Strategies for Boundary and Prediction Selection—0
EtC: Temporal Boundary Expand then Clarify for Weakly Supervised Video Grounding with Multimodal Large Language Model—0
EVOQUER: Enhancing Temporal Grounding with Video-Pivoted BackQuery Generation—0
Exploiting Feature Diversity for Make-up Temporal Video Grounding—0
G2L: Semantically Aligned and Uniform Video Grounding via Geodesic and Game Theory—0
Gaussian Kernel-based Cross Modal Network for Spatio-Temporal Video Grounding—0
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
Team PKU-WICT-MIPL PIC Makeup Temporal Video Grounding Challenge 2022 Technical Report—0
Unsupervised Temporal Video Grounding with Deep Semantic Clustering—0
VideoGEM: Training-free Action Grounding in Videos—0
Video-GroundingDINO: Towards Open-Vocabulary Spatio-Temporal Video Grounding—0
VideoGrounding-DINO: Towards Open-Vocabulary Spatio-Temporal Video Grounding—0
VideoITG: Multimodal Video Understanding with Instructed Temporal Grounding—0
Video LLMs for Temporal Reasoning in Long Videos—0
Video-of-Thought: Step-by-Step Video Reasoning from Perception to Cognition—0
ViGT: Proposal-free Video Grounding with Learnable Token in Transformer—0
SynopGround: A Large-Scale Dataset for Multi-Paragraph Video Grounding from TV Dramas and Synopses—0
Dense Video Object Captioning from Disjoint SupervisionCode0
A Simple Transformer-Based Model for Ego4D Natural Language Queries ChallengeCode0
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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