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
Seq2Time: Sequential Knowledge Transfer for Video LLM Temporal Grounding—0
SimBase: A Simple Baseline for Temporal Video Grounding—0
SynopGround: A Large-Scale Dataset for Multi-Paragraph Video Grounding from TV Dramas and Synopses—0
Multi-sentence Video Grounding for Long Video Generation—0
Described Spatial-Temporal Video Detection—0
AutoTVG: A New Vision-language Pre-training Paradigm for Temporal Video Grounding—0
Simplify Implant Depth Prediction as Video Grounding: A Texture Perceive Implant Depth Prediction Network—0
Artemis: Towards Referential Understanding in Complex VideosCode0
Video-of-Thought: Step-by-Step Video Reasoning from Perception to Cognition—0
SpikeMba: Multi-Modal Spiking Saliency Mamba for Temporal Video Grounding—0
Unified Static and Dynamic Network: Efficient Temporal Filtering for Video GroundingCode0
VideoGrounding-DINO: Towards Open-Vocabulary Spatio-Temporal Video Grounding—0
Video-GroundingDINO: Towards Open-Vocabulary Spatio-Temporal Video Grounding—0
Multi-Modal Domain Adaptation Across Video Scenes for Temporal Video Grounding—0
LLM4VG: Large Language Models Evaluation for Video Grounding—0
Cross-modal Contrastive Learning with Asymmetric Co-attention Network for Video Moment RetrievalCode0
EtC: Temporal Boundary Expand then Clarify for Weakly Supervised Video Grounding with Multimodal Large Language Model—0
Exploring Iterative Refinement with Diffusion Models for Video GroundingCode0
Dual-Path Temporal Map Optimization for Make-up Temporal Video GroundingCode0
DiffusionVMR: Diffusion Model for Joint Video Moment Retrieval and Highlight Detection—0
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
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