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Spatio-Temporal Video Grounding
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Spatio-temporal video grounding is a computer vision and natural language processing (NLP) task that involves linking textual descriptions to specific spatio-temporal regions or moments in a video. In other words, it aims to determine which parts of a video correspond to a given textual query or description. This task is essential for various applications, including video summarization, content-based video retrieval, video captioning, and more.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Large-scale Pre-training for Grounded Video Caption Generation
Knowing Your Target: Target-Aware Transformer Makes Better Spatio-Temporal Video Grounding
Contextual Self-paced Learning for Weakly Supervised Spatio-Temporal Video Grounding
Context-Guided Spatio-Temporal Video Grounding
Video-GroundingDINO: Towards Open-Vocabulary Spatio-Temporal Video Grounding
PG-Video-LLaVA: Pixel Grounding Large Video-Language Models
Guided Attention for Interpretable Motion Captioning
What, when, and where? -- Self-Supervised Spatio-Temporal Grounding in Untrimmed Multi-Action Videos from Narrated Instructions
Embracing Consistency: A One-Stage Approach for Spatio-Temporal Video Grounding
STVGFormer: Spatio-Temporal Video Grounding with Static-Dynamic Cross-Modal Understanding
Gaussian Kernel-based Cross Modal Network for Spatio-Temporal Video Grounding
TubeDETR: Spatio-Temporal Video Grounding with Transformers
Augmented 2D-TAN: A Two-stage Approach for Human-centric Spatio-Temporal Video Grounding
Human-centric Spatio-Temporal Video Grounding With Visual Transformers
Object-Aware Multi-Branch Relation Networks for Spatio-Temporal Video Grounding
Where Does It Exist: Spatio-Temporal Video Grounding for Multi-Form Sentences
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