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Video-Text Retrieval
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Video-Text retrieval requires understanding of both video and language together. Therefore it's different to video retrieval task.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Building an Open-Vocabulary Video CLIP Model with Better Architectures, Optimization and Data
LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment
Prototype-based Aleatoric Uncertainty Quantification for Cross-modal Retrieval
Unified Coarse-to-Fine Alignment for Video-Text Retrieval
UniPT: Universal Parallel Tuning for Transfer Learning with Efficient Parameter and Memory
Multi-event Video-Text Retrieval
Helping Hands: An Object-Aware Ego-Centric Video Recognition Model
TaCA: Upgrading Your Visual Foundation Model with Task-agnostic Compatible Adapter
Global and Local Semantic Completion Learning for Vision-Language Pre-training
Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
VLAB: Enhancing Video Language Pre-training by Feature Adapting and Blending
Mask to reconstruct: Cooperative Semantics Completion for Video-text Retrieval
Alternating Gradient Descent and Mixture-of-Experts for Integrated Multimodal Perception
SViTT: Temporal Learning of Sparse Video-Text Transformers
CiCo: Domain-Aware Sign Language Retrieval via Cross-Lingual Contrastive Learning
Deep Learning for Video-Text Retrieval: a Review
Cross-Modal Retrieval with Partially Mismatched Pairs
Video-Text Retrieval by Supervised Sparse Multi-Grained Learning
UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal Modeling
Tagging before Alignment: Integrating Multi-Modal Tags for Video-Text Retrieval
Revisiting Temporal Modeling for CLIP-based Image-to-Video Knowledge Transferring
MV-Adapter: Multimodal Video Transfer Learning for Video Text Retrieval
Test of Time: Instilling Video-Language Models with a Sense of Time
Masked Contrastive Pre-Training for Efficient Video-Text Retrieval
Seeing What You Miss: Vision-Language Pre-training with Semantic Completion Learning
VTC: Improving Video-Text Retrieval with User Comments
Vision-Language Pre-training: Basics, Recent Advances, and Future Trends
TokenFlow: Rethinking Fine-grained Cross-modal Alignment in Vision-Language Retrieval
Unified Loss of Pair Similarity Optimization for Vision-Language Retrieval
Text-Adaptive Multiple Visual Prototype Matching for Video-Text Retrieval
OmniVL:One Foundation Model for Image-Language and Video-Language Tasks
CLIP-ViP: Adapting Pre-trained Image-Text Model to Video-Language Representation Alignment
Boosting Video-Text Retrieval with Explicit High-Level Semantics
X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval
LaT: Latent Translation with Cycle-Consistency for Video-Text Retrieval
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.