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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.
One Trajectory, One Token: Grounded Video Tokenization via Panoptic Sub-object Trajectory
LoVR: A Benchmark for Long Video Retrieval in Multimodal Contexts
Towards Understanding Camera Motions in Any Video
LV-MAE: Learning Long Video Representations through Masked-Embedding Autoencoders
Learning Audio-guided Video Representation with Gated Attention for Video-Text Retrieval
V$^2$Dial: Unification of Video and Visual Dialog via Multimodal Experts
Temporal Working Memory: Query-Guided Segment Refinement for Enhanced Multimodal Understanding
Expertized Caption Auto-Enhancement for Video-Text Retrieval
CaReBench: A Fine-Grained Benchmark for Video Captioning and Retrieval
Reversed in Time: A Novel Temporal-Emphasized Benchmark for Cross-Modal Video-Text Retrieval
CAREL: Instruction-guided reinforcement learning with cross-modal auxiliary objectives
Beyond Coarse-Grained Matching in Video-Text Retrieval
Text Proxy: Decomposing Retrieval from a 1-to-N Relationship into N 1-to-1 Relationships for Text-Video Retrieval
NAVERO: Unlocking Fine-Grained Semantics for Video-Language Compositionality
Learning Video Context as Interleaved Multimodal Sequences
Video-Language Alignment via Spatio-Temporal Graph Transformer
EA-VTR: Event-Aware Video-Text Retrieval
Multi-Scale Temporal Difference Transformer for Video-Text Retrieval
Diving Deep into the Motion Representation of Video-Text Models
HENASY: Learning to Assemble Scene-Entities for Egocentric Video-Language Model
Uncertainty-aware sign language video retrieval with probability distribution modeling
An Empirical Study of Excitation and Aggregation Design Adaptions in CLIP4Clip for Video-Text Retrieval
RETTA: Retrieval-Enhanced Test-Time Adaptation for Zero-Shot Video Captioning
HaVTR: Improving Video-Text Retrieval Through Augmentation Using Large Foundation Models
vid-TLDR: Training Free Token merging for Light-weight Video Transformer
Unifying Latent and Lexicon Representations for Effective Video-Text Retrieval
Video Editing for Video Retrieval
M2-RAAP: A Multi-Modal Recipe for Advancing Adaptation-based Pre-training towards Effective and Efficient Zero-shot Video-text Retrieval
InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks
ProS: Prompting-to-simulate Generalized knowledge for Universal Cross-Domain Retrieval
RGNet: A Unified Clip Retrieval and Grounding Network for Long Videos
Leveraging Generative Language Models for Weakly Supervised Sentence Component Analysis in Video-Language Joint Learning
TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding
Harvest Video Foundation Models via Efficient Post-Pretraining
TESTA: Temporal-Spatial Token Aggregation for Long-form Video-Language Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.