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Video-Text Retrieval

Video-Text retrieval requires understanding of both video and language together. Therefore it's different to video retrieval task.

Papers

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Showing 1–25 of 111 papers

TitleStatusHype
DiscoVLA: Discrepancy Reduction in Vision, Language, and Alignment for Parameter-Efficient Video-Text RetrievalCode1
One Trajectory, One Token: Grounded Video Tokenization via Panoptic Sub-object TrajectoryCode2
LoVR: A Benchmark for Long Video Retrieval in Multimodal ContextsCode1
Towards Understanding Camera Motions in Any Video—0
LV-MAE: Learning Long Video Representations through Masked-Embedding Autoencoders—0
Learning Audio-guided Video Representation with Gated Attention for Video-Text Retrieval—0
V^2Dial: Unification of Video and Visual Dialog via Multimodal Experts—0
Temporal Working Memory: Query-Guided Segment Refinement for Enhanced Multimodal UnderstandingCode3
Expertized Caption Auto-Enhancement for Video-Text RetrievalCode0
V^2Dial: Unification of Video and Visual Dialog via Multimodal Experts—0
Rethinking Noisy Video-Text Retrieval via Relation-aware Alignment—0
CaReBench: A Fine-Grained Benchmark for Video Captioning and Retrieval—0
Reversed in Time: A Novel Temporal-Emphasized Benchmark for Cross-Modal Video-Text RetrievalCode0
CAREL: Instruction-guided reinforcement learning with cross-modal auxiliary objectivesCode0
Beyond Coarse-Grained Matching in Video-Text Retrieval—0
Text Proxy: Decomposing Retrieval from a 1-to-N Relationship into N 1-to-1 Relationships for Text-Video RetrievalCode1
NAVERO: Unlocking Fine-Grained Semantics for Video-Language Compositionality—0
Learning Video Context as Interleaved Multimodal SequencesCode1
Video-Language Alignment via Spatio-Temporal Graph TransformerCode1
EA-VTR: Event-Aware Video-Text Retrieval—0
Multi-Scale Temporal Difference Transformer for Video-Text Retrieval—0
Diving Deep into the Motion Representation of Video-Text ModelsCode0
HENASY: Learning to Assemble Scene-Entities for Egocentric Video-Language Model—0
Uncertainty-aware sign language video retrieval with probability distribution modeling—0
An Empirical Study of Excitation and Aggregation Design Adaptions in CLIP4Clip for Video-Text Retrieval—0
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