Papers Video-Text Retrieval
“Video-Text Retrieval” 태그가 달린 논문 125편 · 필터 해제
R^3: Composed Video Retrieval via Reasoning-Guided Recalling and Re-ranking
The CoVR-R challenge evaluates composed video retrieval, where a system must retrieve a target video from a large gallery given a reference video and a textual edit instruction. This setting is not a standard video-text …
Video-Text RetrievalVideo RetrievalOmniRetriever: Any-to-Any Audio-Video-Text Retrieval via Fusion-as-Teacher Distillation
Unified multimodal embedding spaces have become the standard interface for cross-modal retrieval and multimodal RAG, and recent audio-video-text (AVT) encoders extend this setting to three modalities. Such encoders can p…
Cross-Modal RetrievalVideo-Text RetrievalTempRet: Temporal Enhancement and Two-Stage Reranking for CVPR 2026 EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge
Video-text retrieval has witnessed remarkable progress driven by large-scale vision-language pretraining, yet most existing approaches inherit an implicit assumption from image-text retrieval: that visual semantics can b…
Video-Text RetrievalImage-text matchingVideo RetrievalUSV: Towards Understanding the User-generated Short-form Videos
Several large-scale video datasets have been published these years and have advanced the area of video understanding. However, the newly emerged user-generated short-form videos have rarely been studied. This paper prese…
Video-Text RetrievalContrastive LearningCluster-Wise Spatio-Temporal Masking for Efficient Video-Language Pretraining
Large-scale video-language pretraining enables strong generalization across multimodal tasks but often incurs prohibitive computational costs. Although recent advances in masked visual modeling help mitigate this issue, …
Video Question AnsweringVideo-Text RetrievalVideo CaptioningSAVE: Speech-Aware Video Representation Learning for Video-Text Retrieval
For video-text retrieval, the use of CLIP has been a de facto choice. Since CLIP provides only image and text encoders, this consensus has led to a biased paradigm that entirely ignores the sound track of videos. While s…
Representation LearningVideo-Text RetrievalRobust Test-time Video-Text Retrieval: Benchmarking and Adapting for Query Shifts
Modern video-text retrieval (VTR) models excel on in-distribution benchmarks but are highly vulnerable to real-world query shifts, where the distribution of query data deviates from the training domain, leading to a shar…
Test-time AdaptationVideo-Text RetrievalVidVec: Unlocking Video MLLM Embeddings for Video-Text Retrieval
Recent studies have adapted generative Multimodal Large Language Models (MLLMs) into embedding extractors for vision tasks, typically through fine-tuning to produce universal representations. However, their performance o…
Video-Text RetrievalVideo RetrievalDelving Deeper: Hierarchical Visual Perception for Robust Video-Text Retrieval
Video-text retrieval (VTR) aims to locate relevant videos using natural language queries. Current methods, often based on pre-trained models like CLIP, are hindered by video's inherent redundancy and their reliance on co…
Natural Language QueriesVideo-Text RetrievalMSAM: Multi-Semantic Adaptive Mining for Cross-Modal Drone Video-Text Retrieval
With the advancement of drone technology, the volume of video data increases rapidly, creating an urgent need for efficient semantic retrieval. We are the first to systematically propose and study the drone video-text re…
Video-Text RetrievalSemantic RetrievalFrom Captions to Keyframes: KeyScore for Multimodal Frame Scoring and Video-Language Understanding
Selecting informative keyframes is critical for efficient video understanding, yet existing approaches often rely on heuristics, ignore semantics, or produce redundant frames. We propose KeyScore, a caption-aware frame s…
Video-Text RetrievalSemantic SimilarityAction RecognitionAutoArabic: A Three-Stage Framework for Localizing Video-Text Retrieval Benchmarks
Video-to-text and text-to-video retrieval are dominated by English benchmarks (e.g. DiDeMo, MSR-VTT) and recent multilingual corpora (e.g. RUDDER), yet Arabic remains underserved, lacking localized evaluation metrics. We…
Video-Text RetrievalVideo RetrievalContext-Adaptive Multi-Prompt Embedding with Large Language Models for Vision-Language Alignment
We propose Context-Adaptive Multi-Prompt Embedding, a novel approach to enrich semantic representations in vision-language contrastive learning. Unlike standard CLIP-style models that rely on a single text embedding, our…
Contrastive LearningVideo-Text RetrievalRepresentation Shift: Unifying Token Compression with FlashAttention
Transformers have demonstrated remarkable success across vision, language, and video. Yet, increasing task complexity has led to larger models and more tokens, raising the quadratic cost of self-attention and the overhea…
Video-Text RetrievalDiscoVLA: Discrepancy Reduction in Vision, Language, and Alignment for Parameter-Efficient Video-Text Retrieval
The parameter-efficient adaptation of the image-text pretraining model CLIP for video-text retrieval is a prominent area of research. While CLIP is focused on image-level vision-language matching, video-text retrieval de…
Image CaptioningRetrievalText RetrievalVideo Alignment+1One Trajectory, One Token: Grounded Video Tokenization via Panoptic Sub-object Trajectory
Effective video tokenization is critical for scaling transformer models for long videos. Current approaches tokenize videos using space-time patches, leading to excessive tokens and computational inefficiencies. The best…
Contrastive LearningText RetrievalToken ReductionVideo-Text Retrieval+1LoVR: A Benchmark for Long Video Retrieval in Multimodal Contexts
Long videos contain a vast amount of information, making video-text retrieval an essential and challenging task in multimodal learning. However, existing benchmarks suffer from limited video duration, low-quality caption…
Caption GenerationRetrievalText RetrievalVideo Retrieval+2Towards Understanding Camera Motions in Any Video
We introduce CameraBench, a large-scale dataset and benchmark designed to assess and improve camera motion understanding. CameraBench consists of ~3,000 diverse internet videos, annotated by experts through a rigorous mu…
Question AnsweringText RetrievalVideo Question AnsweringVideo-Text RetrievalLV-MAE: Learning Long Video Representations through Masked-Embedding Autoencoders
In this work, we introduce long-video masked-embedding autoencoders (LV-MAE), a self-supervised learning framework for long video representation. Our approach treats short- and long-span dependencies as two separate task…
Self-Supervised LearningText RetrievalVideo-Text RetrievalLearning Audio-guided Video Representation with Gated Attention for Video-Text Retrieval
Video-text retrieval, the task of retrieving videos based on a textual query or vice versa, is of paramount importance for video understanding and multimodal information retrieval. Recent methods in this area rely primar…
Information RetrievalRepresentation LearningRetrievalText Retrieval+2