https://github.com/starmemda/CAMoE
공식 코드Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss
Papers with Code 아카이브
Video Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
- 19
- 보존된 평가 기록
- 308
The objective of video retrieval is as follows: given a text query and a pool of candidate videos, select the video which corresponds to the text query. Typically, the videos are returned as a ranked list of candidates and scored via document retrieval metrics.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/starmemda/camow
공식 코드Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss
https://github.com/sunoh-kim/pps
공식 코드https://github.com/tanveer81/rgnet
공식 코드RGNet: A Unified Clip Retrieval and Grounding Network for Long Videos
https://github.com/technomad-ds/lovr-benchmark
공식 코드LoVR: A Benchmark for Long Video Retrieval in Multimodal Contexts
https://github.com/tencentarc/mcq
공식 코드Bridging Video-text Retrieval with Multiple Choice Questions
https://github.com/tencentarc/umt
공식 코드UMT: Unified Multi-modal Transformers for Joint Video Moment Retrieval and Highlight Detection
https://github.com/transvcl/transvcl
공식 코드TransVCL: Attention-enhanced Video Copy Localization Network with Flexible Supervision
https://github.com/trunpm/pmt-aaai23
공식 코드Efficient End-to-End Video Question Answering with Pyramidal Multimodal Transformer
https://github.com/tsujuifu/pytorch_empirical-mvm
공식 코드An Empirical Study of End-to-End Video-Language Transformers with Masked Visual Modeling
https://github.com/tsujuifu/pytorch_violet
공식 코드VIOLET : End-to-End Video-Language Transformers with Masked Visual-token Modeling
https://github.com/txh-mercury/cosa
공식 코드COSA: Concatenated Sample Pretrained Vision-Language Foundation Model
https://github.com/txh-mercury/vast
공식 코드VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset
https://github.com/tzhhhh123/HC-STVG
공식 코드Human-centric Spatio-Temporal Video Grounding With Visual Transformers
https://github.com/uark-aicv/video_representation
공식 코드Contextual Explainable Video Representation: Human Perception-based Understanding
https://github.com/ucf-crcv/tf-covr
공식 코드From Play to Replay: Composed Video Retrieval for Temporally Fine-Grained Videos
https://github.com/uniadapter/uniadapter
공식 코드UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal Modeling
https://github.com/utaustin-swarmlab/neuro-symbolic-agent-challenge
공식 코드https://github.com/videocrafter/animate-a-story
공식 코드Animate-A-Story: Storytelling with Retrieval-Augmented Video Generation
https://github.com/waybarrios/guidance-based-video-grounding
공식 코드https://github.com/wengsyx/ccgs
공식 코드Learning to Locate Visual Answer in Video Corpus Using Question
https://github.com/wengzejia1/open-vclip
공식 코드Building an Open-Vocabulary Video CLIP Model with Better Architectures, Optimization and Data
https://github.com/whwu95/Cap4Video
공식 코드Cap4Video: What Can Auxiliary Captions Do for Text-Video Retrieval?
https://github.com/willard-yuan/hashing-baseline-for-image-retrieval
공식 코드https://github.com/willyfh/msvd-indonesian
공식 코드MSVD-Indonesian: A Benchmark for Multimodal Video-Text Tasks in Indonesian
https://github.com/wiselnn570/videorope
공식 코드VideoRoPE: What Makes for Good Video Rotary Position Embedding?
https://github.com/wjun0830/qd-detr
공식 코드Query-Dependent Video Representation for Moment Retrieval and Highlight Detection
https://github.com/woodfrog/vse_infty
공식 코드Learning the Best Pooling Strategy for Visual Semantic Embedding
https://github.com/www-ye/timezero
공식 코드TimeZero: Temporal Video Grounding with Reasoning-Guided LVLM
https://github.com/xian-sh/unisdnet
공식 코드Unified Static and Dynamic Network: Efficient Temporal Filtering for Video Grounding
https://github.com/xianyongqin/few-shot-video-classification
공식 코드Generalized Few-Shot Video Classification with Video Retrieval and Feature Generation
https://github.com/xid32/naacl_2025_twm
공식 코드Temporal Working Memory: Query-Guided Segment Refinement for Enhanced Multimodal Understanding
https://github.com/xuguohai/X-CLIP
공식 코드X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval
https://github.com/xwen99/temporal_context_aggregation
공식 코드Temporal Context Aggregation for Video Retrieval with Contrastive Learning
https://github.com/yangliu9208/tcgl
공식 코드TCGL: Temporal Contrastive Graph for Self-supervised Video Representation Learning
https://github.com/yellow-binary-tree/hawkeye
공식 코드HawkEye: Training Video-Text LLMs for Grounding Text in Videos
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.