https://github.com/maryeon/whiten_mtd
공식 코드Let All be Whitened: Multi-teacher Distillation for Efficient Visual Retrieval
Papers with Code 아카이브
Video Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 5
- 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/mastervito/diffusionvg
공식 코드Exploring Iterative Refinement with Diffusion Models for Video Grounding
https://github.com/mbzuai-oryx/video-llava
공식 코드https://github.com/mcg-nju/mmn
공식 코드Negative Sample Matters: A Renaissance of Metric Learning for Temporal Grounding
https://github.com/mengcaopku/locvtp
공식 코드https://github.com/mever-team/ausil
공식 코드Audio-based Near-Duplicate Video Retrieval with Audio Similarity Learning
https://github.com/mever-team/distill-and-select
공식 코드DnS: Distill-and-Select for Efficient and Accurate Video Indexing and Retrieval
https://github.com/microsoft/UniVL
공식 코드UniVL: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation
https://github.com/microsoft/lavender
공식 코드LAVENDER: Unifying Video-Language Understanding as Masked Language Modeling
https://github.com/microsoft/xpretrain
공식 코드Advancing High-Resolution Video-Language Representation with Large-Scale Video Transcriptions
https://github.com/mlvlab/MELTR
공식 코드MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation Models
https://github.com/mlvlab/vid-tldr
공식 코드vid-TLDR: Training Free Token merging for Light-weight Video Transformer
https://github.com/mlvlab/vidchain
공식 코드VidChain: Chain-of-Tasks with Metric-based Direct Preference Optimization for Dense Video Captioning
https://github.com/musicman217/Text-Proxy
공식 코드https://github.com/musicman217/gare-text-video-retrieval
공식 코드Contrastive Alignment with Semantic Gap-Aware Corrections in Text-Video Retrieval
https://github.com/mwray/Semantic-Video-Retrieval
공식 코드https://github.com/mzhaoshuai/CenterCLIP
공식 코드CenterCLIP: Token Clustering for Efficient Text-Video Retrieval
https://github.com/mzolfaghari/ECO-efficient-video-understanding
공식 코드ECO: Efficient Convolutional Network for Online Video Understanding
https://github.com/nanguoshun/IVG
공식 코드Interventional Video Grounding with Dual Contrastive Learning
https://github.com/necla-ml/cpr
공식 코드Self-supervised Video Representation Learning with Cascade Positive Retrieval
https://github.com/nevermorelch/ccg
공식 코드Consistency of Compositional Generalization across Multiple Levels
https://github.com/nguyentthong/MAMA
공식 코드MAMA: Meta-optimized Angular Margin Contrastive Framework for Video-Language Representation Learning
https://github.com/ninatu/everything_at_once
공식 코드Everything at Once -- Multi-modal Fusion Transformer for Video Retrieval
https://github.com/ninatu/howtocaption
공식 코드HowToCaption: Prompting LLMs to Transform Video Annotations at Scale
https://github.com/ninatu/in_style
공식 코드In-Style: Bridging Text and Uncurated Videos with Style Transfer for Text-Video Retrieval
https://github.com/nshubham655/RUDDER
공식 코드https://github.com/omkarthawakar/composed-video-retrieval
공식 코드Composed Video Retrieval via Enriched Context and Discriminative Embeddings
https://github.com/opengvlab/egoexolearn
공식 코드https://github.com/opengvlab/internvideo
공식 코드InternVideo: General Video Foundation Models via Generative and Discriminative Learning
https://github.com/opengvlab/internvideo2
공식 코드InternVideo2: Scaling Foundation Models for Multimodal Video Understanding
https://github.com/opengvlab/internvl
공식 코드InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks
https://github.com/opengvlab/unmasked_teacher
공식 코드Unmasked Teacher: Towards Training-Efficient Video Foundation Models
https://github.com/oravus/DeltaDescriptors
공식 코드Delta Descriptors: Change-Based Place Representation for Robust Visual Localization
https://github.com/oravus/seqNet
공식 코드SeqNet: Learning Descriptors for Sequence-based Hierarchical Place Recognition
https://github.com/papermsucode/mdmmt
공식 코드MDMMT: Multidomain Multimodal Transformer for Video Retrieval
https://github.com/pku-yuangroup/languagebind
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.