https://github.com/facebookresearch/multimodal
CoCa: Contrastive Captioners are Image-Text Foundation Models
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/facebookresearch/videoalignment
Circulant temporal encoding for video retrieval and temporal alignment
https://github.com/fcakir/deep-mihash
https://github.com/gabeur/mmt
https://github.com/happyharrycn/actionformer_release
https://github.com/hbzhang/cvpr2020
Learning a Text-Video Embedding from Incomplete and Heterogeneous Data
https://github.com/jayleicn/moment_detr
https://github.com/jpeyre/unrel
Learning from Video and Text via Large-Scale Discriminative Clustering
https://github.com/kingcong/ecolite
ECO: Efficient Convolutional Network for Online Video Understanding
https://github.com/laura-wang/video_repres_mas
Self-supervised Video Representation Learning by Uncovering Spatio-temporal Statistics
https://github.com/linjieli222/hero_video_feature_extractor
End-to-End Learning of Visual Representations from Uncurated Instructional Videos
https://github.com/lntzm/cvpr24track-longvideo
TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding
https://github.com/lucidrains/CoCa-pytorch
CoCa: Contrastive Captioners are Image-Text Foundation Models
https://github.com/lucidrains/zorro-pytorch
https://github.com/luigisigillo/gwit
https://github.com/m-bain/CondensedMovies
Condensed Movies: Story Based Retrieval with Contextual Embeddings
https://github.com/m-bain/webvid
Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval
https://github.com/microsoft/unicl
https://github.com/mindspore-ai/models/tree/master/research/cv/ecolite
ECO: Efficient Convolutional Network for Online Video Understanding
https://github.com/mlfoundations/open_clip
CoCa: Contrastive Captioners are Image-Text Foundation Models
https://github.com/modelscope/modelscope
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections
https://github.com/niluthpol/multimodal_vtt
https://github.com/oncescuandreea/audio-retrieval
https://github.com/opengvlab/internvl-mmdetseg
InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks
https://github.com/pku-yuangroup/video-bench
https://github.com/pwc-1/Paper-9/tree/main/3/vat
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
https://github.com/pwc-1/Paper-9/tree/main/5/x_clip
ICSVR: Investigating Compositional and Syntactic Understanding in Video Retrieval Models
https://github.com/roudimit/AVLnet
HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips
https://github.com/ruc-aimc-lab/teachclip
https://github.com/sczwangxiao/tsgvs-mm2023
RTQ: Rethinking Video-language Understanding Based on Image-text Model
https://github.com/sejong-rcv/2022.Paper.TNIP
https://github.com/shallowlearn/sportsreid
https://github.com/shaohuadong2021/dplnet
https://github.com/showlab/region_learner
https://github.com/stanfordvl/atp-video-language
https://github.com/tanveer81/revisionllm
RGNet: A Unified Clip Retrieval and Grounding Network for Long Videos
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.