Papers with Code 아카이브
Video Retrieval
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- 평가표
- 19
- 보존된 평가 기록
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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/PKU-YuanGroup/Video-LLaVA
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/applications/T2VLAD/README_en.md
T2VLAD: Global-Local Sequence Alignment for Text-Video Retrieval
https://github.com/Pradyumn-Patil/updated-research-paper-links-
https://github.com/SilvioGiancola/SoccerNetv2-DevKit
SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos
https://github.com/Soldelli/VLG-Net
VLG-Net: Video-Language Graph Matching Network for Video Grounding
https://github.com/WuJie1010/Temporally-language-grounding
https://github.com/X-PLUG/mPLUG-2
mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video
https://github.com/XLearning-SCU/2024-ICLR-Norton
Multi-granularity Correspondence Learning from Long-term Noisy Videos
https://github.com/XinshaoAmosWang/DerivativeManipulation
https://github.com/XinshaoAmosWang/Emphasis-Regularisation-by-Gradient-Rescaling
https://github.com/YangLiu9208/SAKDN
TCGL: Temporal Contrastive Graph for Self-supervised Video Representation Learning
https://github.com/aim3-ruc/youmakeup_challenge2022
Negative Sample Matters: A Renaissance of Metric Learning for Temporal Grounding
https://github.com/aimagelab/rmsnet_soccer
SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos
https://github.com/akashe/ProgrammingInterview
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
https://github.com/akoepke/audio-retrieval-benchmark
https://github.com/alipay/vcsl
TransVCL: Attention-enhanced Video Copy Localization Network with Flexible Supervision
https://github.com/alvin-zeng/drn
https://github.com/amitakamath/hard_positives
CoCa: Contrastive Captioners are Image-Text Foundation Models
https://github.com/amitakamath/whatsup_vlms
CoCa: Contrastive Captioners are Image-Text Foundation Models
https://github.com/antoine77340/S3D_HowTo100M
HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips
https://github.com/antoine77340/howto100m
A Joint Sequence Fusion Model for Video Question Answering and Retrieval
https://github.com/antoine77340/iccv17learning
Learning from Video and Text via Large-Scale Discriminative Clustering
https://github.com/antoine77340/milnce_howto100m
HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips
https://github.com/caijincen712/CE
Learning a Text-Video Embedding from Incomplete and Heterogeneous Data
https://github.com/chendelong1999/subobjects
One Trajectory, One Token: Grounded Video Tokenization via Panoptic Sub-object Trajectory
https://github.com/code-implementation1/Code1/tree/main/ecolite
ECO: Efficient Convolutional Network for Online Video Understanding
https://github.com/code-implementation1/Code2/tree/main/AdderNGD
ECO: Efficient Convolutional Network for Online Video Understanding
https://github.com/csdongxian/csdongxian
https://github.com/cshizhe/hgr_v2t
Fine-grained Video-Text Retrieval with Hierarchical Graph Reasoning
https://github.com/damo-nlp-sg/videollama2
Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
https://github.com/damo-nlp-sg/videollama3
Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
https://github.com/danieljf24/hybrid_space
https://github.com/escorciav/Mixture-of-Embedding-Experts
Learning a Text-Video Embedding from Incomplete and Heterogeneous Data
https://github.com/facebookresearch/EgoTV
CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval
https://github.com/facebookresearch/fairseq
VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.