https://github.com/ztangaj/gveval
공식 코드G-VEval: A Versatile Metric for Evaluating Image and Video Captions Using GPT-4o
Papers with Code 아카이브
Video Captioning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 6
- 평가표
- 13
- 보존된 평가 기록
- 86
Video Captioning is a task of automatic captioning a video by understanding the action and event in the video which can help in the retrieval of the video efficiently through text.
Source: NITS-VC System for VATEX Video Captioning Challenge 2020
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/2023-MindSpore-4/Code-5/tree/main/I3D
https://github.com/HanNight/RE-T5
VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research
https://github.com/IgnacioHeredia/plant_classification
https://github.com/InfoX-SEU/DAE-AQA
What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
https://github.com/InfoX-SEU/DAE_AQA
What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
https://github.com/JaywongWang/DenseVideoCaptioning
Bidirectional Attentive Fusion with Context Gating for Dense Video Captioning
https://github.com/KastanDay/video-pretrained-transformer
Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning
https://github.com/LisaAnne/DCC
Deep Compositional Captioning: Describing Novel Object Categories without Paired Training Data
https://github.com/LuoweiZhou/ProcNets-YouCook2
Towards Automatic Learning of Procedures from Web Instructional Videos
https://github.com/MDSKUL/MasterProject
VideoBERT: A Joint Model for Video and Language Representation Learning
https://github.com/MehranTaghian/DQN-Trading
A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading Rules
https://github.com/MindCode-4/code-11/tree/main/delving-deeper-into-convolutional
Delving Deeper into Convolutional Networks for Learning Video Representations
https://github.com/MindCode-4/code-6/tree/main/dice-loss-for-data-imbalanced-nlp-tasks
Delving Deeper into Convolutional Networks for Learning Video Representations
https://github.com/MindSpore-paper-code-3/code8/tree/main/ecolite
ECO: Efficient Convolutional Network for Online Video Understanding
https://github.com/MindSpore-scientific-2/code-3/tree/main/vat
VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research
https://github.com/MindSpore-scientific-2/code-8/tree/main/pytorch_convgru
Delving Deeper into Convolutional Networks for Learning Video Representations
https://github.com/MindSpore-scientific/code-14/tree/main/vat
VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research
https://github.com/NikolasEnt/soccernet-calibration-sportlight
https://github.com/Sejong-VLI/V2T-Action-Graph-JKSUCIS-2023
https://github.com/VP-0822/Video-Keyword-Extractor
https://github.com/WingsBrokenAngel/Semantics-AssistedVideoCaptioningModelTrainedwithScheduledSamplingStrategy
A Semantics-Assisted Video Captioning Model Trained with Scheduled Sampling
https://github.com/X-PLUG/mPLUG-2
mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video
https://github.com/ailab-kyunghee/HiCM2-DVC
HiCM$^2$: Hierarchical Compact Memory Modeling for Dense Video Captioning
https://github.com/aim3-ruc/youmakeup_challenge2022
https://github.com/ali-vilab/i2vgen-xl
A Recipe for Scaling up Text-to-Video Generation with Text-free Videos
https://github.com/amirmazaheri1990/Visual-Text-Correction
https://github.com/ammesatyajit/VideoBERT
VideoBERT: A Joint Model for Video and Language Representation Learning
https://github.com/antoyang/VidChapters
Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning
https://github.com/b05902062/TDConvED
Temporal Deformable Convolutional Encoder-Decoder Networks for Video Captioning
https://github.com/bionick87/ConvGRUCell-pytorch
Delving Deeper into Convolutional Networks for Learning Video Representations
https://github.com/chitwansaharia/HACAModel
https://github.com/chldydgh4687/2020-1.VideoCaptioning
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/cskanani/global_object_proposals
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.