https://github.com/jacobswan1/Video2Commonsense
공식 코드Video2Commonsense: Generating Commonsense Descriptions to Enrich Video Captioning
Papers with Code 아카이브
Video Captioning
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- 보존된 평가 기록
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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/jayleicn/recurrent-transformer
공식 코드MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning
https://github.com/josch14/semantic-metadata-extraction-from-videos
공식 코드https://github.com/jpthu17/HBI
공식 코드Hierarchical Banzhaf Interaction for General Video-Language Representation Learning
https://github.com/jpthu17/emcl
공식 코드Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations
https://github.com/jssprz/video_captioning_datasets
공식 코드https://github.com/junyangwang0410/knight
공식 코드From Association to Generation: Text-only Captioning by Unsupervised Cross-modal Mapping
https://github.com/katesanders9/grounded-events
공식 코드https://github.com/kuangzijian/Improved-Actor-Relation-Graph-based-Group-Activity-Recognition
공식 코드Improved Actor Relation Graph based Group Activity Recognition
https://github.com/kyrieye/MM-2024
공식 코드Dual-path Collaborative Generation Network for Emotional Video Captioning
https://github.com/lRomul/ball-action-spotting
공식 코드https://github.com/linjieli222/HERO
공식 코드HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training
https://github.com/ludanruan/clip4vla
공식 코드Accommodating Audio Modality in CLIP for Multimodal Processing
https://github.com/lvapeab/ABiViRNet
공식 코드Video Description using Bidirectional Recurrent Neural Networks
https://github.com/lvapeab/interactive-keras-captioning
공식 코드A Neural, Interactive-predictive System for Multimodal Sequence to Sequence Tasks
https://github.com/lvapeab/nmt-keras
공식 코드NMT-Keras: a Very Flexible Toolkit with a Focus on Interactive NMT and Online Learning
https://github.com/lzp870/rsfd
공식 코드Refined Semantic Enhancement towards Frequency Diffusion for Video Captioning
https://github.com/m-bain/frozen-in-time
공식 코드Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval
https://github.com/madhawav/MML
공식 코드Video Moment Localization using Object Evidence and Reverse Captioning
https://github.com/marcusnerva/hmn
공식 코드https://github.com/mbzuai-oryx/videogpt-plus
공식 코드VideoGPT+: Integrating Image and Video Encoders for Enhanced Video Understanding
https://github.com/md-mohaiminul/VideoRecap
공식 코드https://github.com/mfurkanilaslan/vg-tvp
공식 코드VG-TVP: Multimodal Procedural Planning via Visually Grounded Text-Video Prompting
https://github.com/microsoft/GenerativeImage2Text
공식 코드GIT: A Generative Image-to-text Transformer for Vision and Language
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/swinbert
공식 코드SwinBERT: End-to-End Transformers with Sparse Attention for Video Captioning
https://github.com/mikewangwzhl/vidil
공식 코드Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners
https://github.com/mlvccn/pkg_vidcap
공식 코드Pseudo-labeling with Keyword Refining for Few-Supervised Video Captioning
https://github.com/mlvlab/MELTR
공식 코드MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation Models
https://github.com/mlvlab/vidchain
공식 코드VidChain: Chain-of-Tasks with Metric-based Direct Preference Optimization for Dense Video Captioning
https://github.com/mugen-org/MUGEN_baseline
공식 코드MUGEN: A Playground for Video-Audio-Text Multimodal Understanding and GENeration
https://github.com/mushfiqur11/SS-VideoCaptioning
공식 코드Video captioning with stacked attention and semantic hard pull
https://github.com/mutonix/vript
공식 코드https://github.com/mv-lab/youtube8m-19
공식 코드Multi-attention Networks for Temporal Localization of Video-level Labels
https://github.com/mzolfaghari/ECO-efficient-video-understanding
공식 코드ECO: Efficient Convolutional Network for Online Video Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.