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/ninatu/howtocaption
공식 코드HowToCaption: Prompting LLMs to Transform Video Annotations at Scale
https://github.com/nvlabs/vila
공식 코드LongVILA: Scaling Long-Context Visual Language Models for Long Videos
https://github.com/omron-sinicx/com_kitchens
공식 코드COM Kitchens: An Unedited Overhead-view Video Dataset as a Vision-Language Benchmark
https://github.com/opennlplab/favdbench
공식 코드https://github.com/primle/LSMDC-Context
공식 코드https://github.com/primle/S2VT-Pointer
공식 코드https://github.com/pytorch/fairseq
공식 코드VLM: Task-agnostic Video-Language Model Pre-training for Video Understanding
https://github.com/qiqang/uedvc
공식 코드Unifying Event Detection and Captioning as Sequence Generation via Pre-Training
https://github.com/quangminhdinh/trafficvlm
공식 코드TrafficVLM: A Controllable Visual Language Model for Traffic Video Captioning
https://github.com/ramakanth-pasunuru/CAS-MAS
공식 코드https://github.com/sbargal/Caffe-ExcitationBP-RNNs
공식 코드https://github.com/shajiayu1/OmniDataComposer
공식 코드OmniDataComposer: A Unified Data Structure for Multimodal Data Fusion and Infinite Data Generation
https://github.com/shengyuzhang/Poet
공식 코드https://github.com/shiyaya/emscore
공식 코드EMScore: Evaluating Video Captioning via Coarse-Grained and Fine-Grained Embedding Matching
https://github.com/shiyi-zh0408/nae_cvpr2024
공식 코드Narrative Action Evaluation with Prompt-Guided Multimodal Interaction
https://github.com/showlab/movieseq
공식 코드https://github.com/silentview/lvd-2m
공식 코드LVD-2M: A Long-take Video Dataset with Temporally Dense Captions
https://github.com/smre-cv/smre
공식 코드Support-set based Multi-modal Representation Enhancement for Video Captioning
https://github.com/sorami/TACL-Membership
공식 코드https://github.com/subho406/OmniNet
공식 코드OmniNet: A unified architecture for multi-modal multi-task learning
https://github.com/tgc1997/RMN
공식 코드Learning to Discretely Compose Reasoning Module Networks for Video Captioning
https://github.com/thorin215/focusedad
공식 코드https://github.com/thu-keg/goal
공식 코드https://github.com/thudm/cogvideo
공식 코드CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer
https://github.com/tsujuifu/pytorch_empirical-mvm
공식 코드An Empirical Study of End-to-End Video-Language Transformers with Masked Visual Modeling
https://github.com/ttengwang/dense-video-captioning-pytorch
공식 코드Dense-Captioning Events in Videos: SYSU Submission to ActivityNet Challenge 2020
https://github.com/ttengwang/pdvc
공식 코드https://github.com/ttgeng233/LongVALE
공식 코드https://github.com/txh-mercury/cosa
공식 코드COSA: Concatenated Sample Pretrained Vision-Language Foundation Model
https://github.com/txh-mercury/vast
공식 코드VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset
https://github.com/uark-aicv/video_representation
공식 코드Contextual Explainable Video Representation: Human Perception-based Understanding
https://github.com/uark-aicv/vltint
공식 코드VLTinT: Visual-Linguistic Transformer-in-Transformer for Coherent Video Paragraph Captioning
https://github.com/ucf-sst-lab/aicity2024cvprw
공식 코드Enhancing Traffic Safety with Parallel Dense Video Captioning for End-to-End Event Analysis
https://github.com/v-iashin/BMT
공식 코드A Better Use of Audio-Visual Cues: Dense Video Captioning with Bi-modal Transformer
https://github.com/v-iashin/MDVC
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.