https://github.com/timothyhtimothy/fast-vqa
공식 코드FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling
Papers with Code 아카이브
Video Understanding
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A crucial task of Video Understanding is to recognise and localise (in space and time) different actions or events appearing in the video.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/tingyu215/ts-llava
공식 코드https://github.com/tingz123/fha-kitchens
공식 코드https://github.com/tong-zeng/dvbench
공식 코드https://github.com/tsujuifu/pytorch_violet
공식 코드VIOLET : End-to-End Video-Language Transformers with Masked Visual-token Modeling
https://github.com/ttgeng233/LongVALE
공식 코드https://github.com/tu184044109/VIDEVAL_release
공식 코드UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content
https://github.com/tuantng/temporalmaxer
공식 코드TemporalMaxer: Maximize Temporal Context with only Max Pooling for Temporal Action Localization
https://github.com/tychen-SJTU/MECD-Benchmark
공식 코드MECD: Unlocking Multi-Event Causal Discovery in Video Reasoning
https://github.com/uark-aicv/video_representation
공식 코드Contextual Explainable Video Representation: Human Perception-based Understanding
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/umarkhalidai/cefhri-efficient-federated-learning
공식 코드https://github.com/unabletousegit/videox22l
공식 코드Task-Aware KV Compression For Cost-Effective Long Video Understanding
https://github.com/uniqzheng/hfr-bvqa
공식 코드FAVER: Blind Quality Prediction of Variable Frame Rate Videos
https://github.com/uolmm/tpqi-vqa
공식 코드Exploring the Effectiveness of Video Perceptual Representation in Blind Video Quality Assessment
https://github.com/utaustin-swarmlab/neuro-symbolic-agent-challenge
공식 코드https://github.com/utaustin-swarmlab/temporal-logic-video-dataset
공식 코드https://github.com/varunj/cvpr-vqe
공식 코드https://github.com/vectorspacelab/video-xl
공식 코드Video-XL: Extra-Long Vision Language Model for Hour-Scale Video Understanding
https://github.com/vincent-zhq/hlv-1k
공식 코드HLV-1K: A Large-scale Hour-Long Video Benchmark for Time-Specific Long Video Understanding
https://github.com/visual-ai/prunevid
공식 코드PruneVid: Visual Token Pruning for Efficient Video Large Language Models
https://github.com/vita-mllm/long-vita
공식 코드Long-VITA: Scaling Large Multi-modal Models to 1 Million Tokens with Leading Short-Context Accuray
https://github.com/vqassessment/bvqi
공식 코드Exploring Opinion-unaware Video Quality Assessment with Semantic Affinity Criterion
https://github.com/vqassessment/dover
공식 코드https://github.com/vqassessment/maxvqa
공식 코드https://github.com/vut-hfut/micro-action
공식 코드https://github.com/vztu/RAPIQUE
공식 코드RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Content
https://github.com/wangheda/youtube-8m
공식 코드The Monkeytyping Solution to the YouTube-8M Video Understanding Challenge
https://github.com/wangjiarui153/AIGV-Assessor
공식 코드https://github.com/wangjk666/omnivid
공식 코드OmniVid: A Generative Framework for Universal Video Understanding
https://github.com/waybarrios/guidance-based-video-grounding
공식 코드https://github.com/wefwefWEF2/video_classification
공식 코드https://github.com/weirayao/leap
공식 코드Learning Temporally Causal Latent Processes from General Temporal Data
https://github.com/wenzhouyidu/light-vqa
공식 코드Light-VQA: A Multi-Dimensional Quality Assessment Model for Low-Light Video Enhancement
https://github.com/westlake-repl/microlens
공식 코드A Content-Driven Micro-Video Recommendation Dataset at Scale
https://github.com/whb139426/grounded-video-llm
공식 코드Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.