https://github.com/lancopku/mr-vpc
공식 코드Towards Multimodal Video Paragraph Captioning Models Robust to Missing Modality
Papers with Code 아카이브
Video Understanding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 7
- 평가표
- 0
- 보존된 평가 기록
- 0
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/lark-png/acvubench
공식 코드https://github.com/lavi-lab/aim
공식 코드AIM: Adaptive Inference of Multi-Modal LLMs via Token Merging and Pruning
https://github.com/lavi-lab/tg-vid
공식 코드https://github.com/leaplabthu/uni-adafocus
공식 코드Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition
https://github.com/leexinhao/VideoEval
공식 코드VideoEval: Comprehensive Benchmark Suite for Low-Cost Evaluation of Video Foundation Model
https://github.com/leeyn-43/clover
공식 코드Clover: Towards A Unified Video-Language Alignment and Fusion Model
https://github.com/leon1207/video-rag-master
공식 코드Video-RAG: Visually-aligned Retrieval-Augmented Long Video Comprehension
https://github.com/leonhlj/fac-net
공식 코드Foreground-Action Consistency Network for Weakly Supervised Temporal Action Localization
https://github.com/lidq92/LinearityIQA
공식 코드Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment
https://github.com/lidq92/MDTVSFA
공식 코드Unified Quality Assessment of In-the-Wild Videos with Mixed Datasets Training
https://github.com/lidq92/VSFA
공식 코드https://github.com/lighting-yxli/tinq
공식 코드TINQ: Temporal Inconsistency Guided Blind Video Quality Assessment
https://github.com/liliu-avril/Awesome-Segment-Anything
공식 코드https://github.com/lilydaytoy/openpvsg
공식 코드https://github.com/lilydaytoy/pvsgannotation
공식 코드https://github.com/lilygeorgescu/AED
공식 코드A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video
https://github.com/limuhit/vr-video-quality-in-the-wild
공식 코드Perceptual Quality Assessment of Virtual Reality Videos in the Wild
https://github.com/linrongc/solution_youtube8m_v3
공식 코드https://github.com/linziyi96/st-adapter
공식 코드ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning
https://github.com/littlespray/crave
공식 코드Content-Rich AIGC Video Quality Assessment via Intricate Text Alignment and Motion-Aware Consistency
https://github.com/littlespray/e-bench
공식 코드VE-Bench: Subjective-Aligned Benchmark Suite for Text-Driven Video Editing Quality Assessment
https://github.com/littlespray/ve-bench
공식 코드VE-Bench: Subjective-Aligned Benchmark Suite for Text-Driven Video Editing Quality Assessment
https://github.com/lixinustc/kvq-challenge-cvpr-ntire2024
공식 코드NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results
https://github.com/lixinustc/kvqe-challenge-cvpr-ntire2025
공식 코드NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: Methods and Results
https://github.com/ljh0v0/fmd-frechet-motion-distance
공식 코드Fréchet Video Motion Distance: A Metric for Evaluating Motion Consistency in Videos
https://github.com/llyx97/video_reason_bench
공식 코드VideoReasonBench: Can MLLMs Perform Vision-Centric Complex Video Reasoning?
https://github.com/lntzm/hicom
공식 코드Hybrid-Level Instruction Injection for Video Token Compression in Multi-modal Large Language Models
https://github.com/longvideobench/longvideobench
공식 코드LongVideoBench: A Benchmark for Long-context Interleaved Video-Language Understanding
https://github.com/longvideohaystack/tstar
공식 코드Re-thinking Temporal Search for Long-Form Video Understanding
https://github.com/lskdream/gamival
공식 코드GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming Content
https://github.com/lunaproject22/causalchaos
공식 코드https://github.com/lunaproject22/ssg
공식 코드Situational Scene Graph for Structured Human-centric Situation Understanding
https://github.com/lunarshen/dsicovla
공식 코드https://github.com/lx6c78/vision-mamba-a-comprehensive-survey-and-taxonomy
공식 코드https://github.com/mac-automl/quota
공식 코드QuoTA: Query-oriented Token Assignment via CoT Query Decouple for Long Video Comprehension
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.