https://github.com/towhee-io/towhee/tree/main/towhee/models/tsm
TSM: Temporal Shift Module for Efficient Video Understanding
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/tristanstraub/fastai-clojure
https://github.com/truskovskiyk/nima.pytorch
https://github.com/tu184044109/BVQA_Benchmark
UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content
https://github.com/ubc-vision/mist
https://github.com/ucsb-nlp-chang/textgrad
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/unimodal4reasoning/adaptivediffusion
AIGCBench: Comprehensive Evaluation of Image-to-Video Content Generated by AI
https://github.com/unispac/visual-adversarial-examples-jailbreak-large-language-models
https://github.com/unitaryai/VTC
https://github.com/utlive/BRISQUE
https://github.com/val-iisc/flss
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/videonetworks/laps-transformer
https://github.com/visaVita/mmaction2
https://github.com/vztu/BVQA_Benchmark
UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content
https://github.com/vztu/VIDEVAL
UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content
https://github.com/vztu/VIDEVAL_release
UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content
https://github.com/w-sugar/Video-Swin-Transformer
https://github.com/wanboyang/Anomaly_AR_Net_ICME_2020
Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learning
https://github.com/wangjk666/stts
https://github.com/wei-tim/YOWO
https://github.com/whwu95/ATM
Side4Video: Spatial-Temporal Side Network for Memory-Efficient Image-to-Video Transfer Learning
https://github.com/willard-yuan/video-text-retrieval-papers
CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval
https://github.com/woctezuma/stylegan2-projecting-images
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/wxh1996/VideoAgent
VideoAgent: Long-form Video Understanding with Large Language Model as Agent
https://github.com/xavierpuigf/virtualhome
https://github.com/xiangjieSui/Omnidirectional-Images-as-Moving-Camera-Videos
Perceptual Quality Assessment of Omnidirectional Images as Moving Camera Videos
https://github.com/xiaogangLi/tensorflow-VTN
Lightweight Network Architecture for Real-Time Action Recognition
https://github.com/xinding-sys/StreamMind
Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
https://github.com/xuyang-liu16/globalcom2
Video Compression Commander: Plug-and-Play Inference Acceleration for Video Large Language Models
https://github.com/yahoo/spivak
https://github.com/yangyucheng000/University/tree/main/model-2/cogvlm
CogVLM2: Visual Language Models for Image and Video Understanding
https://github.com/yasar-rehman/fedvssl
https://github.com/yingsen1/unimd
InternVideo: General Video Foundation Models via Generative and Discriminative Learning
https://github.com/yiyixuxu/TimeSformer-rolled-attention
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/yongliang-wu/MM-VID
https://github.com/youtubevos/MaskTrackRCNN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.