https://github.com/mindspore-ai/models/tree/master/research/cv/ecolite
ECO: Efficient Convolutional Network for Online Video Understanding
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Video Classification is the task of producing a label that is relevant to the video given its frames. A good video level classifier is one that not only provides accurate frame labels, but also best describes the entire video given the features and the annotations of the various frames in the video. For example, a video might contain a tree in some frame, but the label that is central to the video might be something else (e.g., “hiking”). The granularity of the labels that are needed to describe the frames and the video depends on the task. Typical tasks include assigning one or more global labels to the video, and assigning one or more labels for each frame inside the video.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
ECO: Efficient Convolutional Network for Online Video Understanding
Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?
Learnable pooling with Context Gating for video classification
Is Space-Time Attention All You Need for Video Understanding?
Revisiting ResNets: Improved Training and Scaling Strategies
VideoMix: Rethinking Data Augmentation for Video Classification
TNT: Text-Conditioned Network with Transductive Inference for Few-Shot Video Classification
MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks
Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution
Learnable pooling with Context Gating for video classification
A Dataset for Medical Instructional Video Classification and Question Answering
Is Space-Time Attention All You Need for Video Understanding?
Is Space-Time Attention All You Need for Video Understanding?
On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Location
Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks
MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification
Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?
Revisiting ResNets: Improved Training and Scaling Strategies
Billion-scale semi-supervised learning for image classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.