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Video Classification
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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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Optimizing Temporal Convolutional Network inference on FPGA-based accelerators
Video Contents Understanding using Deep Neural Networks
PipeNet: Selective Modal Pipeline of Fusion Network for Multi-Modal Face Anti-Spoofing
TAEN: Temporal Aware Embedding Network for Few-Shot Action Recognition
Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?
X3D: Expanding Architectures for Efficient Video Recognition
Revisiting Few-shot Activity Detection with Class Similarity Control
Convolutional Spiking Neural Networks for Spatio-Temporal Feature Extraction
Motion-Excited Sampler: Video Adversarial Attack with Sparked Prior
On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Location
Rethinking Zero-shot Video Classification: End-to-end Training for Realistic Applications
VideoSSL: Semi-Supervised Learning for Video Classification
Over-the-Air Adversarial Flickering Attacks against Video Recognition Networks
Learning spatio-temporal representations with temporal squeeze pooling
FSD-10: A Dataset for Competitive Sports Content Analysis
iqiyi Submission to ActivityNet Challenge 2019 Kinetics-700 challenge: Hierarchical Group-wise Attention
Cross-Modality Attention with Semantic Graph Embedding for Multi-Label Classification
Appending Adversarial Frames for Universal Video Attack
Video action detection by learning graph-based spatio-temporal interactions
DASZL: Dynamic Action Signatures for Zero-shot Learning
VideoDG: Generalizing Temporal Relations in Videos to Novel Domains
A Multigrid Method for Efficiently Training Video Models
A Spectral Nonlocal Block for Neural Networks
Towards Train-Test Consistency for Semi-supervised Temporal Action Localization
Fast Non-Local Neural Networks with Spectral Residual Learning
Gated Channel Transformation for Visual Recognition
Self-Paced Video Data Augmentation with Dynamic Images Generated by Generative Adversarial Networks
Metric-Based Few-Shot Learning for Video Action Recognition
Identifying and Resisting Adversarial Videos Using Temporal Consistency
Distributed Deep Convolutional Neural Networks for the Internet-of-Things
Two-Stream Video Classification with Cross-Modality Attention
Multi-Agent Reinforcement Learning Based Frame Sampling for Effective Untrimmed Video Recognition
AVD: Adversarial Video Distillation
Few-Shot Video Classification via Temporal Alignment
Loss Switching Fusion with Similarity Search for Video Classification
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