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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.
Towards Activated Muscle Group Estimation in the Wild
Temporal Coherent Test-Time Optimization for Robust Video Classification
Video4MRI: An Empirical Study on Brain Magnetic Resonance Image Analytics with CNN-based Video Classification Frameworks
Analysis of Real-Time Hostile Activitiy Detection from Spatiotemporal Features Using Time Distributed Deep CNNs, RNNs and Attention-Based Mechanisms
Reversible Vision Transformers
Augmenting Ego-Vehicle for Traffic Near-Miss and Accident Classification Dataset using Manipulating Conditional Style Translation
Efficient Movie Scene Detection using State-Space Transformers
Truncate-Split-Contrast: A Framework for Learning from Mislabeled Videos
VideoCoCa: Video-Text Modeling with Zero-Shot Transfer from Contrastive Captioners
Evaluation of Explanation Methods of AI -- CNNs in Image Classification Tasks with Reference-based and No-reference Metrics
A Unified Multimodal De- and Re-coupling Framework for RGB-D Motion Recognition
Deep Unsupervised Key Frame Extraction for Efficient Video Classification
BOREx: Bayesian-Optimization--Based Refinement of Saliency Map for Image- and Video-Classification Models
Transfer-learning for video classification: Video Swin Transformer on multiple domains
Linear Video Transformer with Feature Fixation
Overlooked Video Classification in Weakly Supervised Video Anomaly Detection
S4ND: Modeling Images and Videos as Multidimensional Signals Using State Spaces
TAD: A Large-Scale Benchmark for Traffic Accidents Detection from Video Surveillance
Students Engagement Level Detection in Online e-Learning Using Hybrid EfficientNetB7 Together With TCN, LSTM, and Bi-LSTM
FuTH-Net: Fusing Temporal Relations and Holistic Features for Aerial Video Classification
Traffic Congestion Prediction using Deep Convolutional Neural Networks: A Color-coding Approach
On the Surprising Effectiveness of Transformers in Low-Labeled Video Recognition
UAV-CROWD: Violent and non-violent crowd activity simulator from the perspective of UAV
Motion Sensitive Contrastive Learning for Self-supervised Video Representation
Two-Stream Transformer Architecture for Long Video Understanding
P2ANet: A Dataset and Benchmark for Dense Action Detection from Table Tennis Match Broadcasting Videos
SSIVD-Net: A Novel Salient Super Image Classification & Detection Technique for Weaponized Violence
Adaptive occlusion sensitivity analysis for visually explaining video recognition networks
Intelligent 3D Network Protocol for Multimedia Data Classification using Deep Learning
Contrastive Self-Supervised Learning Leads to Higher Adversarial Susceptibility
NSNet: Non-saliency Suppression Sampler for Efficient Video Recognition
Exploring Fine-Grained Audiovisual Categorization with the SSW60 Dataset
Inductive and Transductive Few-Shot Video Classification via Appearance and Temporal Alignments
Temporal and cross-modal attention for audio-visual zero-shot learning
GOCA: Guided Online Cluster Assignment for Self-Supervised Video Representation Learning
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