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
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Long-term Leap Attention, Short-term Periodic Shift for Video Classification
Beyond Transfer Learning: Co-finetuning for Action Localisation
PatchZero: Defending against Adversarial Patch Attacks by Detecting and Zeroing the Patch
Revisiting Classifier: Transferring Vision-Language Models for Video Recognition
Key-frame Guided Network for Thyroid Nodule Recognition using Ultrasound Videos
Automatic Concept Extraction for Concept Bottleneck-based Video Classification
Analysis and Extensions of Adversarial Training for Video Classification
Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEs
Learning Muti-expert Distribution Calibration for Long-tailed Video Classification
Convex Combination Consistency between Neighbors for Weakly-supervised Action Localization
Attention in Attention: Modeling Context Correlation for Efficient Video Classification
Calibrating Class Weights with Multi-Modal Information for Partial Video Domain Adaptation
SOS! Self-supervised Learning Over Sets Of Handled Objects In Egocentric Action Recognition
Long Movie Clip Classification with State-Space Video Models
StyleFool: Fooling Video Classification Systems via Style Transfer
Alignment-Uniformity aware Representation Learning for Zero-shot Video Classification
Semi-supervised and Deep learning Frameworks for Video Classification and Key-frame Identification
Unsupervised Pre-training for Temporal Action Localization Tasks
Transformers Meet Visual Learning Understanding: A Comprehensive Review
Movie Genre Classification by Language Augmentation and Shot Sampling
RoVISQ: Reduction of Video Service Quality via Adversarial Attacks on Deep Learning-based Video Compression
TFCNet: Temporal Fully Connected Networks for Static Unbiased Temporal Reasoning
Recent Trends in 2D Object Detection and Applications in Video Event Recognition
A Dataset for Medical Instructional Video Classification and Question Answering
Learning To Recognize Procedural Activities with Distant Supervision
Capturing Temporal Information in a Single Frame: Channel Sampling Strategies for Action Recognition
UniFormer: Unifying Convolution and Self-attention for Visual Recognition
Progressive Video Summarization via Multimodal Self-supervised Learning
Co-training Transformer with Videos and Images Improves Action Recognition
Approaches Toward Physical and General Video Anomaly Detection
MASTAF: A Model-Agnostic Spatio-Temporal Attention Fusion Network for Few-shot Video Classification
MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
PreViTS: Contrastive Pretraining with Video Tracking Supervision
Adaptive Token Sampling For Efficient Vision Transformers
MorphMLP: An Efficient MLP-Like Backbone for Spatial-Temporal Representation Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.