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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Technical Report: Disentangled Action Parsing Networks for Accurate Part-level Action Parsing
AdaPool: Exponential Adaptive Pooling for Information-Retaining Downsampling
A Closer Look at Few-Shot Video Classification: A New Baseline and Benchmark
Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric Transformations
Predicting Driver Self-Reported Stress by Analyzing the Road Scene
Overview of Tencent Multi-modal Ads Video Understanding Challenge
Goal-driven text descriptions for images
A Unified Taxonomy and Multimodal Dataset for Events in Invasion Games
Hand Hygiene Video Classification Based on Deep Learning
Hand Pose Classification Based on Neural Networks
Two-stream Convolutional Networks for Multi-frame Face Anti-spoofing
Token Shift Transformer for Video Classification
Temporal Alignment Prediction for Few-Shot Video Classification
Fine-Grained AutoAugmentation for Multi-Label Classification
Aligning Correlation Information for Domain Adaptation in Action Recognition
When Video Classification Meets Incremental Classes
Attention Bottlenecks for Multimodal Fusion
Video Swin Transformer
TNT: Text-Conditioned Network with Transductive Inference for Few-Shot Video Classification
Self-supervised Video Representation Learning with Cross-Stream Prototypical Contrasting
CT-Net: Channel Tensorization Network for Video Classification
A Study On the Effects of Pre-processing On Spatio-temporal Action Recognition Using Spiking Neural Networks Trained with STDP
SSCAP: Self-supervised Co-occurrence Action Parsing for Unsupervised Temporal Action Segmentation
A Spatio-temporal Attention-based Model for Infant Movement Assessment from Videos
IntFormer: Predicting pedestrian intention with the aid of the Transformer architecture
Learning Implicit Temporal Alignment for Few-shot Video Classification
Home Action Genome: Cooperative Compositional Action Understanding
VidTr: Video Transformer Without Convolutions
Beyond Short Clips: End-to-End Video-Level Learning with Collaborative Memories
On the Pitfalls of Learning with Limited Data: A Facial Expression Recognition Case Study
Classifying Video based on Automatic Content Detection Overview
Busy-Quiet Video Disentangling for Video Classification
ViViT: A Video Vision Transformer
Revisiting ResNets: Improved Training and Scaling Strategies
Improved Techniques for Quantizing Deep Networks with Adaptive Bit-Widths
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.