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Video Recognition

Video Recognition is a process of obtaining, processing, and analysing data that it receives from a visual source, specifically video.

Papers

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Showing 251–300 of 307 papers

TitleStatusHype
Retro-Actions: Learning 'Close' by Time-Reversing 'Open' Videos—0
Explainable Deep Learning for Video Recognition Tasks: A Framework & Recommendations—0
Multi-Agent Reinforcement Learning Based Frame Sampling for Effective Untrimmed Video Recognition—0
Sequence Level Semantics Aggregation for Video Object DetectionCode0
testRNN: Coverage-guided Testing on Recurrent Neural NetworksCode0
Learning Spatio-Temporal Representation with Local and Global DiffusionCode0
Collaborative Spatiotemporal Feature Learning for Video Action RecognitionCode0
PA3D: Pose-Action 3D Machine for Video Recognition—0
Design Light-weight 3D Convolutional Networks for Video Recognition Temporal Residual, Fully Separable Block, and Fast Algorithm—0
Early Detection of Injuries in MLB Pitchers from Video—0
Black-box Adversarial Attacks on Video Recognition Models—0
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave ConvolutionCode0
Attention Distillation for Learning Video Representations—0
Micro-Batch Training with Batch-Channel Normalization and Weight StandardizationCode0
Demonstration of Vector Flow Imaging using Convolutional Neural Networks—0
Collaborative Spatio-temporal Feature Learning for Video Action RecognitionCode0
DistInit: Learning Video Representations Without a Single Labeled Video—0
A^2-Nets: Double Attention NetworksCode0
AdaFrame: Adaptive Frame Selection for Fast Video Recognition—0
Multi-Task Learning of Generalizable Representations for Video Action Recognition—0
Higher-order Network for Action Recognition—0
A^2-Nets: Double Attention Networks—0
Morph: Flexible Acceleration for 3D CNN-based Video Understanding—0
Non-local NetVLAD Encoding for Video Classification—0
Learning Effective RGB-D Representations for Scene Recognition—0
Multi Modal Convolutional Neural Networks for Brain Tumor Segmentation—0
Teaching Machines to Understand Baseball Games: Large-Scale Baseball Video Database for Multiple Video Understanding Tasks—0
Learning Discriminative Video Representations Using Adversarial Perturbations—0
Video Jigsaw: Unsupervised Learning of Spatiotemporal Context for Video Action Recognition—0
Multi-Fiber Networks for Video Recognition—0
Contrastive Video Representation Learning via Adversarial Perturbations—0
Correlation Net: Spatiotemporal multimodal deep learning for action recognition—0
Geometry Guided Convolutional Neural Networks for Self-Supervised Video Representation Learning—0
Excitation Dropout: Encouraging Plasticity in Deep Neural NetworksCode0
Fast Retinomorphic Event Stream for Video Recognition and Reinforcement Learning—0
Exploiting Images for Video Recognition with Hierarchical Generative Adversarial Networks—0
Recurrent Residual Module for Fast Inference in Videos—0
Enhance Visual Recognition under Adverse Conditions via Deep Networks—0
Maximum A Posteriori Estimation of Distances Between Deep Features in Still-to-Video Face Recognition—0
Revisiting the Effectiveness of Off-the-shelf Temporal Modeling Approaches for Large-scale Video Classification—0
Attention Transfer from Web Images for Video Recognition—0
Temporal Modeling Approaches for Large-scale Youtube-8M Video UnderstandingCode0
Large-scale Video Classification guided by Batch Normalized LSTM Translator—0
Modeling Sub-Event Dynamics in First-Person Action Recognition—0
Alignment Distances on Systems of Bags—0
Adaptive Detrending to Accelerate Convolutional Gated Recurrent Unit Training for Contextual Video Recognition—0
Flow-Guided Feature Aggregation for Video Object DetectionCode0
Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification—0
Image and Video Mining through Online Learning—0
Generating Videos with Scene Dynamics—0
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