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Extreme Low Resolution Activity Recognition with Spatial-Temporal Attention Transfer

2019-09-09 01:02:11
Yucai Bai, Giang Dai, Long Chen
arXiv_CV
               
Abstract

Extreme low-resolution(LR) activity recognition plays a vital role in privacy protection. In the meantime, remote target recognition is also critical, especially in surveillance cameras. In this problem, the information capacity of LR data is relatively rare. How to exploit high-resolution(HR) data for improving the accuracy of LR action recognition is a notable issue. In this work, we make full use of the HR information of separate spatial and temporal features to promote LR recognition by acquiring better attention. Experiments show that our proposed method can improve LR recognition accuracy up to 4.4\%. Moreover, related experiments are implemented in the well-known datasets (e.g. UCF101 and HMDB51). The results achieve state-of-the-art performance on 12*16 HMDB51.

Abstract (translated)

URL

https://arxiv.org/abs/1909.03580

PDF

https://arxiv.org/pdf/1909.03580.pdf

Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot