Titlebook: Image and Graphics; 12th International C Huchuan Lu,Wanli Ouyang,Min Xu Conference proceedings 2023 The Editor(s) (if applicable) and The A

WITH

GLM: A Model Based on?Global-Local Joint Learning for?Emotion Recognition from?Gaits Using Dual-Strepture global and local characteristics. To enhance the features and improve recognition accuracy, we further introduce an attention-based feature fusion module. Through experiments on benchmark datasets, our proposed model achieves high accuracy in recognizing emotions from gait data.

FADE

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Ar3dHands: A Dataset and?Baseline for?Real-Time 3D Hand Pose Estimation from?Binocular Distorted Imaes. Evaluation shows that our method can achieve state-of-the-art results on several datasets with lower mean 2D end point error and can realize real-time performance on embedded devices without GPUs.

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0302-9743 mage and Graphics, ICIG 2023, held in Nanjing, China, during September 22–24, 2023..The 166 papers presented in the proceedings set were carefully reviewed and selected from 409 submissions. They were organized in topical sections as follows: computer vision and pattern recognition; computer graphic

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Attention-Based Global-Local Graph Learning for?Dynamic Facial Expression Recognition information, we construct a local spatial-temporal graph (LSTG) by extracting intermediate CNN features of local regions based on landmark-guided attention and defining their geometric relationships. We utilize topology-learnable ST-GCNs to exploit the local dynamics and implicit relations. Finally

STYX

HQFS: High-Quality Feature Selection for?Accurate Change Detection& .. The former focuses on extracting features from images, while the latter leverages attention mechanisms and pyramid fusion techniques to generate accurate change predictions. Comprehensive experiments on three benchmark datasets demonstrate the superiority of our method over seven state-of-the-a

Confess

Video-Based Person Re-Identification with?Long Short-Term Representation Learningd-play and can be easily inserted into existing networks for efficient feature learning. As a result, they significantly improve the feature representation ability for V-ReID. Extensive experiments on three widely used benchmarks show that our proposed approach can deliver better performances than m

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