GM-Net: Learning Features with More Efficiency

Deep Convolutional Neural Networks (CNNs) are capable of learning unprecedentedly effective features from images. Some researchers have struggled to enhance the parameters' efficiency using grouped convolution. However, the relation between the optimal number of convolutional groups and the recognition performance remains an open problem. In this paper, we propose a series of Basic Units (BUs) and a two-level merging strategy to construct deep CNNs, referred to as a joint Grouped Merging Net (GM-Net), which can produce joint grouped and reused deep features while maintaining the feature discriminability for classification tasks. Our GM-Net architectures with the proposed BU_A (dense connection) and BU_B (straight mapping) lead to significant reduction in the number of network parameters and obtain performance improvement in image classification tasks. Extensive experiments are conducted to validate the superior performance of the GM-Net than the state-of-the-arts on the benchmark datasets, e.g., MNIST, CIFAR-10, CIFAR-100 and SVHN.Comment: 6 Pages, 5 figure

POSTER: A Pyramid Cross-Fusion Transformer Network for Facial Expression Recognition

Facial Expression Recognition (FER) has received increasing interest in the computer vision community. As a challenging task, there are three key issues especially prevalent in FER: inter-class similarity, intra-class discrepancy, and scale sensitivity. Existing methods typically address some of these issues, but do not tackle them all in a unified framework. Therefore, in this paper, we propose a two-stream Pyramid crOss-fuSion TransformER network (POSTER) that aims to holistically solve these issues. Specifically, we design a transformer-based cross-fusion paradigm that enables effective collaboration of facial landmark and direct image features to maximize proper attention to salient facial regions. Furthermore, POSTER employs a pyramid structure to promote scale invariance. Extensive experimental results demonstrate that our POSTER outperforms SOTA methods on RAF-DB with 92.05%, FERPlus with 91.62%, AffectNet (7 cls) with 67.31%, and AffectNet (8 cls) with 63.34%, respectively