TMM Volume 25 | 2023

Image-text matching, as a fundamental cross-modal task, bridges the gap between vision and language. The core is to accurately learn semantic alignment to find relevant shared semantics in image and text. Existing methods typically attend to all fragments with word-region similarity greater than empirical threshold zero as relevant shared semantics, e.g. , via a ReLU operation that forces the negative to zero and maintains the positive.

Recent advances in unsupervised domain adaptation (UDA) techniques have witnessed great success in cross-domain computer vision tasks, enhancing the generalization ability of data-driven deep learning architectures by bridging the domain distribution gaps.

Despite the development of computer vision techniques, the micro-expression (ME) recognition task still remains a great challenge because MEs have very low intensity and short duration. However, the ME recognition is of great significance since it provides important clues for real affective states detection. This paper proposes a novel Block Division Convolutional Network (BDCNN) with the implicit deep features augmentation. 

Cross-domain Facial Expression Recognition (FER) aims to safely transfer the learned knowledge from labeled source data to unlabeled target data, which is challenging due to the subtle difference between various expressions and the large discrepancy between domains. Existing methods mainly focus on reducing the domain shift for transferable features but fail to learn discriminative representations for recognizing facial expression, which may result in negative transfer under cross-domain settings.

We introduce a Gaussian Mixture Model (GMM) framework for 3D holoscopic image compression in this paper. The elemental-images of the 3D holoscopic image are predicted using GMM and the parameters of GMM are estimated using the common Expectation-Maximization (EM) algorithm. GMM Model Optimization (GMO) is used in this framework to select the optimal number of distributions and avoid local optimum of EM at the same time.

Current approaches for human pose estimation in videos can be categorized into per-frame and warping-based methods. Both approaches have their pros and cons. For example, per-frame methods are generally more accurate, but they are often slow. Warping-based approaches are more efficient, but the performance is usually not good. To bridge the gap, in this paper, we propose a novel fast framework for human pose estimation to meet the real-time inference with controllable accuracy degradation in compressed video domain.