Micro-expression recognition (MER) is challenging due to the difficulty of capturing the instantaneous and subtle motion changes of micro-expressions (MEs). Early works based on hand-crafted features extracted from prior knowledge showed some promising results, but have recently been replaced by deep learning methods based on the attention mechanism. However, with limited ME sample sizes, features extracted by these methods lack discriminative ME representations, in yet-to-be improved MER performance. This paper proposes the Dual-branch Attention Network (Dual-ATME) for MER to address the problem of ineffective single-scale features representing MEs. Specifically, Dual-ATME consists of two components: Hand-crafted Attention Region Selection (HARS) and Automated Attention Region Selection (AARS). HARS uses prior knowledge to manually extract features from regions of interest (ROIs). Meanwhile, AARS is based on attention mechanisms and extracts hidden information from data automatically. Finally, through similarity comparison and feature fusion, the dual-scale features could be used to learn ME representations effectively. Experiments on spontaneous ME datasets (including CASME II, SAMM, SMIC) and their composite dataset, MEGC2019-CD, showed that Dual-ATME achieves better, or more competitive, performance than the state-of-the-art MER methods.

Facial expression recognition (FER) remains a challenging task due to the ambiguity and subtlety of expressions. To address this challenge, current FER methods predominantly prioritize visual cues while inadvertently neglecting the potential insights that can be gleaned from other modalities. Recently, vision-language pre-training (VLP) models integrated textual cues as guidance, culminating in a powerful multi-modal solution that has proven effective for a range of computer vision tasks. In this paper, we propose a Cross-Modal Emotion-Aware Prompting (CEPrompt) framework for FER based on VLP models. To make VLP models sensitive to expression-relevant visual discrepancies, we devise an Emotion Conception-guided Visual Adapter (EVA) to capture the category-specific appearance representations with emotion conception guidance. Moreover, knowledge distillation is employed to prevent the model from forgetting the pretrained category-invariant knowledge. In addition, we design a Conception-Appearance Tuner (CAT) to facilitate the interaction of multi-modal information via cooperatively tuning between emotion conception and appearance prompts. In this way, semantic information about emotion text conception is infused directly into facial appearance images, thereby enhancing a comprehensive and precise understanding of expression-related facial details. Quantitative and qualitative experiments show that our CEPrompt outperforms state-of-the-art approaches on three real-world FER datasets. The code is available at https://github.com/HaoliangZhou/CEPrompt.

Path planning is a core technology for mobile robots. However, existing state-of-the-art methods suffer from issues such as excessive path redundancy, too many turning points, and poor environmental adaptability. To address these challenges, this paper proposes a novel global and local fusion path-planning algorithm. For global path planning, we reduce path redundancy and excessive turning points by designing a new heuristic function and constructing an improved path generation method. For local path planning, we propose an environment-aware dynamic parameter adjustment strategy, incorporating deviation and avoidance dynamic obstacle evaluation factors, thus addressing issues of local optima and timely avoidance of dynamic obstacles. Finally, we fuse those global and local path-planning improvements to form our fusion path-planning algorithm, which can enhance the robot’s adaptability to complex scenarios while reducing path redundancy and turning points. Simulation experiments demonstrate that the improved fusion path-planning algorithm not only effectively addresses existing issues but also operates with higher efficiency.