Chuang Yu

Chuang Yu, Baris Serhan, Angelo Cangelosi

Theory of Mind (ToM) is a fundamental cognitive architecture that endows humans with the ability to attribute mental states to others. Humans infer the desires, beliefs, and intentions of others by observing their behavior and, in turn, adjust their actions to facilitate better interpersonal communication and team collaboration. In this paper, we investigated trust-aware robot policy with the theory of mind in a multiagent setting where a human collaborates with a robot against another human opponent. We show that by only focusing on team performance, the robot may resort to the reverse psychology trick, which poses a significant threat to trust maintenance. The human's trust in the robot will collapse when they discover deceptive behavior by the robot. To mitigate this problem, we adopt the robot theory of mind model to infer the human's trust beliefs, including true belief and false belief (an essential element of ToM). We designed a dynamic trust-aware reward function based on different trust beliefs to guide the robot policy learning, which aims to balance between avoiding human trust collapse due to robot reverse psychology. The experimental results demonstrate the importance of the ToM-based robot policy for human-robot trust and the effectiveness of our robot ToM-based robot policy in multiagent interaction settings.

Xingrui Gu, Chuyi Jiang, Erte Wang et al.

Traditional machine learning methods for movement recognition often struggle with limited model interpretability and a lack of insight into human movement dynamics. This study introduces a novel representation learning framework based on causal inference to address these challenges. Our two-stage approach combines the Peter-Clark (PC) algorithm and Kullback-Leibler (KL) divergence to identify and quantify causal relationships between human joints. By capturing joint interactions, the proposed causal Graph Convolutional Network (GCN) produces interpretable and robust representations. Experimental results on the EmoPain dataset demonstrate that the causal GCN outperforms traditional GCNs in accuracy, F1 score, and recall, particularly in detecting protective behaviors. This work contributes to advancing human motion analysis and lays a foundation for adaptive and intelligent healthcare solutions.

Chuang Yu, Jinmiao Zhao, Mingxuan Zhao et al.

Recently, multimodal large language models (MLLMs) have been widely applied to reasoning tasks. However, they suffer from limited multi-rationale semantic modeling, insufficient logical robustness, and are susceptible to misleading interpretations in complex scenarios. Therefore, we propose a Multi-rationale INtegrated Discriminative (MIND) reasoning framework, which is designed to endow MLLMs with human-like cognitive abilities of "Understand -> Rethink -> Correct", and achieves a paradigm evolution from passive imitation-based reasoning to active discriminative reasoning. Specifically, we introduce a Rationale Augmentation and Discrimination (RAD) paradigm, which automatically and efficiently expands existing datasets by generating diverse rationales, providing a unified and extensible data foundation. Meanwhile, we design a Progressive Two-stage Correction Learning (P2CL) strategy. The first phase enhances multi-rationale positive learning, while the second phase enables active logic discrimination and correction. In addition, to mitigate representation entanglement in the multi-rationale semantic space, we propose a Multi-rationale Contrastive Alignment (MCA) optimization strategy, which achieves semantic aggregation of correct reasoning and boundary separation of incorrect reasoning. Extensive experiments demonstrate that the proposed MIND reasoning framework achieves state-of-the-art (SOTA) performance on multiple public datasets covering scientific, commonsense, and mathematical scenarios. It provides a new perspective for advancing MLLMs towards higher levels of cognitive intelligence. Our code is available at https://github.com/YuChuang1205/MIND

Chuang Yu, Yunpeng Liu, Jinmiao Zhao et al.

Recently, cross-spectral image patch matching based on feature relation learning has attracted extensive attention. However, performance bottleneck problems have gradually emerged in existing methods. To address this challenge, we make the first attempt to explore a stable and efficient bridge between descriptor learning and metric learning, and construct a knowledge-guided learning network (KGL-Net), which achieves amazing performance improvements while abandoning complex network structures. Specifically, we find that there is feature extraction consistency between metric learning based on feature difference learning and descriptor learning based on Euclidean distance. This provides the foundation for bridge building. To ensure the stability and efficiency of the constructed bridge, on the one hand, we conduct an in-depth exploration of 20 combined network architectures. On the other hand, a feature-guided loss is constructed to achieve mutual guidance of features. In addition, unlike existing methods, we consider that the feature mapping ability of the metric branch should receive more attention. Therefore, a hard negative sample mining for metric learning (HNSM-M) strategy is constructed. To the best of our knowledge, this is the first time that hard negative sample mining for metric networks has been implemented and brings significant performance gains. Extensive experimental results show that our KGL-Net achieves SOTA performance in three different cross-spectral image patch matching scenarios. Our code are available at https://github.com/YuChuang1205/KGL-Net.

Chuang Yu, Yunpeng Liu, Jinmiao Zhao et al.

Recently, feature relation learning has drawn widespread attention in cross-spectral image patch matching. However, existing related research focuses on extracting diverse relations between image patch features and ignores sufficient intrinsic feature representations of individual image patches. Therefore, we propose an innovative relational representation learning idea that simultaneously focuses on sufficiently mining the intrinsic features of individual image patches and the relations between image patch features. Based on this, we construct a Relational Representation Learning Network (RRL-Net). Specifically, we innovatively construct an autoencoder to fully characterize the individual intrinsic features, and introduce a feature interaction learning (FIL) module to extract deep-level feature relations. To further fully mine individual intrinsic features, a lightweight multi-dimensional global-to-local attention (MGLA) module is constructed to enhance the global feature extraction of individual image patches and capture local dependencies within global features. By combining the MGLA module, we further explore the feature extraction network and construct an attention-based lightweight feature extraction (ALFE) network. In addition, we propose a multi-loss post-pruning (MLPP) optimization strategy, which greatly promotes network optimization while avoiding increases in parameters and inference time. Extensive experiments demonstrate that our RRL-Net achieves state-of-the-art (SOTA) performance on multiple public datasets. Our code are available at https://github.com/YuChuang1205/RRL-Net.