Explanation-guided learning (EGL) has shown promise in aligning model predictions with interpretable reasoning, particularly in computer vision tasks. However, most approaches rely on external annotations or heuristic-based segmentation to supervise model explanations, which can be noisy, imprecise and difficult to scale. In this work, we provide both empirical and theoretical evidence that low-quality supervision signals can degrade model performance rather than improve it. In response, we propose ALIGN, a novel framework that jointly trains a classifier and a masker in an iterative manner. The masker learns to produce soft, task-relevant masks that highlight informative regions, while the classifier is optimized for both prediction accuracy and alignment between its saliency maps and the learned masks. By leveraging high-quality masks as guidance, ALIGN improves both interpretability and generalizability, showing its superiority across various settings. Experiments on the two domain generalization benchmarks, VLCS and Terra Incognita, show that ALIGN consistently outperforms six strong baselines in both in-distribution and out-of-distribution settings. Besides, ALIGN also yields superior explanation quality concerning sufficiency and comprehensiveness, highlighting its effectiveness in producing accurate and interpretable models.

Deep neural networks (DNNs) have achieved remarkable performance in many tasks, yet they often behave as opaque black boxes. Explanation-guided learning (EGL) methods steer DNNs using human-provided explanations or supervision on model attributions. These approaches improve interpretability but typically assume benign inputs and incur heavy annotation costs. In contrast, both predictions and saliency maps of DNNs could dramatically alter facing imperceptible perturbations or unseen patterns. Adversarial training (AT) can substantially improve robustness, but it does not guarantee that model decisions rely on semantically meaningful features. In response, we propose Explanation-Guided Adversarial Training (EGAT), a unified framework that integrates the strength of AT and EGL to simultaneously improve prediction performance, robustness, and explanation quality. EGAT generates adversarial examples on the fly while imposing explanation-based constraints on the model. By jointly optimizing classification performance, adversarial robustness, and attributional stability, EGAT is not only more resistant to unexpected cases, including adversarial attacks and out-of-distribution (OOD) scenarios, but also offer human-interpretable justifications for the decisions. We further formalize EGAT within the Probably Approximately Correct learning framework, demonstrating theoretically that it yields more stable predictions under unexpected situations compared to standard AT. Empirical evaluations on OOD benchmark datasets show that EGAT consistently outperforms competitive baselines in both clean accuracy and adversarial accuracy (+37%) while producing more semantically meaningful explanations, and requiring only a limited increase (+16%) in training time.

The microscopic cascade prediction task has wide applications in downstream areas like ''rumor detection''. Its goal is to forecast the diffusion routines of information cascade within networks. Existing works typically formulate it as a classification task, which fails to well align with the Social Homophily assumption, as it just use the features of ''infected'' users while neglecting those of ''uninfected'' users in representation learning. Moreover, these methods focus primarily on social relationships, thereby dismissing other vital dimensions like users' historical behavior and the underlying preferences behind it. To address these challenges, we introduce the MSR (Multifaceted Self-Retrieval) framework. During encoding, in addition to the existing social graph, we construct a preference graph to represent ''behavioral preferences'' and further propose a modified multi-channel GRAU for multi-view analysis of cascade phenomenon. For decoding, our approach diverges from classification-based methods by reformulating the task as an information retrieval problem that predicts the target user with similarity measures. Empirical evaluations on public datasets demonstrate that this framework significantly outperforms baselines on Hits@κ and MAP@κ, affirming its enhanced ability.

For commercially successful ground robots, high degree of autonomy, low manufacturing and maintenance cost, as well as minimized deployment limitations in different environments are essential attributes. To deliver an `anywhere deployable' product, it is impractical to rely on one single sensor or one single piece of algorithm to overcome all related challenges. Instead, the entire robotic system should be dedicated designed, including the choices of sensors, processors, algorithm integration for various functionality, and so on.This paper presents our design of perception system for commercial ground robots, which is able to operate in most common environments. The designed system is equipped with low-cost sensors and processors. The first key contribution of this paper is the design of the robotic sensory system, which includes a monocular camera, a 2D laser range finder (LRF), wheel encoders, and an inertial measurement unit (IMU). Our sensory system can be built at a cost of as low as $100. Furthermore, the selected sensors provide complementary characteristics for perception of both robot ego-motion and its surrounding environments, which are the prerequisites for `anywhere' deployment. The second key contribution of this paper is that a complete set of technologies is proposed based on our sensor systems, including sensor calibration (factory calibration and online calibration), localization (environmental exploring and re-localization), as well as mapping. The proposed methodology includes both efficient engineering implementation and theoretical novelty for high performance systems. Experimental results from our robotic testing platform and off-the-shelf commercial robots are presented. These results demonstrate that the proposed system can be deployed in various environmental conditions without performance compromise.