Existing infrared physical adversarial attack methods struggle to balance strong attack performance and rapid deployment, with fixed iteration steps causing optimization redundancy. We propose an infrared adversarial patch optimization method based on a Gaussian heat diffusion model. By constructing aggregation regularization derived from Fourier's heat conduction law, we precisely guide digital-domain adversarial perturbations to form continuous aggregated shapes, improving physical realizability. We propose the number of connected regions as a compactness metric and, building upon it, design a dual-threshold early-stopping mechanism that further enhances optimization efficiency. Experiments on multiple infrared datasets demonstrate that our method outperforms mainstream approaches in both attack efficacy and optimization efficiency, and exhibits strong cross-model performance across detectors. Remarkably, physical experiments achieve the highest average attack success rate of 91.7% using only a single patch.

Visual localization is a fundamental machine learning problem. Absolute Pose Regression (APR) trains a scene-dependent model to efficiently map an input image to the camera pose in a pre-defined scene. However, many applications have continually changing environments, where inference data at novel poses or scene conditions (weather, geometry) appear after deployment. Training APR on a fixed dataset leads to overfitting, making it fail catastrophically on challenging novel data. This work proposes Continual Domain Expansion (ConDo), which continually collects unlabeled inference data to update the deployed APR. Instead of applying standard unsupervised domain adaptation methods which are ineffective for APR, ConDo effectively learns from unlabeled data by distilling knowledge from scene-agnostic localization methods. By sampling data uniformly from historical and newly collected data, ConDo can effectively expand the generalization domain of APR. Large-scale benchmarks with various scene types are constructed to evaluate models under practical (long-term) data changes. ConDo consistently and significantly outperforms baselines across architectures, scene types, and data changes. On challenging scenes (Fig.1), it reduces the localization error by >7x (14.8m vs 1.7m). Analysis shows the robustness of ConDo against compute budgets, replay buffer sizes and teacher prediction noise. Comparing to model re-training, ConDo achieves similar performance up to 25x faster.

LiDAR localization is a fundamental task in robotics and computer vision, which estimates the pose of a Li- DAR point cloud within a global map. Scene Coordinate Regression (SCR) has demonstrated state-of-the-art performance in this task. In SCR, a scene is represented as a neural network, which outputs the world coordinates for each point in the input point cloud. However, SCR treats all points equally during localization, ignoring the fact that not all objects are beneficial for localization. For exam-ple, dynamic objects and repeating structures often negatively impact SCR. To address this problem, we introduce LiSA, the first method that incorporates semantic aware-ness into SCR to boost the localization robustness and accuracy. To avoid extra computation or network parame-ters during inference, we distill the knowledge from a seg-mentation model to the original SCR network. Experi-ments show the superior performance of LiSA on standard LiDAR localization benchmarks compared to state-of-the- art methods. Applying knowledge distillation not only pre-serves high efficiency but also achieves higher localization accuracy than introducing extra semantic segmentation modules. We also analyze the benefit of semantic in-formation for LiDAR localization. Our code is released at https://github.com/Ybchun/LiSA.