Chunxiao Wu, Huixin Huang, Jialei Pan, Qiang Liang, Tao Wu, Cheng Wu, Long Huang, Yuanji Li 等 12 位作者
Self-supervised pre-training on synthetic aperture radar (SAR) imagery enables transferable representations, but the optical-SAR domain gap and speckle perturbations limit representation stability. Most SAR foundation models use masked image modeling (MIM) through pixel reconstruction or structural feature prediction, which is sensitive to speckle or relies on predefined operators. Joint modeling of cross-view semantics and perturbation stability at corresponding spatial locations remains underexplored. We propose XuanJian-DINOSAR, a SAR foundation model based on global and local semantic consistency. We curate over 360K publicly available SAR target chips, detection images, and scene images spanning diverse targets, scenes, and sensors, and adapt an optically pre-trained DINOv2 model to SAR through self-supervised learning. The framework combines DINO global semantic self-distillation, iBOT local masked modeling, and KoLeo feature-distribution regularization to learn globally discriminative and locally structure-aware representations. We introduce a local semantic consistency branch tailored to SAR intensity statistics and coherent imaging characteristics. Shared geometric transformations produce spatially aligned teacher weak views and student strong views. Intensity transformations and Gamma multiplicative speckle perturbations are applied to student views, while a cosine consistency loss aligns patch tokens at corresponding locations, promoting stability under radiometric perturbations. This objective complements the cross-region and cross-scale semantic consistency of DINOv2 multi-crop training, jointly enforcing global and local semantic consistency. Experiments on multiple downstream classification and detection datasets demonstrate state-of-the-art performance on most evaluation metrics, validating the generality and transferability of the learned representations.
This paper considers online intrusion-risk assessment and graded warning for unmanned aerial vehicles under bounded measurement attacks. To address this problem, we develop a monitoring framework that combines trusted state estimation, attack-aware risk assessment, and probabilistic signal temporal logic (PrSTL). Trusted observations support recursive state estimation, while potentially attacked observations inform integrity testing and intrusion-risk assessment. The key contribution is a confidence-set construction that combines normalized innovation statistics with physical attack bounds to characterize admissible measurement biases. The resulting family of bias-corrected Gaussian posteriors yields an upper bound on the probability of occupying a no-fly zone or its surrounding safety margin. Confidence guarantees and finite-cover numerical certificates quantify the reliability of this risk envelope, which is evaluated with or without an integrity alarm. The PrSTL monitor analyzes the resulting risk sequence to distinguish ordered, persistent, and repeated risk and produces graded warnings with explicit robustness margins and supporting windows. We validate the monitor in flight experiments with a Crazyflie quadrotor tracked by a Vicon motion capture system. Controlled comparisons quantify the contributions of individual monitoring components. Prescribed risk profiles verify the temporal semantics and illustrate how distinct risk patterns produce different warning levels.
Chengfeng Han, Ang Wang, Baole Ai, Xianlu Bian, Jie Yao, Zilong Huang, Dandan Ding
Training-free sparse attention offers a practical acceleration solution to Diffusion Transformers (DiTs) via reducing computations without fine-tuning. It typically involves estimating the importance of query-key regions and deriving sparse masks to compute only the important candidates, which inevitably introduces approximation errors that may degrade generation quality. To better balance the efficiency-quality trade-off, we propose iCATS, integrating improved importance estimation and sparse mask construction with an efficient hardware execution strategy. Specifically, for importance estimation, unlike previous works that perform independent clustering over query and key tokens based on feature similarity to estimate attention scores, iCATS demonstrates that clustering based on query-key dot-product interactions is more accurate and further reformulates this objective as a simple quadratic form for low-cost computation. For sparse mask construction, instead of using a fixed top-p rule, we observe that tolerance to sparse approximation errors varies across denoising timesteps and therefore introduce an SNR-guided sparsity schedule to adjust sparsity dynamically, leading to higher accuracy. Finally, for hardware execution, we devise a tail-merging strategy to reduce padding overhead caused by irregular cluster sizes, improving GPU kernel utilization. Extensive experiments show that iCATS achieves $2.03\times$ acceleration with 31.017 dB PSNR on HunyuanVideo-T2V-13B and $1.55\times$ acceleration with 29.301 dB PSNR on Wan2.1-T2V-14B, delivering a state-of-the-art efficiency-quality trade-off.
Robot tasks require grounding spatial language in physical scenes, but task success alone cannot establish whether spatial instructions are followed. We present BEHAVIOR-Spatial, a benchmark built on BEHAVIOR-1K that measures spatial question answering, point-based target grounding, and robot action to examine how VLM spatial understanding relates to VLA behavior. It comprises 3,256 spatial questions across 49 activities, 1,240 grounding queries across 14 manipulation tasks, and action evaluations covering those 14 tasks and 10 navigation tasks. Our manipulation protocol evaluates held-out configurations and instruction following when the requested target changes within a fixed training layout. The baseline achieves 41.6% on held-out configurations but only 16.1% under changed instructions. VLM adaptation and robot-data pretraining provide limited improvements, whereas oracle visual target cues raise these rates to 60.3% and 38.0%, respectively. Perception evaluations reveal policy-dependent correlations between VLM question-answering and grounding accuracy and VLA manipulation performance. Together, these findings motivate evaluating spatial instruction following alongside task completion and examining when spatial perception performance translates into robot behavior. Project website: https://arielchen07.github.io/behavior-spatial/.
Yichuan Cao, Xiao-Shan Gao, Qiao-Long Huang, Jiawei Nie
In this paper, we prove that irreducibility testing for sparse polynomials in $\mathbb{Z}[X]$ is coNP-hard under deterministic polynomial-time many-one reductions. That is, there exists no polynomial-time irreducibility test algorithm unless $\mathrm P=\mathrm{NP}$. We also provide an upper bound for this problem by demonstrating that the same irreducibility testing problem lies in $\mathsf{PP}\subseteq\mathsf{PSPACE}$; in other words, it can be solved in probabilistic polynomial time and, moreover, within polynomial space. The fact that $\mathsf{SparseIrred}_{\mathbb{Z}}\in\mathsf{PP}$ relies on the quasi-Riemann hypothesis result of Cureton--Thorner and on the uniform effective prime-ideal bound by Frei--Sofos--Widmer just posted recently.
We introduce OvisOCR3, a 0.8B model for a broad range of OCR-related tasks. OvisOCR3 unifies document parsing, general OCR, key information extraction (KIE), document figure captioning, and cross-page table reconstruction in a single model. We train the model on real and synthetic data through continued pretraining and use domain-balanced mid-training to balance supervision across tasks with different data volumes and response lengths. Multi-teacher on-policy distillation transfers expertise from five domain-specific 4B teachers into the shared model, followed by mixed-task reinforcement learning with domain-specific rewards. We compare OvisOCR3 with general-purpose vision-language models and specialized OCR models on public and in-house benchmarks covering all five task families. In these comparisons, it achieves an overall score of 96.74 on OmniDocBench v1.6 and the highest overall scores on PureDocBench, Real5-OmniDocBench, and MDPBench. On OCRBench v2, it ranks first, scoring 65.9 and 61.3 on the English and Chinese splits, respectively. It also outperforms the baselines on OCRBench and all four KIE benchmarks. On DocFigCapBench, it achieves the highest localization F1 and a caption score close to that of Qwen3.8-Max. For cross-page tables, it leads in Relation F1 on CrossTabBench and truncated-table TEDS on MPDocBench-Parse. These results demonstrate that a compact 0.8B model can achieve strong performance across diverse OCR-related tasks. OvisOCR3 is available at https://huggingface.co/ATH-MaaS/OvisOCR3.