arXiv subjects

Mingshuo Xu

Publications and source records attributed to Mingshuo Xu.

3 recordsLinked to original sources

arXiv2026

MITE-Net: SWaP-Optimized 4K Video Tiny Target Perception for Embodied Edge SAR

Real-time tiny target perception in high-resolution imagery is critical for embodied Search-and-Rescue (SAR) missions. However, strict Size, Weight, and Power (SWaP) constraints on edge devices like UAVs create a bottleneck: traditional image downsampling causes severe feature loss, while slice-based processing incurs prohibitive latency. To address this gap, this paper introduces a comprehensive framework encompassing a novel architecture, specialized datasets, and hardware-level benchmarks. First, we propose MITE-Net, a SWaP-optimized cascaded architecture, which couples a bio-inspired, learning-free Tiny Target Motion-Based Region Proposal Network (TTM-RPN) with a sub-0.14M-parameter R-CNN-like head. Second, to standardize 4K tiny target evaluation, we construct the SAR-Tiny Datasets by relabeling two challenging UAV datasets: SeaDroneSee-Tiny (dynamic maritime scenes, tiny targets predominantly of 64-256 pixels ) and UAVID-Tiny (cluttered urban scenes, extremely tiny targets, less than 64 pixels). Third, we benchmark against state-of-the-art YOLO models on an edge device, NVIDIA Jetson AGX Xavier, where MITE-Net directly processes 4K maritime imagery, achieving a 100\% search success rate at 30.33 FPS. Consuming merely 3.19 W (9.51 FPS/W), MITE-Net vastly outperforms YOLO baselines in target recall and energy efficiency. Conversely, UAVID-Tiny evaluations expose a compound structural limitation: the learning-free bionic front-end struggles against urban backgrounds, while the ultra-lightweight head lacks representational capacity for complex features. Ultimately, this work delivers an efficient onboard perception paradigm and a rigorous baseline guiding future end-to-end SAR architectures.

cs.CV↗

arXiv2026

Embodied Interpretability: Linking Causal Understanding to Generalization in Vision-Language-Action Models

Vision-Language-Action (VLA) policies often fail under distribution shift, suggesting that decisions may depend on spurious visual correlations rather than task-relevant causes. We formulate visual-action attribution as an interventional estimation problem. Accordingly, we introduce the Interventional Significance Score (ISS), an interventional masking procedure for estimating the causal influence of visual regions on action predictions, and the Nuisance Mass Ratio (NMR), a scalar measure of attribution to task-irrelevant features. We analyze the statistical properties of ISS and show that it admits unbiased estimation, and we characterize conditions under which action prediction error provides a valid proxy for causal influence. Experiments across diverse manipulation tasks indicate that NMR predicts generalization behavior and that ISS yields more faithful explanations than existing interpretability methods. These results suggest that interventional attribution provides a simple diagnostic approach for identifying causal misalignment in embodied policies.

cs.RO↗

arXiv2026

vSTMD: Visual Motion Detection for Extremely Tiny Target at Various Velocities

Visual motion detection for extremely tiny (ET-) targets is challenging, due to their category-independent nature and the scarcity of visual cues, which often incapacitate mainstream feature-based models. Natural architectures with rich interpretability offer a promising alternative, where STMD architectures derived from insect visual STMD (Small Target Motion Detector) pathways have demonstrated their effectiveness. However, previous STMD models are constrained to a narrow velocity range, hindering their efficacy in real-world scenarios where targets exhibit diverse and unstable dynamics. To address this limitation, we present vSTMD, a learning-free model for motion detection of ET-targets at various velocities. Our key innovations include: (1) a cross-Inhibition Dynamic Potential (cIDP) that serves as a self-adaptive mechanism efficiently capturing motion cues across a wide velocity spectrum, and (2) the first Collaborative Directional Gradient Calculation (CDGC) strategy, which enhances orienting accuracy and robustness while reducing computational overhead to one-eighth of previously isolated strategies. Evaluated on the real-world dataset RIST, the proposed vSTMD and its feedback-facilitated variant vSTMD-F achieve relative $F_{1}$ gains of $30\%$ and $58\%$ over state-of-the-art (SOTA) STMD approaches, respectively. Furthermore, both models demonstrate competitive orientation estimation performance compared to SOTA deep learning-driven methods. Experiments also reveal the superiority of the natural architecture for ET-object motion detection - vSTMD is $60\times$ faster than contemporary data-driven methods, making it highly suitable for real-time applications in dynamic scenarios and complex backgrounds. Code is available at https://github.com/MingshuoXu/vSTMD.

cs.CV↗