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Vision-Language-Action (VLA) models achieve strong performance in robotic manipulation, but reinforcement learning (RL) fine-tuning often degrades generalization under spatial distribution shifts. We analyze flow-matching VLA policies and identify the collapse of spatial inductive bias as a key factor limiting robust transfer. To address this, we propose SA-VLA, which explicitly grounds VLA policies in spatial structure by integrating implicit spatial representations, spatially-aware step-level dense rewards, and SCAN, a spatially-conditioned exploration strategy tailored for flow-matching policies. This principled alignment mitigates policy over-specialization and preserves zero-shot generalization to more complex tasks. Experiments on challenging multi-object and cluttered benchmarks demonstrate that SA-VLA enables stable RL fine-tuning and substantially more robust, transferable behaviors.

Matching images with significant scale differences remains a persistent challenge in photogrammetry and remote sensing. The scale discrepancy often degrades appearance consistency and introduces uncertainty in keypoint localization. While existing methods address scale variation through scale pyramids or scale-aware training, matching under significant scale differences remains an open challenge. To overcome this, we address the scale difference issue by detecting co-visible regions between image pairs and propose SCoDe (Scale-aware Co-visible region Detector), which both identifies co-visible regions and aligns their scales for highly robust, hierarchical point correspondence matching. Specifically, SCoDe employs a novel Scale Head Attention mechanism to map and correlate features across multiple scale subspaces, and uses a learnable query to aggregate scale-aware information of both images for co-visible region detection. In this way, correspondences can be established in a coarse-to-fine hierarchy, thereby mitigating semantic and localization uncertainties. Extensive experiments on three challenging datasets demonstrate that SCoDe outperforms state-of-the-art methods, improving the precision of a modern local feature matcher by 8.41%. Notably, SCoDe shows a clear advantage when handling images with drastic scale variations.

Vision-Language-Action models (VLAs) support generalist robotic control by enabling end-to-end decision policies directly from multi-modal inputs. As trained VLAs are increasingly shared and adapted, protecting model ownership becomes essential for secure deployment and responsible open-source usage. In this paper, we present SleeperVLA, the first backdoor-based ownership verification framework specifically designed for VLAs. SleeperVLA embeds a stealthy and harmless backdoor watermark into the protected model during training by injecting secret messages into embodied visual data. For post-release verification, we propose a swap-and-detect mechanism, in which a trigger-aware projector and an external classifier head are used to activate and detect the embedded backdoor based on prediction probabilities. Extensive experiments across multiple datasets, model architectures, and adaptation settings demonstrate that SleeperVLA enables reliable and unique ownership verification while preserving benign task performance. Further results show that the embedded watermark remains detectable under post-release model adaptation.

Reliable correspondence estimation is a long-standing problem in computer vision and a critical component of applications such as Structure from Motion, visual localization, and image registration. While recent learning-based approaches have substantially improved local feature descriptiveness, most methods still rely on implicit assumptions about shared visual content across views, leading to brittle behavior when spatial support, semantic context, or visibility patterns diverge between images. We propose SAMatcher, a novel feature matching framework that formulates correspondence estimation through explicit co-visibility modeling. Rather than directly establishing point-wise matching from local appearance, SAMatcher first predicts consistent region masks and bounding boxes within a shared cross-view semantic space, serve as structured priors to guide and regularize correspondence estimation. SAMatcher employs a symmetric cross-view interaction mechanism that treats paired images as interacting token sequences, enabling bidirectional semantic alignment and selective reinforcement of jointly supported regions. Based on this formulation, a reliability-aware supervision strategy jointly constrains region segmentation and geometric localization, enforcing cross-view consistency during training. Extensive experiments on challenging benchmarks demonstrate that SAMatcher significantly improves correspondence robustness under large-scale and viewpoint variations. Beyond quantitative gains, our results indicate that monocular visual foundation models can be systematically extended to multi-view correspondence estimation when co-visibility is explicitly modeled, offering new insights for fusion-based visual understanding.