#01Nov 10, 2026
cs.CV
RS-JEPA: Remote Sensing Joint-Embedding Predictive Architecture with Scan-Path Views for Cross-Sensor Image Retrieval
Tanishq Rachamalla, Srishti Kaushik, Muhammad Attique Khan and 2 more
Cross-sensor remote sensing image retrieval aims to find scenes with the same content across heterogeneous sensors, such as SAR and optical or panchromatic (PAN) and multispectral (MS) imagery, without manual labels. Existing self-supervised methods either reconstruct sensor-specific pixel detail, align each image only with its co-registered twin, or rely on large transformers with 118-173M parameters (and up to 398M). We introduce RS-JEPA, a compact joint-embedding predictive framework built on a Vision Mamba encoder shared by both sensors. Since a state-space encoder reads an image as an ordered sequence of patches, we propose scan-path views, which treat two reading orders of the same image as two views with identical pixels and predict the hidden patches in one order from the visible patches in the other.To keep this prediction challenging, we propose Adversarial View Coupling (AVC), which learns the hardest split of the visible patches, and a neighbourhood consensus loss that aligns the scene similarities of both sensors. Extensive experiments on SAR-optical (BEN-14K, CBRSIR-VS) and PAN-MS (DSRSID) benchmarks demonstrate that RS-JEPA achieves the highest F1@5 among twelve re-implemented methods (72.92, 80.35 and 94.43) with a retrieval network of only 3.88M parameters (14.57M in training, 8-16times fewer than prior JEPA retrievers). Like prior methods, RS-JEPA trains on co-registered pairs. Our results highlight reading order as a source of self-supervised views. code: https://github.com/Tanishq251/RSJEPA.
#02Nov 10, 2026
cs.CV
A Lightweight Differentiable Method for Real-Time Skin Rendering and Digitization
Mario Marquez-Balduque, Carlos Rodriguez-Pardo, Jorge Lopez-Moreno
Inverse rendering for skin typically relies on offline path tracing to estimate physical properties, which fail to map directly to the approximate shaders utilized in interactive applications, such as game engines. We bridge this gap by directly estimating subsurface scattering parameters tailored for real-time rendering pipelines. We introduce a differentiable Screen-Space Subsurface Scattering (SSSS) framework that enables the joint optimization of spatially varying base textures and scattering distances. To account for local variations in skin thickness, we estimate these parameters across distinct facial regions, generating a blended profile map ready for production runtime engines. Our pipeline utilizes a low-cost, single-light, single-view capture setup designed for accessible and practical appearance acquisition. Optimization is driven by a masked loss function targeting regions where subsurface scattering effects are dominant. We validate our approach on a real-world dataset of $45$ individuals across the Monk Skin Tone Scale, ensuring demographic generalization. Furthermore, we complement this evaluation with synthetic data, a multi-light setup validating our single-light assumption, and a tilted-view configuration confirming angular consistency. Results demonstrate that our method effectively recovers scattering parameters from lightweight inputs, yielding realistic skin appearance directly compatible with standard real-time shaders.
#03Nov 10, 2026
cs.CV
Toward Foundation Detection Models via Example-Based Grounding in EM-DETR
Sheethal Bhat, Bogdan Georgescu, Awais Mansoor and 3 more
DeepLearning (DL)-based Computer-Aided Diagnostic (CAD) systems have shown great promise in supporting clinicians, but their performance heavily depends on the availability of large, well-annotated datasets. In medical lesion detection, the scarcity of comprehensive, stan dardized annotations and the heterogeneity of existing datasets limit the development of robust foundation models. In this work, we introduce a novel approach to building a multi-domain foundation model for le sion detection by integrating diverse medical image datasets annotated for both anatomical structures and abnormalities. We first propose a student-teacher training strategy to effectively combine datasets with heterogeneous label spaces, mitigating catastrophic forgetting, and im proving feature learning. In addition, building on our previously pro posed example-based EM-DETR framework, we adapt it to jointly learn from multiple domain-specific datasets, enabling fine-grained, example driven detection across modalities. Our model achieves state-of-the-art (SOTA) results, with image-level AUC scores exceeding previous SOTA by 1% point on four key findings in CXRs. We achieve a leading mAP50 in CXRs and mammography, surpassing EM-DETR trained on a single dataset by 2-3% points. This approach paves the way for scalable foundation models in medical imaging that leverage heterogeneous data while maintaining robust and generalizable performance.
#04Nov 10, 2026
cs.RO
Elastic-WAM: Adaptive Spatiotemporal Imagination for Efficient Robot Control
Haitian Li, Tianfu Li, Xuxi Zhao and 8 more
World-Action Models improve robot control by predicting future visual dynamics, but iterative video denoising incurs substantial inference overhead. Recent approaches reduce this cost through partial future denoising, yet typically rely on fixed refinement budgets. Our empirical observations reveal that preferred refinement depths vary across tasks and states, while different future regions exhibit varying relevance to action generation. Motivated by these findings, we propose Elastic-WAM, which adaptively allocates world refinement computation across temporal denoising time and spatial future space. A unified spatiotemporal refinement demand field governs when to stop world refinement, how far to advance each denoising step, and which regions to refresh. We parameterize this field with a shared temporal basis and spatial amplitudes, and learn it through a three-stage action-grounded supervised procedure. At inference, the learned field guides recurrent sparse world refinement to improve task success while reducing inference overhead. Evaluations across diverse simulation benchmarks and real-world long-horizon manipulation demonstrate state-of-the-art control performance with reduced inference latency.
#05Nov 10, 2026
cs.CV
ViGA-FLAME: Aligning Latent Visual and Geometric Representations for Robust 3D Head Expression Estimation
Wen Hao He, Jiacheng Wu, Jie Chen
Estimating 3D head expression from a single image requires mapping visual appearance to underlying facial geometry. We propose ViGA-FLAME, a two-stage framework that aligns semantic visual features with a structured geometric latent space. First, a Parametric-Geometric Variational Autoencoder (PG-VAE) learns a unified representation of canonical head surfaces and FLAME parameters, supporting both detailed surface reconstruction and interpretable parameter recovery. Supervision on geometry and parameters, together with consistency between the two decoding branches, connects surface detail with parametric structure. Second, a trainable Visual-Geometric Adapter (ViGA) maps features from a pretrained semantic visual encoder into this latent space. Asymmetric attention enables ViGA to align semantic image features from the visual encoder with the structured geometric information represented in the PG-VAE latent space, without altering the original visual stream. By grounding image evidence in this latent prior, ViGA-FLAME enables robust 3D head expression estimation across severe facial deformations and large viewing angles, efficiently improving robustness over competing feed-forward methods.