#01Aug 4, 2026
cs.CV
GeoMAR: Unleashing Geometrically Aligned Features for Masked Autoregressive Blind Face Restoration
Lu Gan, Hanyu Yan, Chaofeng Chen and 2 more
Codebook-based blind face restoration (BFR) often suffers from ambiguous conditioning features and a fragile prediction mechanism under severe degradation. To address these challenges, we propose GeoMAR, a framework designed to unleash geometrically aligned features with masked autoregressive (MAR) refinement for robust face restoration. For feature conditioning, we introduce a dual-input extraction pipeline to extract component-based geometric descriptions with explicit, spatially faithful anchors. These textual priors are integrated with low-quality (LQ) features via an Aligned Geometric Priors Injector, which employs a KV-Q exchange strategy to generate geometrically aligned features. For prediction mechanism, we reformulate the one-step mapping into a multi-step MAR process. This coarse-to-fine generation progressively refines complex facial regions based on increasingly reliable context. Experiments on one synthetic and three real-world benchmarks demonstrate that GeoMAR achieves highly competitive perceptual quality and coherent visual structures compared with existing methods. The code is available at https://github.com/BRL-SYSU/GeoMAR.git.
#02Aug 4, 2026
cs.CV
Progressive Learning of a Diffusion-based Inpainting Model for Separating Overlapped Fingerprints
Noor Hussein, Anil K. Jain, Karthik Nandakumar
Overlapped friction ridge patterns are a recurring problem in latent fingerprints recovered from crime scenes and in live-scan scenarios where residual fingerprints on the sensor may corrupt subsequent acquisitions. Existing approaches for separating overlapped fingerprints either rely on rule-based orientation field completion that requires strong domain knowledge or train end-to-end deep neural networks that do not account for domain-specific considerations. This work introduces a diffusion-based pipeline for separating component fingerprints from an image containing overlapping friction ridge patterns. We formulate the separation problem as an inpainting task and progressively learn a diffusion model for this task in multiple stages. Starting from a pre-trained Stable Diffusion model, we progressively incorporate a fingerprint prior, add the ability to complete partial fingerprints, and finally propose \textbf{overlap-aware inpainting} that reconstructs each component print using a diffusion inpainting model based on multi-channel conditioning. Experiments on two public datasets demonstrate that component fingerprints reconstructed using the proposed diffusion-based inpainting method can match with their mated counterparts with very high probability.
#03Aug 4, 2026
eess.IV
Unsupervised Adversarial Domain Adaptation for Uterine layer Segmentation: From Labeled Cine to Unlabeled Dynamic EPI MRI
Smiti Tripathy, Milauni Desai, Jordina Aviles Verdera and 1 more
Uterine peristalsis is a key physiological phenomenon responsible for various functions across the menstrual cycle, intimately linked to uterine wall microstructure. Alterations in uterine motion and tissue properties are implicated in the etiology of gynecological diseases, yet these processes have been studied in isolation. We introduce a dynamic multi-echo gradient echo EPI framework for simultaneous characterization and correlation of uterine peristaltic activity and time-resolved T2* changes at 0.55T. Inherent susceptibility artifacts, reduced resolution, and burden of manual uterine layer annotation are addressed by an unsupervised adversarial domain adaptation framework, transferring segmentation knowledge from labeled cine MRI to unlabeled dynamic EPI. We implemented Unet-LSTM with multi-scale domain discriminators that exploits temporal layer dynamics. A Dice score of 0.88 and Jaccard index of 0.80 was achieved. Mean T2* values were 108ms, 76ms, and 124ms for the myometrium, junctional zone, and endometrium. A negative correlation between junctional zone area and T2* was observed in 14/39 cases, providing first insights into oxygenation patterns associated with junctional zone contraction and motion, demonstrating feasibility of assessing the interplay between contractility and dynamic T2* changes.
#04Aug 4, 2026
cs.CV
ParVL: Parallel Scaling and Expandable Compute Allocation for Multimodal LLMs
Yang Yang, Qinyu Zhao, Mouxiang Chen and 5 more
Existing scaling strategies for Multimodal Large Language Models (MLLMs) typically expand either model parameters or sequential inference computation, incurring substantial memory or latency overhead. More importantly, most existing methods fail to alter the rigid, fixed computation allocation between the Vision Transformer and the Large Language Model components, limiting task-specific optimization. To address this, we introduce the Parallel Vision-Language (ParVL) scaling framework for MLLMs, which scales parallel computation by reusing the existing ViT and LLM backbone parameters across multiple vision and language branches. This framework raises a central question: given a fixed backbone parameter budget, how should additional shared-backbone computation be allocated between the vision and language modalities? We instantiate each parallel computational stream with branch-specific prefix parameters over a shared backbone, and train the entire model end-to-end via full-parameter supervised fine-tuning on roughly 13B tokens. We systematically study the computation-allocation trade-off between the ViT encoder and LLM decoder. ParVL improves overall multimodal performance over same-recipe single-branch baselines, and the best evaluated vision--language allocation varies across tasks. Code is available at https://github.com/YangYangGirl/ParVL.
#05Aug 4, 2026
cs.CV
StreamDAM: Presence-Aware Memory for Real-Time Streaming Video Object Segmentation
Xiang Chen
Quality-tier video object segmentation (VOS) trackers such as DAM4SAM top accuracy leaderboards, but they are measured offline, one frame at a time with no clock. Under an honest streaming protocol at 30 frames per second, where a frame that misses its budget is served the last mask already computed, the winner collapses: the rich memory that makes it accurate is too slow to keep up, and what it emits is blind to whether the object is even present. We trace both failures to one place, the tracker's memory pipeline, and rebuild it for streaming. \method{} makes the memory machinery itself run at frame rate through in-model optimization rather than a bolted-on fallback, and governs it with a single learned presence signal that decides what enters memory, how far back the tracker reads, when to withhold output, and when to re-detect. A mechanism analysis shows why a fixed policy cannot win: the control that helps when an object truly disappears is the one that hurts when it is merely hard to see, so the choice must be made per frame. Across four benchmarks and five modern baselines, \method{} is the strongest streaming tracker, recovers nearly all of the offline model's accuracy under the clock, and on the hardest content exceeds the offline model it is built from.