#01Nov 2, 2026
cs.CV
Align Once to Explain: Feature Alignment for Scalable B-cosification of Foundational Vision Transformers
Raphael Maser, Siddhartha Gairola, Sukrut Rao and 1 more
Foundational vision models have become the de facto standard for many vision tasks due to their strong performance. However, they are notoriously opaque and remain hard to interpret. We present ALOE (ALign Once to Explain), a one-time, label-free feature alignment approach that efficiently converts foundational vision models into inherently interpretable B-cos variants. Once aligned, the B-cos backbone is used as a drop-in replacement across several downstream tasks-amortizing the cost of interpretability. ALOE is robust across pre-training paradigms (supervised, self-supervised, vision-language) and can preserve teacher-level generalization with orders of magnitude less data than training from scratch. On classification, it strongly outperforms vanilla B-cosification (e.g., +10.5 p.p. top-1 on ImageNet for DINOv3 ViT-B/16), retains strong linear probing, k-NN, and zero-shot transfer competitive with foundational backbones (DINOv3, SigLIP2) across diverse downstream datasets. It also preserves spatially structured features useful for dense prediction, while yielding well-localized and highly human-interpretable explanations by design.
#02Nov 2, 2026
cs.CV
Bg-Mix: On Using Image Backgrounds for Data Augmentation in Electron Microscopy Images
Ankur Srivastava, Joana Sarah Grah, Katharina Ladewig and 1 more
Electron microscopy (EM) serves as a cornerstone in virology enabling the visualization of viral particles and is widely used in research, diagnostics and morphological characterization of pathogens. Computer-assisted image analysis can help facilitate and speed up this labor-intensive method, however, typical EM virus datasets are small and sparsely labeled, resulting in a lack of readily available deep learning models due to training limitations. To overcome data shortages and leverage content-rich, diverse sample backgrounds, we employed background-based data augmentation. To this end, we propose Bg-Mix, a form of data augmentation method, which decomposes training images into foreground objects and backgrounds and subsequently rearranges both into novel augmented images. This not only enhances the performance of object detection models but also increases the diversity of the training data. Using Bg-Mix provides an increase between +0.8 and +3.3 box mean average precision (mAP) points over the baseline when tested across two datasets using two architectures, thereby demonstrating its effectiveness.
#03Nov 2, 2026
cs.CV
Progressive Multi-Scale Super-Resolution via Cascaded Bicubic Anchors and Inter-Scale Mutual Refinement
Chun-An Lin, Yu-Chia Chang, Chi-Mao Fan and 2 more
Super-resolution networks are trained for one magnification factor, so several output resolutions require several models. We propose PMS-SR, a progressive single-input multiple-output network that produces x2, x4, and x8 results in one forward pass, with a Transformer backbone on the low-resolution grid. In Stage 1, each scale is predicted as a residual over the detached bicubic upsampling of the previous scale's prediction. With Dirac and zero initialization, training starts exactly from the bicubic cascade, and each head adds only the band the previous scale lacks. In Stage 2, inter-scale mutual refinement couples the scales in both directions through space-to-depth feedback, deformable alignment, and per-pixel fusion, so x8 features refine the x2 output. Trained on DF2K, the 7.95M-parameter model achieves a higher PSNR than every compared multi-scale method at x4 and x8 on all five standard benchmarks. At x8, it outperforms the x8-specific DRN-L on all of them, leads all compared methods on Urban100, and is within 0.13 dB of the best result elsewhere. Against three single-scale SwinIR-M models, PMS-SR uses 22% of the parameters, runs 2.9x faster on BSD100 inputs, and its outputs agree 6.7-12.4 dB more closely. Code and pretrained models will be released at https://github.com/julian135707/PMS-SR upon acceptance.
#04Nov 2, 2026
cs.CV
R$^2$-Omni: Reducing Structural and Precision Redundancy in Omni-LLMs for Long-Form Generation
Bolong Feng, Pengfei Zhou, Zhenglin Wan and 2 more
Omni-modal large language models (Omni-LLMs) typically process large numbers of multimodal tokens from audio-visual inputs, incurring substantial computational costs and a large memory footprint for the key-value (KV) cache. Aggressive token pruning can alleviate this problem while maintaining competitive performance on multiple-choice tasks, which require only a single option as output. However, we observe substantial quality degradation when generating long-form, open-ended responses. We attribute this gap to query-guided selection, which can retain sufficient evidence for answer selection while discarding details needed for comprehensive generation. This motivates reducing overlapping information across tokens, termed structural redundancy, while preserving diverse content. However, the retained representations may still contain precision redundancy due to unnecessarily high numerical precision in their cached values. To address these two levels of redundancy in token representations, we introduce a training-free framework named R$^2$-Omni. First, Structure-Aware Token Consolidation (STC) consolidates redundant representations using spatiotemporal similarity in video and local continuity in audio. Then, Semantic-Sensitivity Guided Precision Allocation (SSPA) allocates value precision to the retained representations according to the estimated reduction in attention-output error under quantized keys, with query attention and audio-visual alignment providing a semantic prior. Extensive experiments on long-form generation benchmarks demonstrate an average 5.0$\times$ reduction in the memory footprint while preserving 97.98% of baseline generation quality and achieving a 1.48$\times$ end-to-end speedup, highlighting the benefits of jointly reducing structural and precision redundancy.
#05Nov 2, 2026
cs.CV
TaoMate-FVSR:Audio-Guided Facial Video Restoration
Xinyang Li, Zeran Xu, Qijun Gan and 3 more
AI-generated videos often depict people within detailed scenes while leaving their small facial regions poorly resolved. Visual super-resolution can sharpen these regions without resolving whether the recovered lips and teeth agree with the spoken audio. We present an audio-guided facial video restoration framework that addresses this ambiguity within a reference-guided video flow model. Source--noise mixing initializes reconstruction from a partially preserved observation, and structured reference injection separates appearance anchors from continuous motion context. Training combines restoration of degraded images and videos with correction of degraded dubbed sources under target audio. Appearance and synchronization supervision allow the model to recover facial detail and support visual dubbing when the audio is replaced. On talking-head restoration benchmarks, our method achieves the best paired perceptual similarity and synchronization scores among the compared restoration methods. Component comparisons examine audio conditioning, the reconstruction formulation, and reference injection. Native AIGC examples further illustrate the recovery of lip contours, dental structure, and surrounding facial texture in scene-level footage. injection. Native AIGC examples further illustrate the recovery of lip contours, dental structure, and surrounding facial texture in scene-level footage.