#01Nov 10, 2026
cs.CV
AutoThreshold: Joint Confusion-Profile Learning for Adaptive Mask Binarization
Shaokai Wu, Xunchu Zhou, Pinzhi Wang and 5 more
A single validation-tuned threshold can become brittle when dense-score distributions shift. We introduce AutoThreshold, a post-hoc policy that predicts the joint mass of positive and negative pixels over score bins. This target directly parameterizes threshold-indexed confusion counts, avoiding products of independently estimated prevalence and class-conditional rates. Softmax normalization and cumulative decoding yield coherent, monotone confusion profiles; a plug-in decoder then selects a threshold for any $F_β$ objective without retraining the head. Across three independently trained Kvasir pipelines transferred to four polyp datasets, Joint exceeds continuous Otsu by $0.0080 \pm 0.0046$ external macro Dice, with positive gains in 3/3 runs. On the reference pipeline, three head seeds achieve $0.6324 \pm 0.0027$, 0.0085 above Otsu; direct-utility Ridge achieves 0.6342, with the paired difference interval spanning zero. The same Joint checkpoints outperform Otsu for $F_{0.5}$, Dice, and $F_2$. On developmental KITTI Road leave-one-corruption-family-out evaluation, Joint improves over scalar regression by 0.0082 [0.0064, 0.0101], while direct utility is 0.0022 higher. These results establish joint confusion prediction as a useful, metric-retargetable inductive bias, without claiming Bayes-optimal $F_β$ decisions.
#02Nov 10, 2026
cs.CV
Sim(3)-TurboReg: Robust and Efficient Scale-Aware Registration via Tri-Scale Consistency
Zijun Zhou, Shaocheng Yan, Zhenjun Zhao and 5 more
Feed-forward 3D foundation models recover geometry at high fidelity, yet inference memory constraints keep them from processing long sequences natively. Current streaming systems work around this limit with sliding-window chunking, which introduces severe monocular scale ambiguity. Resolving this ambiguity requires Sim(3) registration that stays accurate and efficient under outlier contamination. We introduce Sim(3)-TurboReg, which reformulates the compatibility graph construction of TurboReg by replacing absolute distance constraints with a strict Tri-Scale Consistency (TSC) criterion. Our analysis shows that TSC suppresses both mismatching and coordinate-distortion outliers. We integrate the resulting solver into existing streaming systems and obtain high-fidelity, kilometer-scale reconstruction for feed-forward models. On controlled unknown-scale registration at 90% outliers, ours matches RANSAC accuracy with 92.5% lower median latency. In DA3-Streaming on KITTI, ours improves mean ATE RMSE by 22.2% and reduces mean end-to-end time by 16.6%. Code: https://github.com/ivan-zzj/Sim3-TurboReg
#03Nov 10, 2026
cs.CV
CroCoMAE: Cross-modal self-supervised collaborative representation learning for SAR-optical remote sensing data
Vito Recchia, Giuseppe Guarino, Cássio Fraga Dantas and 3 more
Remote sensing increasingly captures the Earth's surface through complementary sensors such as SAR and optical, yet ground-truth annotations remain scarce, limiting supervised approaches. Self-supervised learning (SSL) addresses the label scarcity, and cross-modal SSL further exploits the complementarity between co-located sensors, but existing cross-modal frameworks align modalities mostly through global image-level representations or treat them as largely redundant, leaving their fine-grained complementary structure underexploited. We propose CroCoMAE, a cross-modal SSL framework for SAR-optical remote sensing that unifies contrastive learning and masked image modeling within a ViT-based architecture, operating at two complementary levels. At the image level, an intra-modality contrastive loss on the class token yields globally discriminative, augmentation-invariant representations. At the patch level, masked image modeling drives fine-grained per-modality representations, while a cross-modal contrastive objective aligns a shared subspace across SAR and optical patch tokens, leaving the complementary subspace unconstrained to retain modality-specific information. On two SAR-optical benchmarks covering scene classification and semantic segmentation, CroCoMAE improves over uni-modal SSL baselines and outperforms state-of-the-art cross-modal frameworks in most configurations, with gains in both single-modality and multi-modal evaluation across varying label regimes and the largest improvements on semantic segmentation.
#04Nov 10, 2026
cs.CV
SpecValley: Aggregation as Spectral Valley in Dense Heterophilic Graphs
Christoph Jahn, Delvin Bacho, Urs Waldmann and 1 more
Node classification in heterophilic graphs remains an open problem: aggregating over neighbourhoods can damp precisely the part of the signal that carries the labels. The field has answered with ever more expressive propagation operators, leaving the aggregation step that precedes them fixed by degree normalisation. However, degree normalisation acts as a spectral valley in its own right, attenuating a fixed middle band whose position at lambda equal 1 is set by the normalisation rather than by the data, exactly where the eigenvalues of dense graphs accumulate. We therefore begin with the representations rather than the propagation, in two stages: a learnable aggregation followed by an ordinary spectral filter. The first, SpecValley learns the position, sharpness and flank gains of the valley for every graph. We show formally how the aggregation operator and downstream filter interact. We establish that the response 1 minus lambda of degree normalisation vanishes exactly at that accumulation point. A spectral energy budget shows that what aggregation removes from a band, no linear spectral filter can recover. Crowding alone says nothing about the benefit. That is decided by how much label energy the valley removes, which we measure for every graph. SpecValley achieves strong results across heterophilic benchmarks, and a controlled ablation over aggregation operators and downstream filters attributes the gain to aggregation rather than the filter.
#05Nov 10, 2026
cs.AI
Multimodal Abstention That Preserves Acquired Correct Answers
Jinyuan Zhang
Adding abstention to a task-adapted multimodal model can discard correct answers, including capabilities acquired during adaptation. We formulate abstention repair as maximizing correct abstention on unanswerable questions under a budget on newly lost correct answers. We propose ReFrain (Regression-Free Abstention), which combines a linear probe on first-token logits, threshold calibration on held-out image clusters, and a deterministic veto that replaces rejected outputs with the displayed "none of the above" option while preserving all other responses. "Regression-free" denotes zero loss on the calibration set at the zero-budget target. All abstention methods in our main comparison start from the same answering-SFT checkpoint; we separately track lost correct answers, repaired errors, and retained SFT gains. On Qwen2.5-VL-3B, ReFrain at zero budget retains all 57 SFT-gained answers with no losses and improves correct abstention by 7.30 percentage points. At a 0.5% calibration budget, it reaches 74.05% correct abstention with 8 losses and retains 55 gains; CLIP-UP EmbLoRA reaches 64.65% with 62 losses, 41 repairs, and 40 retained gains. The same probe at its default threshold loses 298 correct answers. With models and thresholds frozen, confirmation on 228 independent question clusters yields 66.05% correct abstention and no losses at the 0.5% point, exceeding EmbLoRA by 32.72 percentage points. Ordinary refusal SFT achieves higher correct abstention at greater regression cost. Code and reproduction materials: https://github.com/ModeLiberaL/ReFrain.