arXiv_CV
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DailyWildfireCAN: A Dataset and Benchmark for Next-Day Wildfire Spread Prediction in Canada
2026-11-10 15:29:13Mahacine Ettahri, Hager Radi Abdelwahed, Toluwaleke Olutayo, Daniel Aloise, Ga\'etan Marceau CaronAbstract
Wildfires represent a critical ecological and socio-economic challenge in Canada, where vast boreal forests and a warming climate have amplified the frequency and severity of extreme fire seasons. Predicting next-day wildfire spread can support fire management, but requires capturing complex interactions among weather, fuels, and terrain. Deep learning has emerged as a promising approach to learning these relationships from heterogeneous environmental data, yet its development and evaluation depend on representative datasets. Existing deep learning research on wildfire spread relies predominantly on US-focused datasets, leaving Canadian fire environments underrepresented. To address this gap, we present DailyWildfireCAN, a multi-modal, multi-temporal dataset and benchmark for predicting next-day wildfire spread across Canada. The dataset tracks continuous fire progression over multi-day events at a high spatial and temporal resolution of 90-m and 24-hour, respectively. This resolution preserves fine-scale information relevant to fire-edge geometry, local fuel variations, and terrain effects, supporting research toward improved daily wildfire spread forecasting. DailyWildfireCAN covers 2,440 wildfire events across eight fire seasons 2017-2024 and integrates 33 features across diverse modalities, including topography, meteorology, land cover, vegetation and forest structure, human influence indices, and satellite imagery. We establish a comprehensive benchmark comparing mono-temporal and multi-temporal prediction models, accompanied by a multi-dimensional evaluation framework assessing robustness across wildfire characteristics, geographic regions, and temporal conditions, spatial prediction quality, and sensitivity to reference label variations.
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URL
https://arxiv.org/abs/2611.14207
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SpecValley: Aggregation as Spectral Valley in Dense Heterophilic Graphs
2026-11-10 15:01:42Christoph Jahn, Delvin Bacho, Urs Waldmann, Bastian GoldlueckeAbstract
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.
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https://arxiv.org/abs/2611.14176
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GARD: Gradient-Guided Adversarial Representation Defense against Backdoors in Multimodal Contrastive Learning Models
2026-11-10 14:30:36Yuhong Wang, Yujun Cai, Lei Feng, Songze LiAbstract
Multimodal contrastive learning models such as CLIP are vulnerable to backdoor attacks with diverse trigger structures, ranging from localized patches to globally distributed perturbations, with recent attacks increasingly optimizing these triggers in the representation space. Existing post-hoc defenses based on clean fine-tuning may leave backdoors active, while trigger-inversion methods depend on accurately estimating the attack target and trigger. We observe that backdoored encoders exhibit structure-dependent representation sensitivity: global perturbations preferentially expose distributed backdoors, whereas local patch perturbations are more effective for localized backdoors. Based on this observation, we propose Gradient-Guided Adversarial Representation Defense (GARD), a target- and trigger-agnostic post-hoc defense. GARD optimizes complementary global and local adversarial probes to maximally displace clean-image representations, and then realigns the perturbed representations with detached clean anchors while preserving image-text alignment. The resulting model requires no additional computation at inference time. Across six visual backdoor attacks and two CLIP visual backbones, GARD reduces the attack success rate below 1% in all 12 evaluated attack-backbone combinations while maintaining competitive clean accuracy. These results demonstrate the effectiveness of adversarial representation probing for repairing backdoored multimodal contrastive encoders.
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URL
https://arxiv.org/abs/2611.14143
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HaiRuo: Forecasting Ocean Eddies as Fields and Objects with a Satellite-Altimetry Foundation Model
2026-11-10 14:24:07Xukun Qin, Haochen Sun, Yuyang Tian, Ge ChenAbstract
Mesoscale eddies carry most of the ocean's kinetic energy and move heat, salt and nutrients across basins, yet where an eddy will be two weeks ahead is still read off ocean models with post-hoc tracking heuristics. We introduce HaiRuo, which forecasts eddies as fields and as objects at once: from 30 days of satellite sea-level maps, one forward pass returns 15 days of sea level and geostrophic velocity, daily eddy masks and the trajectory of every eddy alive today. HaiRuo pairs two components with a clear division of labour: (1) HaiRuo-1B, an open-weight 1.05-billion-parameter foundation model pretrained without labels on 24 years of global satellite-altimetry observations, which supplies the 15-day physical fields and masks (+0.14 field skill and +1.1 mIoU points under the same decoder) and further lowers 15-day centre error by 1.2 km frozen (1.35 km over training from scratch); and (2) XuanJi, a Lagrangian object decoder that samples the evolving forecast in each eddy's co-moving polar frame and refines the 15-day path across three look-ahead rounds, removing 2.5 km on HaiRuo-1B (2.9 km with joint fine-tuning) and 2.5-3.3 km via its object attention across six published backbones. Under one pre-registered global evaluation over the whole ocean, HaiRuo improves on fourteen methods on every endpoint (25.2 vs. 30.6 km 15-day centre error, -18%), holding its margin outside the labelled regions and through both phases of the El Nino-Southern Oscillation (ENSO), while reproducing westward drift, 50-60 deg S eastward reversal and poleward cyclone-anticyclone deflection. Over ten days through one fixed tracker, HaiRuo's forecast field cuts the eddy-centre error of WenHai and XiHe by 37-43% (-2.8 km vs. day 0 while they degrade +4.3-8.7 km), and its object trajectories cut pipeline error by 77-78% globally. Open-weight checkpoints, code and project page: this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2611.14136
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Sim-TurboReg: Robust and Efficient Scale-Aware Registration via Tri-Scale Consistency
2026-11-10 14:23:34Zijun Zhou, Shaocheng Yan, Zhenjun Zhao, Yutong Hu, Ke Yang, Yingdong Gu, Pengcheng Shi, Jiayuan LiAbstract
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: this https URL
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URL
https://arxiv.org/abs/2611.14135
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Continuous 3D Spatial Transcriptomics Reconstruction via Histology-Informed Implicit Neural Representations
2026-11-10 14:16:40Kaito Shiku, Ryoma Bise, Ahtisham Fazeel Abbasi, Yuichiro Iwashita, Shumpei Takezaki, Andreas Dengel, Muhammad Nabeel AsimAbstract
Three-dimensional spatial transcriptomics (3D ST) enables the analysis of gene-expression patterns across tissue depths. However, 3D ST measurements are acquired from discrete 2D tissue sections, which are often sparsely sampled due to high acquisition costs. To address this limitation, we propose a histology-informed multi-task implicit neural representation (HIM-INR) for continuous 3D gene-expression reconstruction from sparse ST observations. HIM-INR jointly reconstructs gene-expression profiles and histological representations using an INR with a shared backbone, leveraging densely sampled histological images as auxiliary spatial supervision. Once trained, HIM-INR predicts gene expression at arbitrary 3D coordinates without requiring histological images. Experiments on two 3D ST datasets demonstrate improved reconstruction over existing methods, particularly as the number of missing ST sections increases.
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URL
https://arxiv.org/abs/2611.14129
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CroCoMAE: Cross-modal self-supervised collaborative representation learning for SAR-optical remote sensing data
2026-11-10 14:10:41Vito Recchia, Giuseppe Guarino, C\'assio Fraga Dantas, Giuseppina Andresini, Annalisa Appice, Dino IencoAbstract
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.
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URL
https://arxiv.org/abs/2611.14124
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TAMEing the Open-World Personalization: Towards Open-Set Personalized MLLM Assistant
2026-11-10 14:07:50Rongpei Hong, Jian Lang, Ting Zhong, Fan ZhouAbstract
Personalizing Multimodal Large Language Models (MLLMs) as intelligent assistants has recently emerged as an active research topic, where an MLLM is expected to go beyond generic, one-size-fits-all replies, and instead generate responses grounded in user-specific objects, entities, and concepts encountered during interactions. Nevertheless, existing studies mainly focus on personalizing MLLMs on a static, predefined set of personalized concepts and require users to manually and frequently extend this set to accommodate newly emerging concepts, which is infeasible in dynamic-world scenarios. To address this limitation, we recast conventional MLLM personalization as an Open-Set problem, and propose TAME-O, the first long-context open-set personalized MLLM assistant. Specifically, TAME-O introduces a training-free, plug-and-play Concept Reconciliation Skill (CR Skill). It enables the assistant to automatically unlock novel concepts during prolonged human-MLLM interactions while preventing confusion with previously unlocked ones, allowing the MLLM to continually grasp more and more concepts and progressively refine the modeling of known ones. Extensive experiments under the open-set personalization setting underscore the efficacy of our method.
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URL
https://arxiv.org/abs/2611.14118
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Where Do Vision-Language Models Get Left and Right? Position-Code Counterfactuals in Qwen2.5-VL, Qwen3-VL and InternVL3
2026-11-10 13:25:38Enhao Cui, Junzhi Yang, Yaoshun Lin, Yuhan Feng, Zijian ChenAbstract
A vision-language model may take left-right direction from the orientation of image content within patches, its vision transformer's (ViT) position code, or token positions and order in its language model (LLM). Ablations show the ViT code is necessary; we test sufficiency by mirroring only a position code. In Qwen2.5-VL-7B, Qwen3-VL-4B and InternVL3-2B, mirroring ViT width positions reproduces the flipped-image answer on 84.3-93.2% of CV-Bench left-right questions (Qwen2.5-VL-3B: 87.5%, partly a left fallback). With two options, this also counts disruption; a half-grid shift flips most answers too. With the ViT code intact, LLM-side id and token-order mirrors change few left-right answers in all three series (a weak id test in Qwen2.5-VL). In Qwen2.5-VL-7B the effect sits in the global-attention ViT blocks, mostly the last. On What'sUp, flipping content and token order, not the position code, keeps more on/under than left-right answers at the flipped-image answer. Post hoc analyses qualify these results. On CV-Bench items with located objects, random permutations flip 18.9-31.0 points more answers when reversing the objects' order than when keeping it. On What'sUp, in Qwen2.5-VL and Qwen3-VL, the share of the left-right changes made by random permutations that land on the mirror relation is mostly below chance. Counted as returns to the original answer, the What'sUp contrast of support with left-right relations is clear only in InternVL3-2B. Most remaining errors give the same relation whichever object is asked about, suggesting that repair may target relation binding, object localization or use of the reference object.
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URL
https://arxiv.org/abs/2611.14060
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CURRENT: A Recurrent Neural Network for Concurrent Spatial and Temporal Super-Resolution of 4D Flow MRI
2026-11-10 13:25:28Luuk Jacobs, Pietro Dirix, Sebastian KozerkeAbstract
Objective: To improve the spatial and temporal resolution of aortic 4D flow MRI through a unified deep learning framework for post-acquisition super-resolution (SR) of reconstructed velocity vector fields. Methods: We propose CURRENT, a recurrent neural network that performs spatial and temporal SR with a single network architecture. Supervised training was conducted using paired low- and high-resolution datasets generated by embedding computational fluid dynamics simulations of aortic flow into in vivo thoracic MRI backgrounds, followed by controlled spatial and temporal downsampling. CURRENT was evaluated for two-fold spatial and temporal SR against 4DFlowNet and for two-fold spatiotemporal SR against sequential spatial and temporal SR pipelines based on 4DFlowNet. Image-based and k-space-based quantitative metrics were considered and conventional interpolation were used as baselines. In addition, the impact of CURRENT's spatiotemporal SR on physics-informed relative pressure estimation was assessed. Finally, the ability of CURRENT to generalize from synthetic training data to in vivo acquisitions was investigated. Results: CURRENT achieved superior spatial SR performance and comparable temporal SR performance to dedicated 4DFlowNet models while providing a unified solution for both tasks. Simultaneous spatiotemporal SR using CURRENT yielded more accurate relative pressure estimates than either spatial or temporal SR alone and outperformed sequential 4DFlowNet-based spatial and temporal SR approaches. Furthermore, the proposed model demonstrated promising generalization to in vivo 4D flow MRI data despite being trained exclusively on synthetic datasets. Conclusion: CURRENT provides a unified framework for spatial and temporal SR of 4D flow MRI. The proposed approach has the potential to enhance physics-based analysis and broaden the applicability of accelerated 4D flow MRI.
Abstract (translated)
URL
https://arxiv.org/abs/2611.14058
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Structured Confidence Perturbation via Fisher Geometry for Adversarial Training
2026-11-10 13:13:30Chenlong Wei, Hanling Wang, Chong Zhang, Qinkai Yu, Fangyu Wu, Xiaobo JinAbstract
Adversarial training is often plagued by "pseudo-attacks", perturbations that significantly increase the loss value but fail to truly alter the model's confidence structure. To address this, we propose two complementary approaches to directly attack and defend against confidence structure deformation. First, building upon the traditional loss maximization perspective, we introduce a hybrid objective that integrates cross-entropy and KL divergence, encouraging adversarial examples to simultaneously disrupt prediction accuracy and distribution consistency. Second, we geometrically reconstruct adversarial training by generating perturbations that maximize the distribution distance on the confidence manifold. Theoretical analysis shows that the optimal perturbation direction aligns with the principal eigenvectors of the Fisher information matrix. We also propose an efficient algorithm to avoid high-dimensional matrix computations. Experiments on multiple datasets and model architectures demonstrate that our method outperforms existing methods in adversarial robustness and robust transferability, particularly excelling in structural perturbations within high-confidence regions.
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https://arxiv.org/abs/2611.14046
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HiPFuse: Real-Time LiDAR Semantic Segmentation for Robotic Perception on Edge Devices
2026-11-10 12:57:21Haiyang Wu, George Vosselman, Ville LehtolaAbstract
Real-time robotic perception requires accurate LiDAR semantics under limited resources. Bird's-eye-view (BEV) aggregation is efficient but can mix differently labeled surfaces in columns. We present HiPFuse, a point-BEV fusion framework that exchanges point and height-partitioned BEV features across horizontal scales, combining point-wise detail with 2D spatial context from one LiDAR scan. Fixed height assignments preserve separate vertical aggregation groups, with auxiliary supervision of initial BEV features using targets from the same 3D cells. A dual-scale BEV context module adds broader context from a coarser grid. On SemanticKITTI and MCD, HiPFuse achieves 63.7% and 62.5% mIoU at 14.2 and 22.4 FPS on Jetson AGX Orin, respectively, exceeding the 10 Hz scan rate. Separate training and evaluation show indoor applicability. Replacing only the baseline semantic front end with HiPFuse in fixed robotic systems reduces odometry absolute trajectory error RMSE by 10.6% on average across two outdoor sequences and increases accepted-loop recall by 8.7 percentage points on SemanticKITTI. Qualitative semantic maps show more continuous road labeling. Our code is available at this https URL.
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URL
https://arxiv.org/abs/2611.14025
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Lipschitz-Controlled Flow Matching for Robust Image Restoration
2026-11-10 12:40:17Sedjro Salomon Hotegni, Konstantin Sonntag, Paul-Christian B\"urkner, Sebastian PeitzAbstract
Flow-matching models can produce high-quality image restorations, but their learned dynamics may amplify small input perturbations and make reconstructions unstable. We introduce LipsFlower to reduce this amplification. We derive stability bounds for Flow-from-$Y$, which initializes the ordinary differential equation from a noise-perturbed observation, as well as for observation-conditioned posterior sampling, which conditions the velocity field on the observation throughout integration. Based on this analysis, we develop LipHDiT, a Lipschitz-aware hourglass diffusion transformer that serves as the vector-field architecture of LipsFlower. LipHDiT incorporates stabilized approximate spectral normalization, bounded modulation, and convex residual mixing. We also use adversarial finite-radius gain regularization at sampled interpolation states to penalize excessive sensitivity to the state and, for conditional sampling, the observation. We test both formulations on blind face restoration, measuring adversarial robustness and the perceptual quality of the reconstructions. LipsFlower reduces perturbation amplification while keeping restoration quality competitive with the evaluated baselines. These results suggest that Lipschitz-aware flow matching can improve the stability of generative image restoration.
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URL
https://arxiv.org/abs/2611.13995
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Pulse or Provenance? What rPPG-Based Deepfake Detectors Learn
2026-11-10 12:36:56Othmane Harraq, Tamer AldwairiAbstract
Remote photoplethysmography (rPPG) has been proposed as a physiological cue for deepfake detection, based on the premise that synthesized faces lack a genuine cardiac signal. We examine what rPPG-style detectors learn on Celeb-DF++ (22 forgery methods) and FaceForensics++ (FF++), using one identically sampled window per real and fake video. On talking-face forgeries, simple chrominance traces reach an AUC of 0.998, exceeding a dedicated rPPG extractor (0.87). Controlled experiments indicate that this signal is not physiological in origin. On fully synthesized forgeries, it is equally discriminative when extracted from background regions or from full frames without face detection, and it is concentrated above the 0.7 to 4 Hz heart-rate band. It is largely removed by shuffling frame order but persists after normalizing resolution, frame rate and encoding, and an independent reimplementation reproduces these findings. Removing high-frequency content makes most synthesized videos appear real, adding it makes real videos appear fake, and injecting a synthetic pulse has little effect. In controlled self-reenactment experiments with two generators, animating a face from a single frame introduces the same high-frequency signal, whereas reconstructing each frame from its own source does not. Face-based chrominance detectors trained on Celeb-DF++ classify over 99% of real FF++ videos as fake, indicating that they largely encode video source rather than manipulation, and a CLIP-based detector exhibits the same behavior. A frozen CLIP backbone matches or exceeds fusions of CLIP with rPPG and frequency features. Finally, we show that the official Celeb-DF and Celeb-DF++ splits share identities and source-video content between training and test, and that custom protocols reusing test videos and identities in training inflate weaker detectors by up to 0.30 AUC.
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URL
https://arxiv.org/abs/2611.13992
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DORIS: Polarization Aware Multimodal Degradation Perception for Underwater Image Restoration
2026-11-10 12:33:35Wenshuo Zhang, Xiangyue Zhang, Junlin Li, Wenjie Tian, Yubo Zheng, Zhixin Dong, Hongli Xu, Jingyu Ru, Xinde LiAbstract
Recovering high-quality images from degraded underwater observations is crucial for underwater exploration. Polarization observations show promise as an effective source of complementary physical cues for recovering target radiance under clear-water conditions. However, existing methods have yet to jointly model raw intensity information and polarization cues to enable image restoration driven by an understanding of mixed underwater degradations. To overcome these limitations, we propose DORIS, a unified multimodal language-driven framework that jointly uses polarization and RGB information to generate textual descriptions of degradation types and severity levels through structured semantic instructions and enhances joint restoration representations under the guidance of degradation-aware semantic features to improve restoration robustness under mixed degradations. We further construct a new benchmark named MixPolar, which contains underwater images exhibiting real degradations and the corresponding annotations. It supports systematic evaluation of degradation perception, image restoration, and downstream task performance. Extensive experiments demonstrate that our method effectively integrates polarization and RGB information, achieving competitive performance in degradation perception and image restoration.
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URL
https://arxiv.org/abs/2610.17991
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Counterfactual Analysis of Extent Leakage from Annotation-Derived Prompts in Vision-Language Medical Image Segmentation
2026-11-10 12:07:24Niran Nataraj, Ankita Das, C. Krishna Mohan, Kenji KawashimaAbstract
Text-guided segmentation models receive an image and a short caption about the lesion, and on public benchmarks the caption improves accuracy considerably. On colonoscopy and breast ultrasound, however, the size, count and location words of each caption were computed from the mask that later scores the prediction, and the chest X-ray captions were written by benchmark annotators. We tested what two published models take from them. On colonoscopy and chest X-ray, it is mostly the lesion's extent. Keeping only the size word or the lung-zone phrase recovers 84 to 98 percent of the improvement over an empty caption, and removing it loses 52 to 77 percent. The rest matters little, and a polyp called a rock is segmented just as well. We call this extent leakage. On breast ultrasound, nearly half the captions also name the diagnosis, which carries part of the benefit. Retraining on three datasets shows that models pick up this habit from their training captions, though only partly on breast ultrasound, and that it weakens their segmentation from the image alone. The leak can be measured without training a model, since caption words alone predict lesion area (coefficient of determination 0.54 to 0.77 on six of seven test sets). Without the test mask, the best method we tested, which estimates extent from the image, reaches 74.59 Dice on colonoscopy against the published 77.23. We propose an evaluation protocol that keeps what a caption says about the image apart from what it copies from the label.
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URL
https://arxiv.org/abs/2611.13970
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RB-LDC: Redundancy-Balanced Latent Coding for Noise-Robust Diffusion Latents
2026-11-10 11:41:05Hyunseok Jeong, Jaeho Jeon, Young-Sik KimAbstract
Latent diffusion models decode approximately correct latents, making decoder sensitivity to perturbations relevant to image quality. After whitening, forward diffusion corrupts every direction at the same signal-to-noise ratio (SNR), despite unequal decoder sensitivities. We study an invertible linear layer between the encoder and diffusion process that shifts each direction's log-SNR. Its linear minimum mean-square-error (LMMSE) recovery risk has an exact spectral expression for any whitened latent distribution. Under an energy budget and a floor ensuring invertibility, minimizing decoder-sensitivity-weighted LMMSE risk integrated over the diffusion schedule yields a unique optimal allocation. The layer aligns with the decoder's expected pullback metric and equalizes marginal risk reduction across directions, computed by a deterministic spectral solver. At large budgets, the allocation follows a power law in sensitivity whose exponent is determined by the low-SNR tail of the loss weighting: one half for noise prediction and two thirds for data prediction under the cosine schedule, with a crossover on truncated schedules. We instantiate this principle as RB-LDC using Jacobian-based sensitivity estimates. On ImageNet at 256 x 256 resolution with SD-VAE and VA-VAE, trained RB-LDC improves reconstruction from noisy latents over budget-matched isotropic coding in all four metrics and five coding configurations, averaged over 15 SNRs and three pipeline seeds. It reduces average perturbed FID by 17.1 for VA-VAE block-local coding while preserving clean reconstruction. We also report intermediate SNRs where perturbed FID worsens. With a frozen diffusion model, RB-LDC yields lower FID point estimates in all 12 tested combinations of sampling progress and channel SNR under corruption during sampling.
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URL
https://arxiv.org/abs/2611.13955
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SegDis: Foreground Segmentation Followed by Type Discrimination for Salient and Camouflaged Object Detection
2026-11-10 11:26:14Dingning Luo, Shuo Dou, Tong Jiang, Zhenhua Duan, Jiachen Zhu, Xianfeng LiAbstract
Salient and camouflaged object detection (SOD and COD) both recover foreground support, yet most joint methods output only a binary mask and therefore cannot distinguish the type. USC12K distinguishes the two types, but its baseline USCNet still binds foreground existence to the type within a single normalization, so the failure source cannot be attributed. Our cross-task study shows that foreground localization transfers between SOD and COD, whereas type discrimination does not: a COD-only model recovers foreground on SOD images but its type judgement follows the training distribution rather than the foreground-background relation, so the two decisions require separate mechanisms. SegDis factorizes the output into an explicit foreground probability and a conditional type probability given foreground: FLR provides high-quality task-agnostic support, and RTA refines the SOD/COD assignment from the foreground-background relation without a task identifier. Because support is explicit, each ground-truth foreground pixel is classified as correct-type, type-swapped, or missed, so foreground-recovery and type-assignment errors are diagnosed separately. With PVT-v2-B2, SegDis attains 68.98% FG-mIoU and 78.35% mIoU on USC12K at about one-eighth of USCNet's parameters; CSCS is 5.75.
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URL
https://arxiv.org/abs/2611.11378
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MEOW: Medical Open-World Interactive Segmentation
2026-11-10 11:21:59Philip Matthias Winter, Maria Wimmer, Bernhard Sch\"{a}fl, David Major, Dimitrios Lenis, Astrid Berg, Theresa Neubauer, Ariharasudhan Muthusami, Johannes Novotny, Georg Zotti, Katja B\"{u}hlerAbstract
In open-world recognition, machine learning models not only make predictions on unknown structures ("generalized open-set"), but also continually learn and improve from those predictions over time. Interactive segmentation, despite its access to user feedback, has so far remained confined to the open-set setting. In this work we introduce MEOW, the first framework that turns non-interactive segmentation backbones into interactive open-world learners without updating learnable model parameters. MEOW combines parameter-free transductive reasoning with memory consolidation to accumulate knowledge and thereby improve future performance. Focusing on medical data, we show that MEOW consistently improves its own predictions on classes never seen during training, and transfers zero-shot to unfamiliar domains, including micro-CT data and other imaging modalities.
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URL
https://arxiv.org/abs/2611.13935
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Adapting a Vision Foundation Model for Fracture Characterization in Acoustic Image Logs
2026-11-10 11:04:27Guangyu Wang, Xiaodong Ma, Xinming WuAbstract
Natural fractures (NFs) and drilling-induced tensile fractures (DITFs) in acoustic image logs provide insights into rock mass structure and in situ stress, but manual characterization is time-consuming. Deep learning can automate this task, although expert-labeled data are scarce. We investigate whether adapting a pretrained vision foundation model (VFM) outperforms task-specific models trained from scratch with limited annotations. Using 80 annotated acoustic image samples, we fine-tuned DINOv3, pretrained on satellite imagery, and evaluated it on four boreholes excluded from fine-tuning. The adapted model estimated NF and DITF parameters comparably to human interpretation. Relative to U-Net, YOLOv8, and Swin Transformer models trained from scratch, it yielded fewer false positives and negatives and more accurate fracture counts and orientations. However, its performance advantage generally narrowed as training sample size decreased. We also assessed decoder architecture, fine-tuning strategy, and image enhancement. A progressive upsampling decoder with full fine-tuning performed best among the DINOv3 configurations tested, while fracture-focused image enhancement did not consistently improve accuracy. These results demonstrate the potential of VFM adaptation for fracture characterization in acoustic image logs with limited labeled data and suggest a transferable workflow for other annotation-scarce geoscientific imaging tasks.
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URL
https://arxiv.org/abs/2611.13911
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