Computer Vision · Cross-listed from eess.IV
Secret-Stego Dissimilarity as a Design Axis: Invertible Coverless Image Steganography with Diffusion Models
Hongxin Xu, Jianping Mei, Can Wang et al. (+1 more)
Coverless image steganography (CIS) synthesizes a stego image rather than modifying an existing cover image, enabling authorized recipients to reconstruct the original secret image from the stego. Existing
diffusion-based CIS methods can generate natural-looking stego images but preserve substantial visual similarity to the secret image. This resemblance risks exposing structural and semantic cues, giving rise to security vulnerabilities that cannot be evaluated solely via recovery fidelity. Achieving substantial visual dissimilarity between the secret and stego images without compromising stego quality and recovery fidelity remains challenging. To address this issue, we propose InvCISD, an invertible
diffusion framework that couples the latent representations of the secret and an irrelevant reference image with an invertible network called LIMNet. We first train LIMNet in
diffusion latent space, followed by end-to-end fine-tuning of the entire network, i.e., LIMNet integrated
diffusion inversion and generation modules. Experiments demonstrate that the proposed method substantially reduces secret-stego visual similarity, improves stego quality, and retains satisfactory secret reconstruction quality. Our further investigation shows that all evaluated methods are highly detectable by the CIS-oriented steganalysis model, indicating that resistance against targeted steganalysis constitutes a critical direction for future CIS research.
Read the full abstract ▾
All authors Hongxin Xu, Jianping Mei, Can Wang, Defang Chen
Computer Vision
Learning an Interior Layout Policy in a Domain Specific Language Action Space
Yuhao Lu, Weichen Zhang, Wenyi Xiao et al. (+2 more)
Indoor scene layout generation is a challenging task in interior design. Existing methods often oversimplify the task by reducing room conditions to coarse 3D bounding boxes and neglecting structural elements such as doors and windows. More fundamentally, many prior approaches formulate spatial
reasoning as direct coordinate prediction, thereby casting interior layout design as continuous regression over raw
geometric parameters, which hinders the model from learning the underlying
reasoning logic of intelligent layout design. We propose LayoutDSL, a novel LLM-based framework for learning an interior layout policy in a domain-specific
language (DSL) action space. The DSL provides an explicit symbolic representation of layout information and serves as a structured action space for layout
reasoning, where each action corresponds to an interpretable design decision. Under this DSL-based policy learning paradigm, we construct 3D-FrontDSL, a
dataset of room-structure annotations paired with synthetic DSL action sequences for supervised fine-tuning. To promote a more generalizable and scalable policy with verifiable feedback, we design rewards grounded in interior design principles and physical plausibility, and optimize the policy via reinforcement learning. Extensive experiments demonstrate that LayoutDSL substantially improves spatial plausibility and design logicality over strong baselines and existing methods.
Read the full abstract ▾
All authors Yuhao Lu, Weichen Zhang, Wenyi Xiao, Haohui Chen, Yiyun Fei
Computer Vision
Performance of large language models in the optical diagnosis of colorectal polyps
Joshua C. Vences, William T. Tran, Nikko Gimpaya et al. (+15 more)
Background and Study Aims: Accurate optical diagnosis of colorectal polyps guides resection strategy and surveillance, with
multimodal large
language models (MLLMs) showing potential for image-based diagnosis. We aimed to evaluate the diagnostic accuracy of MLLMs in classifying colorectal polyps and predicting histology. Methods: We conducted a retrospective diagnostic performance study using the
PRIME dataset, a curated set of white light and narrow-band imaging (NBI) images. We evaluated Claude Opus 4, Google Gemini 2.5 Pro, GPT-o3, GPT-4o, and GPT-5. For Paris, Narrow-band Imaging Colorectal Endoscopic (NICE), and predicted histology, we calculated F1 scores, percent correct scores, and accuracy of each MLLM compared to expert responses for 132 cases. Cochran's Q and McNemar's Test were used to determine differences between predicted values of each MLLM. Results: The F1 scores among MLLMs were >0.9 for all models for neoplastic vs. non-neoplastic polyps. Gemini 2.5 Pro demonstrated the highest F1 scores for invasive vs. non-invasive polyps and low- vs. high-grade adenoma, at 0.560 and 0.492 respectively. Claude Opus 4 and GPT-5 had statistically significantly higher percent correct scores than other MLLMs at 41.7%, using Paris classification. Conclusions: Claude Opus 4 and Gemini 2.5 Pro showed the highest accuracy in differentiating polyp subtypes, performing closest to expert consensus. Sensitivity and specificity, however, did not meet ESGE standards, highlighting the need for prospective multicenter trials and the design of human-in-the-loop workflows before clinical deployment.
Read the full abstract ▾
All authors Joshua C. Vences, William T. Tran, Nikko Gimpaya, Catharine M. Walsh, Rishad J. Khan, Robert Bechara, Asher C. Wiggins, Celine N. Rousan, Kaitlyn V.G.L. Morgado, Angie Ibrahim, Kevin H. M. Kuo, Daniel von Renteln, Alexander Hann, Dennis L. Shung, Michael A. Scaffidi, Charles Ménard, Joshua Landy, Samir C. Grover
Computer Vision
ORCA: ORgan-Centroid Aggregation for Training-Free 3D CT Visual Token Compression
Renjie Liang, Zijian Xu, Jinqian Pan et al. (+6 more)
A 3D CT scan entering a
vision-
language model produces a long sequence of visual
tokens, often thousands to tens of thousands per volume, and this sequence must be compressed before a
language model can consume it.
Token compression is well studied in general
vision, but little of it targets 3D CT specifically. A common baseline is grid average, which pools regular grid
cells and can blend distinct anatomy, lesion, and air into one
token. We present ORCA (ORgan-Centroid Aggregation), a
token compressor for 3D CT. It merges adjacent
tokens with organ guidance and adds a sinusoidal encoding of each region's centroid to preserve spatial layout. This preserves the anatomical information a downstream model needs. ORCA is training-free and plug-and-play, producing an adjustable
token set without any model change or text query. We evaluate it across two
datasets (CT-RATE and Merlin) and five encoders. The evaluation spans two task types: attribute prediction over five families (size, density, location, texture, and disease) and text generation (visual question answering and report generation). At matched
token budgets, ORCA improves consistently over existing compression methods. It shrinks the visual context $64\times$ and its KV-cache $50\times$, and is $31\times$ faster to process each volume. Code released at https://github.com/renjie-liang/ORCA-3DCT.
Read the full abstract ▾
All authors Renjie Liang, Zijian Xu, Jinqian Pan, Chengkun Sun, Zhengkang Fan, Shawn Li, You Qin, Mei Liu, Jie Xu
Computer Vision · Cross-listed from cs.RO
Belief-Space Perception Routing under Coupled Sensor Faults and Compute Contention
Sparsh Roy, Vihan Aggarwal and Davin Yin
A
robot that has to see and react on a fixed clock runs into two problems at once. Its cameras degrade in rain, mud, fog, and darkness. And the single onboard processor it runs on is shared with planning and control, so the compute left over for perception moves around from second to second. Most systems model the two separately. We present a perception router that tracks probabilistic estimates of sensor-fault state and compute- contention state, couples them with a noisy-OR term, and uses the coupled estimate to pick one of four detector configurations (YOLO11x/n at 1280 or 640 px) so that the frame finishes before its deadline. Where the two stressors co-occur, the coupled policy cuts the deadline-miss rate by 1.1 to 9.4 percentage points against a policy that treats them independently. The interval excludes zero in five of six conditions, the pooled effect over 10 sequences and 6 conditions has sign-test p = 0.001, and every uncoupled control and the fault-free trajectory sit at exactly 0.0 pp. Routing costs tens of microseconds per frame. We then asked whether the coupling the method exploits arises on its own. Across eight real RADIATE adverse-weather sequences and three workload proxies independent of the fault signal, after Benjamini-Hochberg correction and a replication run, none of 24 tests found it. We report that null and scope the routing result as a proof of mechanism. Whether such coupling occurs in the field is still open, and the released evaluation pipeline lets a deployment settle it on its own traces.
Read the full abstract ▾
Computer Vision · Cross-listed from eess.IV
Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation
Xuan Cuong Ngo
Recent radiology-adapted
vision-
language models have achieved strong performance on standard report generation
benchmarks, yet their robustness and generalization remain constrained by imperfect
alignment and correlation between visual and textual features. Existing methods connect image and text either implicitly through autoregressive report supervision or explicitly through contrastive learning. However, autoregressive supervision alone is insufficient to establish reliable image-text
alignment, while contrastive learning can push apart unpaired reports that describe related pathologies simply because they are not paired with the same image. This is problematic in radiology, where different reports may share compatible pathology semantics rather than being true negatives. As a result, the learned representation may fail to organize images and reports around shared pathology concepts, causing the decoder to rely on pretrained
language priors and generate clinically plausible reports that are not fully supported by radiographic evidence. To address this issue, we propose PALM, a pathology-aware
alignment framework for radiology report generation. Instead of directly matching each image-report pair while separating all others, PALM aligns visual and textual features through shared pathology prototypes. These prototypes provide a clinically meaningful bridge between radiographic evidence and textual findings, allowing cases with similar pathology semantics to move toward common concepts without separating compatible cases. In addition, we introduce Masked Evidence Modeling to strengthen the image encoder sensitivity to local radiographic evidence by learning semantic changes caused by masked image regions. Experiments on MIMIC-CXR, IU X-Ray, and MIMIC-ABN show that PALM consistently improves both report generation and abnormality-focused robustness.
Read the full abstract ▾
Computer Vision
Kamil Książek, Piotr Suszyński, Michał Jan Włodarczyk et al. (+2 more)
Vision foundation models, such as DINOv2, learn highly expressive representations but rely on massive, opaque architectures that demand substantial computational power and
memory. To provide an interpretable-guided and efficient solution to this issue, we first propose a spectral analysis and new visualization technique for individual
attention heads based on the
Laplacian eigenvectors of their
attention maps. Building upon recent observations regarding the block structure of
Vision Transformers, we perform semantic clustering of
attention heads and identify functional redundancies. Leveraging these insights, we introduce SAPER (Soft
Attention PrunER), an end-to-end differentiable
pruning framework based on the LapSum Soft Top-K approach. Extensive experiments on ImageNet-1K demonstrate that SAPER achieves a highly favorable accuracy-efficiency trade-off, outperforming the competitive RAPTOR baseline in FLOPs reduction while preserving strong classification performance.
Read the full abstract ▾
All authors Kamil Książek, Piotr Suszyński, Michał Jan Włodarczyk, Jacek Tabor, Przemysław Biecek
Computer Vision · Cross-listed from cs.CL
Cross-Task Dissociation in Frontier Vision-Language Model Theory of Mind
Kejia Zhang, Youran Sun, Chugang Yi et al. (+1 more)
Do frontier
vision-
language models present a coherent Theory-of-Mind (ToM) profile across tasks, matching the same human reference group, or does that profile fragment from one paradigm to the next? We evaluate a shared panel of nine frontier VLMs on two psychology-derived
benchmarks: the Keysar Director Task (visual perspective-taking under
egocentric interference) and the Frith-Happé animated triangles scored with the Castelli rubric (intention attribution from pure motion). On the Director Task, without chain-of-thought, the panel makes the
egocentric error on 78% of trials like children rather than adults; variation is substantial across models, and
reasoning rescues several models. On the triangles, the panel under-attributes intention: its ToM profile sits more than three times closer to the high-functioning-autistic-adult (HF-ASD) mean than to the typical-development-adult (TD) mean, while Goal-Directed and Random stay near TD. No model is nearest TD on both tasks; the model that looks adult-like on the Director Task falls on the HF-ASD side on the triangles, and the most TD-like model on the triangles is child-like on the Director Task. We report group-level descriptions, not diagnostic labels for any model.
Read the full abstract ▾
All authors Kejia Zhang, Youran Sun, Chugang Yi, Haizhao Yang
Computer Vision
MDWD: A Street-Level Dataset for Municipal Solid Waste Detection in Dense Urban Environments
Andrea Filiberto Lucas, Mark Bugeja, Carl James Debono et al. (+1 more)
Automated visual monitoring of urban environments is a growing Computer
Vision research area, but municipal solid waste detection remains under-represented in dedicated
benchmark resources. Existing waste-related
datasets predominantly address individual litter detection, aerial imagery, or image-level classification, and none simultaneously provide street-level imagery, instance-level localization, and categorization of domestic waste streams within a structured municipal collection context. This paper introduces the Maltese Domestic Waste
Dataset (MDWD), a street-level
benchmark comprising 3,697 high-resolution images and 11,461 manually annotated instances across five domestic waste categories representative of Malta's municipal collection system. The
dataset captures substantial variation in location, illumination, object scale, occlusion, and urban context. To establish reproducible baselines, a cross-architecture
benchmark is conducted across multiple generations of the YOLO family and a
transformer-based detector. On the test set, RF-DETR-M achieves the strongest overall performance with an mAP50 of 94.49% and an F1-score of 93.56%, whilst smaller-capacity variants maintain competitive accuracy at substantially reduced parameter counts. These results indicate that MDWD supports effective training across both compact real-time detectors and
transformer-based models, establishing a
benchmark for future research in
vision-based municipal waste monitoring.
Read the full abstract ▾
All authors Andrea Filiberto Lucas, Mark Bugeja, Carl James Debono, Dylan Seychell
Computer Vision
Semantically Calibrated Evidence Composition for CT Vision-Language Learning
Guoliang You, Haifan Gong and Xiaomeng Chu
Learning transferable representations from CT-report pairs requires combining whole-volume context with anatomy-specific evidence. Existing methods typically emphasize either global CT-report
alignment or fine-grained anatomy-level correspondence. Global
alignment preserves broad study context but leaves the contribution of localized evidence implicit, whereas anatomy-level
alignment explicitly grounds local findings but does not specify how independently represented evidence should interact, acquire study-level meaning, and contribute to a global CT representation. To address this gap, we propose SCOPE (Semantic Calibration Of comPosed Evidence), a framework for semantically calibrated evidence composition in CT
vision-
language learning. Under organ-specific report supervision, mask-guided queries with fixed anatomical identities extract context-aware organ evidence from shared, uncropped volumetric features, while an unrestricted global query retains access to whole-volume context. The global query then drives Local-Global Coupling to compose the organ evidence into a unified evidence representation. The composed evidence is subsequently calibrated using the diagnostic summary, providing study-level semantic supervision beyond local organ descriptions, and is finally integrated as a controlled residual into a context-preserving whole-volume representation aligned with the complete report. This progressive pathway connects localized evidence with study-level semantics without reducing the CT representation to a predefined set of organs. On CT-RATE and RadChestCT, SCOPE achieves macro AUCs of 85.0 and 72.2, respectively, outperforming the previous SOTA by 7.2 and 4.2, while also yielding substantial gains in linear probing and cross-modal
retrieval. These results demonstrate the effectiveness of semantically calibrated evidence composition.
Read the full abstract ▾
Computer Vision
Latent-Centroid Steering: Single-Pass Classifier-Free Guidance for Command-Aligned Autonomous Driving
Meibo Hu, Jiamian Wang, Pichao Wang et al. (+1 more)
Vision-
language models (VLMs) have recently emerged as a promising paradigm for end-to-end autonomous driving, enabling
agents to map
multimodal inputs and high-level
navigation instructions directly to executable trajectories. However, in practice, these models exhibit a persistent command-following gap: predicted trajectories often show weak sensitivity to
navigation commands, resulting in incorrect behavior at critical decision points. We identify this issue as a form of conditional policy collapse, where regression-based training under
multimodal trajectory distributions encourages the model to rely on dominant visual priors while marginalizing the
language-conditioned signal. To address this issue, we introduce a principled formulation of classifier-free guidance (CFG) for regression-based
vision-
language driving. We show that CFG can be interpreted as isolating the instruction-induced residual in the action space by contrasting conditional and unconditional predictions, thereby explicitly amplifying the effect of the
navigation command at inference time. However, a standard two-pass CFG introduces prohibitive latency for real-time control and produces noisy instance-level guidance directions. Building on a mean-shift interpretation of CFG, we propose Latent-Centroid Steering (LCS), a single-pass guidance mechanism that replaces instance-level residuals with class-level latent shifts. By projecting conditional representations toward precomputed command-specific centroids, LCS performs class-level latent steering based on cluster geometry that is both more stable and computationally efficient. We demonstrate that LCS reduces inference latency by approximately 50% while achieving stronger command adherence and improved driving performance on both closed-loop (Bench2Drive) and open-loop (nuScenes)
benchmarks. Code are available at https://github.com/codingmlinprocess/LCS.
Read the full abstract ▾
All authors Meibo Hu, Jiamian Wang, Pichao Wang, Zhiqiang Tao
Computer Vision
Meibo Hu, Guohao Sun, Annemarie D. Ross et al. (+2 more)
Vision-
language models (VLMs) have emerged as a powerful framework for
multimodal video understanding. However, they remain limited in the sign
language translation task, where we identify a key failure mode of existing VLMbased translators: poor spatial-temporal visual grounding. In particular, we find that standard next-
token cross-
entropy does not directly provide signal for where and when the model should attend, causing models to overlook sign-relevant regions and frames. To address this challenge, we propose AttnSign, a VLM-based spatial-temporal
attention steering framework for sign
language translation. AttnSign first introduces spatial
attention supervision for sign-relevant regions, such as face and hands, in each frame; then develops an RL-based motion-cadence steering method that encourages the model to explore and focus on sign-level keyframes. Experimental results on How2Sign and OpenASL
benchmarks show that our proposed AttnSign consistently outperforms existing methods.
Read the full abstract ▾
All authors Meibo Hu, Guohao Sun, Annemarie D. Ross, Sheng Li, Zhiqiang Tao
Computer Vision
Real-Time Visual Obstruction Detection in Surgical Augmented Reality
Shih-Chin Yang, Yanming Xiu, Hanting Ye et al. (+3 more)
Surgical augmented reality (AR) can provide contextual guidance by overlaying virtual annotations, tool cues, and procedural information onto the
surgical workspace. However, the virtual content may obstruct task-relevant real-world information, such as
surgical instruments, and interfere with users' perception during time-sensitive
surgical tasks. In this paper, we investigate visual obstruction detection for
surgical AR and present a latency-aware pipeline that combines
vision-
language model (VLM)-based
surgical-object recognition with
segmentation-based obstruction
reasoning. To reduce inference overhead, the system adopts a cascaded small-to-large VLM architecture with
segmentation-guided early exiting and
attention-based visual
token pruning. The small VLM handles easy frames when its key-object prediction is supported by
segmentation consistency, while difficult frames are forwarded to a large VLM with pruned visual
tokens. We construct a pseudo-AR
surgical obstruction detection
benchmark by overlaying virtual content onto
surgical-tool images and labeling whether the virtual content obstructs task-relevant instruments. Evaluation results show that the proposed system achieves 87.43% obstruction detection accuracy with an average end-to-end latency of 479 ms, reducing latency by 62.90% compared with a cloud large-model baseline. These results demonstrate the feasibility of latency-aware obstruction detection for
surgical AR and motivate future work on dynamic
surgical videos, multi-object scenes, and clinically grounded AR guidance content.
Read the full abstract ▾
All authors Shih-Chin Yang, Yanming Xiu, Hanting Ye, Qi Chen, Elias Rotondo, Maria Gorlatova
Computer Vision
Learning How Much, Not Just What: Cross-Patient Burden Order for CT Vision-Language Pretraining
Guoliang You, Haifan Gong and Xiaomeng Chu
Volumetric CT
vision-
language pretraining learns 3D representations from scan-report pairs, but global and anatomy-aware objectives supervise only correspondence: they establish what is present and leave how much unconstrained. Nothing separates a mild from an extensive case of the same finding along a consistent direction, so the graded burden
language in reports collapses into a present/absent signal. Longitudinal supervision would supply this order, but patient-matched CT pairs are scarce at scale; cross-sectional cohorts already encode weak burden cues across different patients. We introduce Spectrum, an anatomy-conditioned framework that represents each study at whole-study and organ scopes. For each organ-mapped pathology, a rule-based scorer mines confidence-filtered lower-to-higher pairs of different patients, and Burden-Direction
Alignment (BDA) aligns the pathology-conditioned image delta with the report delta at each scope, separating that direction from its reverse. Because the endpoints are different people, a target-conditioned aligner first makes them comparable, so the delta reflects burden rather than between-patient variation. BDA further separates the selected direction from its reverse, anchors it to the observed higher-burden endpoint, and enforces consistency across ordered triplets. Since every pair is drawn within a single pathology, BDA is designed to constrain intra-class structure that image-report contrast alone never touches. Spectrum attains 85.6 zero-shot AUROC on CT-RATE and 72.7 on external RAD-ChestCT, with consistent gains in linear probing and
retrieval. Weak cross-patient order is thus a scalable complement to anatomy-aware correspondence, yielding burden-aware CT representations without longitudinal data.
Read the full abstract ▾
Computer Vision
Manifold-GS: Certified Hybrid Assets via Varifold-Conservative Gaussian Splatting
Boyang Li
3D Gaussian Splatting (3DGS) gives high-quality novel-view synthesis, but its adaptive radiance primitives are not directly usable as structured assets: opacity is not an additive area measure, refinement can change the induced geometry, and watertight mesh extraction can hallucinate collision surfaces in unobserved regions. We introduce
Manifold-GS, a certified hybrid asset layer for Gaussian scenes. The method separates appearance opacity from
geometric quadrature mass, represents surface-like Gaussians as a discrete unoriented varifold, and exports only confidence-certified open surface patches while retaining uncertified content as residual splats. It provides refinement-conservative mass transport, local realizability diagnostics, source-preserving patch bindings, and conservative collision candidates. On three DTU scenes, a frozen asset
benchmark shows zero patch-defined edit leakage, texture round-trip PSNR of 30.1/35.3/33.7 dB, and lower collision floater area than official 2DGS meshes on all scenes, with large gaps on two scenes. The result is a precision-coverage tradeoff rather than a universal reconstruction claim. External-region annotations, phantom-collision probes, and 5k-face simplification further support the certified asset interpretation, while RGB-only experiments show that local realizability is not sufficient for sparse-view surface identifiability.
Read the full abstract ▾
Computer Vision · Cross-listed from eess.IV
MedSAM2-Anatomy: Training-Free Inference-Time Optimization for Musculoskeletal Segmentation
John Garcia Henao, Nicholas Bünger, Benedikt Herzog et al. (+11 more)
High-resolution 3D
segmentation of hip and shoulder anatomy from CT and MRI is essential for
surgical planning, yet frozen
segmentation models often fail under domain shift. CNN-based expert models are fully automatic but lack adaptability, whereas promptable foundation models generalize better but require manual prompting. We present MedSAM2-Anatomy, a training-free inference-time optimization framework that improves frozen
segmentation models without retraining or human interaction. A frozen expert model generates anatomical priors that are automatically converted into multiple
prompt hypotheses for a frozen 3D foundation model. Candidate masks are fused while anatomically implausible priors are rejected. No model weights are updated and no manual
prompts are required. TotalSegmentator and MedSAM2 are used as representative expert and foundation models, allowing the contribution of the inference policy to be isolated. Evaluation on the independent Balgrist-V0 CT and MRI cohorts shows that inference-time optimization increases median Dice from 0.71 to 0.92 on hip MRI and from 0.89 to 0.92 on shoulder CT, while reducing median HD95 on hip MRI from 22.0 mm to 5.0 mm. On public TotalSegmentator
benchmarks, the expert model remains strongest, indicating that the optimal fusion strategy depends on the reliability of the expert prior. These results demonstrate that training-free inference-time optimization provides a practical strategy for improving frozen
segmentation models without manual prompting.
Read the full abstract ▾
All authors John Garcia Henao, Nicholas Bünger, Benedikt Herzog, Cindy Guerrero Toro, Benjamin Vella, Matthias Biner, Rico Brütsch, Carmen Castroviejo Fernandez, Felix Öttl, Norman Juchler, Armando Hoch, Bettina Hochreiter, Sven Hirsch, Sebastiano Caprara
Computer Vision
SCALP: Semi-Supervised Statistical Shape Modeling from Imperfect 3D Photogrammetry via Landmark-Anchored Spectral Warp
Nawazish Khan, Sanjay Bhandari, Sarang Joshi et al. (+8 more)
Correspondence-based statistical shape modeling (SSM) is vital for population-level morphometric analysis, but conventional pipelines assume clean, fully registered surfaces. Real-world clinical photogrammetry scans are often noisy, partial, and cluttered, hindering the adoption of radiation-free surface imaging as a safe alternative to computed
tomography (CT) for infant craniosynostosis. We present SCALP (Semi-supervised Correspondence via lAndmark Localization and sPectral warping), a two-stage framework that constructs consistent shape models directly from raw, imperfect surface scans. First, a semi-supervised Point
Transformer leverages a small expert-annotated
dataset alongside a large unlabeled cohort to accurately localize craniofacial landmarks with minimal annotation overhead. Second, these landmarks anchor a Laplace–Beltrami spectral deformation of an anatomical template, generating dense correspondences while naturally isolating the cranium from peripheral scanning clutter without manual preprocessing. Experiments on infant photogrammetry scans demonstrate that SCALP consistently outperforms state-of-the-art unsupervised point-cloud approaches, offering a clinically practical pathway toward objective, radiation-free head shape analysis.
Read the full abstract ▾
All authors Nawazish Khan, Sanjay Bhandari, Sarang Joshi, Alzbeta Novotna, Tiffany Jeong, Loretta Bowman, Michael Hernandez, Tobi Somorin, Viraj Govani, Jesse Glodstein, Shireen Elhabian
Computer Vision
RadPRISM: Schema-stratified radiology-report supervision for concept-disentangled image representations and visual grounding
Fabian Drexel, Marlene Fritzsche, Era Stambollxhiu et al. (+15 more)
Vision-
language pretraining learns rich medical image representations from radiology reports, but previous model variants commonly operate within a single shared
embedding space, so concept-level structure and interpretability must be recovered post hoc, limiting model transparency and, hence, clinical utility. We introduce RadPRISM, which makes a clinician-defined radiology schema a designated stratification axis: an on-premise large
language model extracts per-concept text spans from free-text reports, and each clinical concept is aligned in its own dedicated visual subspace, turning concept stratification into direct, top-level
alignment supervision. Instantiated on chest radiographs with a 19-concept schema over $203{,}602$ examinations from an internal multi-year archive, RadPRISM improved internal
dataset zero-shot classification from $0.717$ (95% CI, $0.710-0.723$) to $0.868$ (95% CI, $0.863-0.872$) macro AUROC over a matched global-
alignment baseline, performed on par with the purpose-built CARZero reference in external zero-shot classification while substantially outperforming it (up to 4.3-fold) in pointing-game visual grounding. In addition, a radiologist reader study demonstrated concept-stratified
retrieval ability ($0.78$ macro
retrieval correctness rate within rank 3), surfacing disentangled descriptive findings that report-level
retrieval and fixed-label vocabularies cannot express. RadPRISM yields discriminative, spatially faithful, natively concept-stratified representations shaped by and transparently inspectable by clinicians.
Read the full abstract ▾
All authors Fabian Drexel, Marlene Fritzsche, Era Stambollxhiu, Miriam Kumpf, Lena Schmitzer, Lea Schumann, Jannik Kahmann, Friedrich Puttkammer, Johannes Moll, Jannik Lübberstedt, Zeineb Ben Chaaben, Anirudh Narayanan, Cosmin I. Bercea, Sebastian Ziegelmayer, Marcus R. Makowski, Daniel Rueckert, Lisa C. Adams, Keno K. Bressem
Computer Vision · Cross-listed from eess.IV
Automatic LV Localization and Short-Axis Plane Estimation from Arbitrary CMR Slice
Yi Yu, Yixuan Liu, Ziyu Zhang et al. (+4 more)
Accurate estimation of left ventricular (LV) orientation is essential for cardiac
magnetic resonance (CMR) imaging and downstream analysis. Existing methods typically formulate orientation recognition as discrete view classification or rely on multi-slice
geometric intersection, limiting their ability to model continuous 3D orientation and generalize across arbitrary slices. This work introduces a novel paradigm: Joint LV localization and 3D orientation estimation from a single CMR slice. To investigate this setting, representative orientation-aware detection frameworks are adapted to the CMR domain, and their limitations are analyzed. Upon that, we propose the Polar-Coupled Circular (PCC)
embedding that provides a continuous and unambiguous orientation representation to address the limitations. Meanwhile, a scalable
benchmark is constructed through automatic slice sampling from volumetric CMR
segmentation datasets. Extensive experiments on four
datasets demonstrate strong performance, achieving an average mIoU of 86.18% and an average angle deviation of 3.39{\deg}. This study establishes a new task setting for single-slice LV orientation modeling and provides a geometry-consistent framework for spatially informed CMR analysis. Code is available at https://github.com/yuyi1005/cmr-3d-ood.
Read the full abstract ▾
All authors Yi Yu, Yixuan Liu, Ziyu Zhang, Parker Martin, Zhenyu Bu, Yuchi Han, Yuan Xue
Computer Vision · Cross-listed from eess.IV
Two-Stage Teacher-Student Reliable Prior Learning for Robust Underwater Image Enhancement
Yifan Chen, Jiaming Liu, Ye Zheng et al. (+2 more)
Underwater image enhancement (UIE) aims to recover clear images from observations affected by wavelength-dependent absorption, scattering, and spatially nonuniform degradation. Although existing generative methods can handle complex degradations, severe information loss may lead to semantic drift in the restored results. To address this issue, we propose RPL-UIE, a two-stage teacher–student framework for reliable prior learning. In the teacher stage, the network learns reliable and complementary spatial priors characterizing appearance and photometric properties from paired degraded and reference images. In the student stage, the network takes only degraded images as input and learns to emulate the teacher's prior extraction capability, thereby providing more reliable restoration guidance for the enhancement process without requiring reference images at inference. To reduce the prior-learning discrepancy between the teacher and student models, we further develop Residual Prior Refinement
Diffusion (RPRD) and Frequency-Aware Prior Residual Calibration (FPRC). RPRD uses the coarse priors as anchors and progressively predicts the necessary corrections in the residual space. FPRC retains stable low-frequency residual components and selectively modulates high-frequency detail residuals, producing calibrated priors to support high-quality reconstruction. Experiments on multiple UIE
benchmarks demonstrate competitive restoration performance. Downstream
underwater object detection and instance
segmentation experiments further demonstrate the improved utility of enhanced images for visual perception, while tests on real-world data captured by a remotely operated vehicle (ROV) support the robustness and practical applicability of RPL-UIE.
Read the full abstract ▾
All authors Yifan Chen, Jiaming Liu, Ye Zheng, Zhe Sun, Tao Chen
Computer Vision · Cross-listed from cs.LG
Rethinking Pretraining for Specialized Design Data: Evidence from the JONES-19 Cultural Design Dataset
Alexandros Haridis and Charles Zhou
Design and architectural archives encode expert human knowledge in graphical formats, providing a critical testbed for design-inspired Machine Learning (ML) challenges absent with typical computer
vision benchmarks. Building on JONES-19, a small-size image
dataset based on The Grammar of Ornament (London, 1857), we evaluate the discriminative performance of Convolutional Neural Networks (CNNs) in two model training strategies: (a) ImageNet pretraining for domain-general "visual common sense," and (b) learning from scratch on the design data in JONES-19. We find that while domain-general priors improve discriminative performance, learning from scratch augmented with repeated local sampling (multi-crop) effectively recovers these gains. For highly structured design data, local design-driven representations provide sufficient foundation for learning, challenging a reliance on massive general-purpose pretraining. These findings suggest that in specialized design domains, careful curation of smaller high-quality
datasets that capture empirical and formal design principles may prove more effective and informative on the nature of a particular design domain than prioritizing large-scale data collection.
Read the full abstract ▾
Computer Vision
Counting the Cost of War Under Satellite Embargo: Zero-Shot Estimation of Impacted Infrastructure
Saleh Sakib Ahmed and M. Sohel Rahman
Rapid estimation of impacted structures - critical for conflict-zone humanitarian response - is frequently hindered by post-strike satellite data embargoes and imagery blackouts. We bypass this operational bottleneck by reframing impacted building mapping as a zero-shot
geometric projection task on archival, pre-strike maps. Using coordinate and incident text from LiveUAMap and ArcGIS, Large
Language Models extract weapon payloads (W) to project kinetic blast perimeters via Hopkinson-Cranz scaling (R_base = Z * W^(1/3)). To count exposed structures within these zones without post-strike imagery, we introduce two technical innovations: Adaptive Field-of-View to eliminate resolution (zoom) bias in 2D
segmentation (SAMGeo), and 2.5D pseudo-height depth maps combined with
segmentation masks to help Large
Vision-
Language Models (LVLMs) resolve overlapping, dense rooftops. Evaluated on 2026 Middle East conflict data, depth-augmented LVLMs dramatically outperform traditional
segmentation in congested urban centers. This establishes a powerful hybrid paradigm for zero-shot crisis mapping: ultra-fast 2D
segmentation for sparse rural zones, and depth-augmented LVLMs for dense urban environments.
Read the full abstract ▾
Computer Vision · Cross-listed from eess.IV
FDIR: Harmonizing Fidelity and Human-Machine Preference in Lossy Compression Image Restoration
Kuan-Yen Chen, Fang-Yi Su, Philip Chikontwe et al. (+1 more)
Image restoration quality can be evaluated along three complementary facets: pixel-level fidelity, human perception, and downstream machine preference. However, existing lossy compression restoration methods optimize for at most one of these criteria: fidelity-oriented models often regress toward conditional means and produce over-smoothed outputs, while generative approaches hallucinate plausible but factually incorrect textures that degrade both ground-truth fidelity and downstream task accuracy. To navigate this three-way tradeoff, we propose FDIR, a two-stage architecture that decouples the conflicting demands through complementary inductive biases: Quality-Guided One-Step Flow Matching (QO-Flow) recovers global semantic structure in latent space via a single forward pass, while Flow-Conditioned Detail Refinement (FCDR) deterministically restores high-frequency textures and suppresses generative
hallucinations in pixel space. Extensive experiments demonstrate that FDIR achieves superior fidelity, with a favorable perceptual-fidelity balance and competitive machine preference.
Read the full abstract ▾
All authors Kuan-Yen Chen, Fang-Yi Su, Philip Chikontwe, Jung-Hsien Chiang
Computer Vision
Distill What RGB Can Recover: Privileged 3D Evidence for RGB-Only Vision-Language Models
Yanbin Hu, Jin Cui, Jun Ye et al. (+4 more)
3D scene understanding requires
reasoning about entity existence, spatial layout, and object relations, yet RGB images alone often provide insufficient 3D cues. Existing 3D-VLMs commonly rely on depth or 3D-position-aware inputs at inference time, introducing additional acquisition, reconstruction, or annotation costs that limit RGB-only deployment. We therefore study how training-time 3D evidence can be converted into spatial
reasoning capabilities retained under RGB-only inference. We propose a privileged-evidence
distillation framework that constructs a distillable teacher through a unified evidence interface and controlled residual injection, and transfers its knowledge to a deployable student receiving only RGB images and questions through logit and structured representation
distillation. To avoid imitating teacher signals unsupported by RGB, we further introduce evidence-sensitivity-guided
distillation, which uses corrupted evidence to identify highly evidence-dependent targets and down-weight their supervision. We also define a recoverability decomposition based on the matched baseline, teacher, and student, separating privileged gains into RGB-recoverable improvements and residual teacher advantages. Across four
benchmarks, the teacher achieves the best result on 7 of 11 reported metrics among the compared methods. The RGB-only student outperforms its matched baseline on all 11 metrics, including gains of 10.4 ScanQA CIDEr and 19.1 Scan2Cap CIDEr@0.5, without additional inference-time inputs. These results validate the effectiveness of training-time privileged 3D evidence
distillation for both teacher performance and deployable RGB-only spatial
reasoning. Separately, our matched baseline-teacher-student analysis characterizes privileged-gain transfer across evidence types and spatial skills.
Read the full abstract ▾
All authors Yanbin Hu, Jin Cui, Jun Ye, Jiepeng Zhou, Jiangcheng Song, Boran Zhao, Pengju Ren
Computer Vision
What Carries the Signal in Pathology Foundation-Model Atlases? A Patient-Level Controlled Benchmark in Breast Cancer
Chimdi Walter Ndubuisi
Pathology foundation models are reported to encode molecular programmes in tissue morphology, but the evidence is usually a cohort-wide ranked gene list rather than a prediction for a held-out patient. We rebuild such an analysis with the patient as the unit of evidence and ask which pipeline component carries signal. Across 11 frozen backbones, four pre-specified gene programmes and 285 TCGA-BRCA patients with paired slides and RNA-seq (44
cells; GroupKFold by patient, all preprocessing fitted inside the fold), ridge regression on mean-pooled
embeddings predicts held-out programme scores at Spearman rho = 0.25-0.56, UNI2 strongest on all four (immune 0.556). A matched permutation null gives raw p ~ 1e-4 at 10,000 permutations for every
cell; Holm-adjusted p = 0.0044. The signal is real but not uniformly morphological. Against competing models on the same patients and folds,
embeddings beat tissue composition for ER/luminal, proliferation and immune (+0.280, +0.284, +0.479; p <= 0.003) but not basal, where compartment fractions alone reach 0.469 against the
embedding's 0.493 (p = 0.77). Fifty-four interpretable
cell-count features come within 0.043-0.085 on every programme. The
geometric machinery contributes nothing measurable, and we identify why: the geodesic graph selects neighbours by Euclidean nearest-neighbour search and only reweights edges already chosen, so the
topology is Euclidean by construction (Riemannian minus Euclidean = +0.0010, 95% CI [-0.0007, +0.0029]). Applied consistently the geometry is worse (-0.0117). Ridge regression beats the graph-and-metric decoder by +0.097 (CI [+0.069, +0.127]). The driver-count metric common in this literature is near-uninformative here: 91.8% of random six-gene panels recover >=5/6 drivers.
Read the full abstract ▾
Computer Vision
Toward Robust and 3D-Aware RGB-NIR Imaging in the Dark
Muyao Niu, Mingze Ma, Yifan Zhan et al. (+5 more)
Robust low-light imaging remains challenging for the community. Recent studies have explored fusing Near-Infrared (NIR) with noisy RGB to achieve improved enhancement, yet most methods depend on carefully curated training data pairs, with limited robustness under different scenarios. This paper offers a new perspective for RGB-NIR low-light imaging by incorporating 3D-aware neural modeling. Without using clean RGB supervision, a powerful model can be optimized to implicitly fuse extremely noisy RGB observations with NIR cues in 3D space, effectively recovering clean RGB images. The proposed model obviates the requirement for clean RGB data collection, generalizes across different noise levels. Extensive evaluations on synthetic and real data demonstrate its superiority. Codes available: https://github.com/MyNiuuu/3DarkFusion
Read the full abstract ▾
All authors Muyao Niu, Mingze Ma, Yifan Zhan, Qingtian Zhu, Zhihang Zhong, Wei Guo, Chang Wen Chen, Yinqiang Zheng
Computer Vision
Scaling Properties of Text Conditioning in Visual Generation
Zilong Chen, Chaorui Deng, Kunchang Li et al. (+2 more)
We study empirical scaling properties for text conditioning in visual generation. Such properties have rarely been measured because
diffusion loss does not scale with the number of
tokens in natural-
language prompts. Surprisingly, we find that the converged
diffusion loss scales with the amount of structured
language in the
prompt. To quantify structured
language, we adapt two complementary measures: a white-box likelihood metric (GPG) and a black-box attribute metric (ED). Across controlled training runs, the converged
diffusion loss decreases approximately linearly with GPG and follows a power law with ED. Guided by these scaling properties, we improve diffusability by constructing structured
prompts with semantic and
geometric annotations derived from images, and improve promptability by training a prompter through supervised fine-tuning, cold-start, and verifier-gated on-policy
distillation. The resulting system outperforms all evaluated open-weight models on nearly every compositional,
reasoning, and world-knowledge
benchmark, while matching or surpassing the strongest closed-weight models on most evaluations.
Read the full abstract ▾
All authors Zilong Chen, Chaorui Deng, Kunchang Li, Hongyi Yuan, Haoqi Fan
Computer Vision
HierDoc: Hierarchical Page-to-Region Evidence Routing for Long-Document Visual Question Answering
Rongjian Gu, Wengang Zhou, Junyu Xiong et al. (+4 more)
Multi-page document visual question answering requires locating sparse evidence at both the page and region levels. Existing approaches typically emphasize one level over the other: page-centric methods focus on page acquisition, with region operations serving mainly as
navigation aids, whereas region-centric methods assume that the relevant pages have already been supplied. Consequently, page and region selection remain disconnected rather than forming successive evidence decisions. We propose HierDoc, a hierarchical evidence-routing framework that formulates long-document evidence acquisition as two-stage set prediction from pages to regions. A page policy selects evidence pages from the full document; these pages are then parsed for semantic elements, after which a region policy selects the elements passed to a downstream answer model. Both answer-agnostic policies are optimized with stage-wise GRPO using granularity-specific structured-set rewards. The answer model receives selected full pages together with selected region crops and OCR or table text, preserving global context while emphasizing fine-grained evidence. Across the evaluated
benchmarks, HierDoc achieves state-of-the-art or competitive performance among open-weight systems, improving LongDocURL by 16.87% relative to the strongest reported open-weight baseline. Controlled ablations further show that selected regional evidence improves the page-only system in accuracy and F1 by 5.51% and 4.82%, respectively. These results demonstrate the benefit of organizing coarse page routing and fine-grained region routing as successive, separately optimized stages of a unified evidence-acquisition process.
Read the full abstract ▾
All authors Rongjian Gu, Wengang Zhou, Junyu Xiong, Yonghui Wang, Bing Yin, Bei Wang, Houqiang Li
Computer Vision
CodeShrink: Adaptive Visual Compression for Efficient Multimodal Code Understanding
Wenxin Tang, Jingyu Xiao, Zhenyu Liu et al. (+6 more)
Rendering source code as images offers a promising way to reduce the input costs of
Multimodal Large
Language Models (MLLMs). Adjusting image resolution can trade visual
token cost against content fidelity. However, resolution scaling alone overlooks two sources of inefficiency: blank regions created by line breaks and indentation, and code regions irrelevant to the current instruction. Moreover, the best compression setting varies across inputs, tasks, and models, limiting fixed-ratio strategies. We propose CodeShrink, an adaptive visual compression framework with three components. Blank-Free Rendering replaces whitespace-dependent layouts with compact layouts and explicit structural markers, removing layout-induced
tokens. Adaptive Compression Configuration uses a lightweight
agent trained with reinforcement learning to predict a per-input setting that balances
token efficiency and readability. Dominant
Token Selection jointly analyzes the instruction and code image to prune task-irrelevant visual
tokens during inference. We evaluate CodeShrink on code question answering, clone detection, and code completion. CodeShrink reduces visual
token use by up to 71.2% while matching or exceeding uncompressed text-only inputs, and consistently outperforms text-based and visual compression baselines across all three tasks. These results show that combining layout compaction, adaptive configuration, and instruction-aware
pruning can make
multimodal code understanding more efficient. Our code is available at https://github.com/vinsontang1/CodeShrink.
Read the full abstract ▾
All authors Wenxin Tang, Jingyu Xiao, Zhenyu Liu, Zipeng Xie, Junliang Liu, Wang Luo, Yuan Jiang, Yintong Huo, Michael Lyu
Computer Vision
OASIS: Occlusion-aware Single-image Hand Avatar Reconstruction via 3D Gaussian Splatting
Zhisheng Han, Shiyao Wu, Jiayan Qiu et al. (+6 more)
Single-image 3D hand avatar reconstruction is fundamentally ill-posed and particularly challenging due to limited visual evidence under severe self-occlusion and the complex pose-dependent deformation of highly articulated hands. Existing methods predominantly rely on implicit NeRF-style representations, whose volumetric fitting is computationally expensive and often struggles to preserve fine-grained hand details. In this work, we present OASIS, a tailored 3D Gaussian Splatting framework for single-image hand avatar reconstruction. To faithfully encode sparse image-specific appearance cues in single-view reconstruction, we construct geometry-aligned visual evidence
tokens by explicitly aligning input image observations with 3D hand geometry and context-adaptively tokenizing the resulting visual evidence. Since severe self-occlusion makes the reliability of image evidence inherently visibility-dependent, we introduce a visibility-conditioned point-image
attention to reliably transfer visual evidence to
geometric tokens, yielding occlusion-aware Gaussian features for faithful and robust reconstruction. To further capture non-rigid deformation of articulated hands, we introduce a Feature-on-Mesh representation to enable Gaussian deformation to be guided by local surface stretching. Under this framework, we adopt a one-shot adaptation scheme that learns a shared hand prior from multi-identity training data and then fits it to a target image for target-specific reconstruction. Extensive experiments show that OASIS outperforms existing baselines in both visual fidelity and efficiency across challenging poses and in-the-wild scenarios, and further demonstrates strong versatility in downstream applications such as text-to-avatar generation and texture editing.
Read the full abstract ▾
All authors Zhisheng Han, Shiyao Wu, Jiayan Qiu, Yakun Ju, Lu Liu, Le Zhang, Pengfei Feng, Huiyu Zhou, Zheheng Jiang