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版本更新 · v3arXiv:2609.14856

RAIN: Region-Aware Inversion Network for Semantic Watermark Extraction

Zilai Li

原文摘要

Semantic watermarks for diffusion models embed ownership information into the generative process while preserving perceptual quality, but Gaussian-Shading extraction conventionally requires multi-step diffusion inversion to recover the initial noise. Recent one-step methods show that this cost can be reduced substantially. We study this problem through extended flow matching and conditional regression. The key observation is that, near the high-SNR image endpoint, recovering a useful noise statistic given by the first-step output of the extended flow matching in the high-SNR regime is much simpler than reconstructing the full inverse trajectory, and Gaussian Shading only requires the recovered latent to remain in the correct watermark decision region. Based on this observation, we propose a lightweight, prompt-free extractor that decomposes endpoint recovery into an image-like anchor and a noise-oriented residual, which increases the capability of the model to utilize GPU parallel computation. The resulting method avoids iterative inversion and repeated evaluation of a diffusion-scale U-Net, providing an efficient one-step extraction pipeline with a concise theoretical interpretation. The computational cost of extracting noise is lower than that of both OSI and FARI. The github repo is there: https://github.com/TheLovesOfLadyPurple/RAIN-lightweight-NN-for-one-step-semantic-watermark-extraction

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.14993

Validation of a Facial-Video Digital Hydration Biomarker Score for Tracking Hydration State Transitions in a Real-World Fasting Model

Yudara Kularathne, D. M. J. Prathapa, Thanveer Ahamed, Janindu Athukorala, Thisara Perera

原文摘要

Hydration influences physiology and daily function, yet repeated practical measurement in everyday settings remains limited. We present initial real-world evidence that a facial-video lifestyle digital biomarker score can track the direction of hydration state transitions. Using Ramadan fasting as a model of prolonged fluid abstinence, we assessed whether the score tracks the transition between the fasting (pre-drink) state and the rehydrated (post-drink) state across three validation layers. In the primary cohort (104 participants, 208 videos), the score increased from a mean of 36.6 in the fasting (pre-drink) state to 70.6 in the rehydrated (post-drink) state (change = +33.9, 95% CI [+30.7, +37.1], p < 0.001, Cohen's d = 2.03), with 102 of 104 participants (98.1%) showing an upward shift. In a nested body-weight subgroup (20 participants), body weight increased by +0.74 kg after fluid intake (p < 0.001), confirming fluid uptake and providing supportive physical anchoring. In a nested serum osmolality subgroup (10 participants), osmolality decreased by -5.1 mOsm/kg (p < 0.001), with all post-drink values within the normal clinical range, providing exploratory biological support. An exploratory secondary comparison of water versus electrolyte rehydration showed no statistically significant difference in short-term score change (p = 0.247). This study does not establish a clinical diagnostic test for dehydration. Rather, these findings support the score as a lifestyle digital biomarker for tracking the direction of hydration state change in real-world settings.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.22904

Triple-Phase Sequential Fusion Network with Contrast Enhancement Kinetics for Hepatobiliary-Phase Liver MRI Synthesis

Qiuli Wang, Xinhuan Sun, Fengxi Chen, Yongxu Liu, Jie Cheng, Lin Chen, Jiafei Chen, Siwei Miao 等 12 位作者

原文摘要

Gadoxetate disodium-enhanced MRI is essential for the detection and characterization of hepatocellular carcinoma. However, acquisition of the hepatobiliary phase (HBP) requires a prolonged post-contrast delay, which reduces workflow efficiency and increases the risk of motion artifacts. In this study, we propose a Triple-Phase Sequential Fusion Network (TriPF-Net) with contrast enhancement kinetics to synthesize HBP images by leveraging the sequential information from pre-HBP sequences: while T1-weighted imaging serves as the indispensable baseline, the model adaptively integrates arterial-phase (AP) and venous-phase (VP) features when available. By modeling the tissue-specific contrast uptake and excretion dynamics across these three phases, TriPF-Net ensures robust HBP synthesis even under the stochastic absence of one or both dynamic contrast-enhanced sequences. In addition, clinical variables, including age, sex, total bilirubin, and albumin, are incorporated to enhance physiological consistency. Compared with conventional methods, TriPF-Net achieved superior performance on datasets from two centers. On the first-center dataset, the model achieved an MAE of 10.65, a PSNR of 23.27, and an SSIM of 0.76. On the second-center dataset, the model was trained independently, and the corresponding values were 12.41, 23.11, and 0.78, respectively. Moreover, lesion-level evaluation showed that the synthesized images achieved contrast comparable to that of real HBP images, with no significant difference in CNR (P = 0.2699). Blinded evaluations confirmed synthesized images were visually comparable to real HBP, with TriPF-Net remaining robust to missing input phases. This flexible solution enhances clinical workflow and lesion depiction, and may also serve as an automated quality control reference to flag real HBP acquisitions that warrant closer inspection.

eess.IV · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11697

SurgTAP-3D: A Large-Scale Cross-Surgery Video Dataset with Contact-Aware 3D Motion Tracking

Jiaxin Guo, Wenzhen Dong, Taoyu Wu, Haomin Kuang, Mengya Xu, Kejian Shi, Ziyi Wang, Tongfan Guan 等 12 位作者

原文摘要

Understanding 3D scene dynamics in minimally invasive surgery (MIS) is a fundamental step toward physically intelligent and autonomous surgical systems. However, existing public datasets offer limited scalability and diversity in real-world clinical scenarios. Moreover, annotations typically focus on either tissue or instruments alone, overlooking tool-tissue interactions. This gap limits applications such as augmented reality guidance and autonomous surgery driven by vision-language-action (VLA) models or world models, which demand large-scale, high-quality 3D supervision. In this work, we construct SurgTAP-3D, a large-scale in-vivo pseudo-3D surgical motion tracking dataset and benchmark, comprising 520K frames, 4,549 action clips, and 1.4M 2D/3D point trajectories from diverse robotic and manual procedures under unified action categories. To make annotation scalable, we design a unified framework for surgical 3D point tracking that converts uncalibrated monocular surgical videos into structured 4D annotations through contact-region prompting, active-tool segmentation, dynamic bundle adjustment, and global 4D refinement. This pipeline adapts foundation priors to surgical videos and produces temporally consistent trajectories, pseudo-scale geometry, camera poses, and instrument masks. The dataset captures realistic challenges inherent to surgical environments, including occlusions, smoke, bleeding, and rapid instrument motion. We establish a comprehensive benchmark on SurgPose, SurgT, and STIR, covering both instrument and tissue tracking. We demonstrate instrument displacement estimation, deformable surface reconstruction, and foundation-model fine-tuning. Extensive experiments and analysis demonstrate the effectiveness of our dataset and annotation framework for 3D surgical point tracking, reconstruction, and surgical action understanding. Homepage: https://wrld.github.io/surgtap-3d/

cs.CV · cs.RO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.14068

CurveBench: A Benchmark for Exact Topological Reasoning over Nested Jordan Curves

Amirreza Mohseni, Mona Mohammadi, Morteza Saghafian, Naser Talebizadeh Sardari

原文摘要

We introduce CurveBench, a benchmark for visual topological reasoning. Given an image of disjoint closed curves, a model must recover the tree of adjacent regions rooted at the exterior. The benchmark contains 756 hand-drawn images, ranging from simple configurations to maze-like and densely packed arrangements. Each image has a manually verified reference tree. We evaluate predictions by exact tree matching with the exterior root fixed. Gemini 3.1 Pro, the strongest model in our evaluation, achieves 71.1% exact-match accuracy on CurveBench-Easy and 28.7% on CurveBench-Hard. A Qwen3-VL-8B checkpoint trained using reinforcement learning with verifiable rewards reaches 31.7% on Easy, compared with 21.7% for its Thinking base model at a 16,384-token generation budget. The same comparison yields a gain of 1.1 percentage points on Hard.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08655

Scoring, Not Learning: Decomposing Catalog-Consolidation Gains in Web-Scraped Retail Product Recognition

Kaden Leis, Xiang Ma

原文摘要

Retail product catalogs assembled from web image search are cheap to build and pervasively noisy: queries return packaging shots instead of products, and entries proliferate under names that cannot be distinguished visually. Consolidating such a catalog can look like an alternative to scaling the model. We show this is not a comparison between two ways of improving a recognizer, but between improving a recognizer and changing what counts as correct. We study a public archive of 165,769 cheese images across 1,824 query-named entries. A packaging filter and near-duplicate removal reduce it to a 998-class recognition set on one fixed, group-aware split. Canonicalizing entries to their base type (844 classes) raises top-1 accuracy by 4.0 points. Scoring the unchanged model against the merged label space, by summing its predicted probability within each canonical group, recovers +4.27 points with no retraining (+4.25 over six seeds). Retraining on the consolidated catalog then adds -0.10$\pm$0.53, +0.08$\pm$0.65 and -0.10$\pm$0.65 points at 28, 50 and 88 M parameters, indistinguishable from zero at every capacity ($\pm$ is the across-seed standard deviation). That component is specific, not mechanical: random merges with the identical size profile yield only +0.68 points, and the three backbones agree to within 0.1 points. But it is overwhelmingly category coarsening, not alias correction: across the 37 merged groups, type coarsenings supply 94% of the gain and alias collapses only 7%. We also detect no interaction between consolidation and backbone size: tripling backbone parameters is worth +1.50$\pm$0.57 points on the raw catalog and +1.42$\pm$00.44 on the canonical one, a within-seed interaction of -0.08 [-0.79,+0.63]. On this archive, the reported consolidation gain primarily reflects the evaluation label space, and separating that out costs one evaluation

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.15992

Player Disjoint Multi-Task Tennis Stroke Analysis from Monocular Pose Sequences

Jigyashman Hazarika, Prince Kumar, Karthik Vasu

原文摘要

Analyzing tennis technique from monocular RGB video could make coaching more accessible, but prior methods usually rely on hand-trimmed strokes, test on random splits that leak player identity, or fail on players with different body proportions. We present an end-to-end, player-disjoint framework with an autonomous kinetic stroke event detector, a 78-dimensional causal velocity representation ([X, V]), a 569,095-parameter multi-task Transformer Encoder (E7), exploratory 9-dimensional kinematic deviation profiling, and a heuristic feedback prototype. All models are evaluated with Clean LOPO v2: Leave-One-Player-Out cross-validation over 7 players and 3 seeds, giving 21 outer evaluations and 147 training runs per configuration, with nested inner cross-validation. Against 206 frozen consensus impact events, the detector reaches an F1-score of 85.65% (+3.24% over baseline thresholding) with a mean absolute timing error of 80.6 ms. Under heavy class imbalance, Stroke Type reaches 74.27% accuracy, 65.37% balanced accuracy, and Macro-F1 = 0.6453. Direction reaches 43.90% accuracy, 37.75% balanced accuracy, and Macro-F1 = 0.3141, a 17.9% relative Macro-F1 gain over the static baseline E0, though balanced accuracy is only a little above the 33.3% chance level. Posture Quality reaches 52.13% accuracy, 50.56% balanced accuracy (essentially chance), and Macro-F1 = 0.4232. Single-task specialists did not differ significantly from the joint model on Stroke Type (p = 0.4062) or Posture Quality (p = 0.1406). On Direction the joint model did somewhat better (exact permutation p = 0.0469), but this is suggestive only and does not pass a Bonferroni threshold. Deviation profiling shows a nominal, unadjusted separation between good and bad posture (|d| = 0.341, p = 4.07 x 10^-8), which drives the feedback prototype. We report negative ablations, seeds, and limits as a baseline for pose-based coaching.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07061

Segment What It Can Draw: Querying a Text-to-Image Generator with Its Own Images for Skin Lesion Segmentation

Hongyu Cao, Guangfu Guo, Jinghan Zhang, Dakshak Keerthi Chandra, Ying Zhang, Kunpeng Liu

原文摘要

Skin lesion segmentation without annotated masks requires the target to be specified through other cues. Existing approaches rely on dermoscopy-specific rules, annotation-derived model priors, or user-selected regions. A general text-to-image diffusion transformer offers a text interface to the target, but its concept-word attention is unreliable on dermoscopy images: in the model's attention-output space, the word-derived skin-to-lesion direction is poorly aligned with the direction separating real skin from real lesions. We show that the direction derived from generated healthy-skin and lesion images is substantially better aligned. Based on this finding, we propose GENIQ, a training-free method that constructs initial skin and lesion prototypes from generated images and refines them using the most skin-like and lesion-like patches from unlabelled dermoscopy images. It scores test patches along the refined direction and applies a gated threshold to suppress contextual spill-over around small lesions. Without inference-time annotations or additional models, GENIQ achieves Dice scores of 0.85-0.88 on three dermoscopy test sets. It outperforms zero-shot and word-based attention methods on all three sets, and a hand-designed dermoscopy pipeline in Dice and IoU on two.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04002

Evaluating the Impact of Nonlinear Registration on Longitudinal PET Analysis in a Rodent Stroke Model

Haytham Ziani, El Mostafa Kalmoun, Zohaib Atif, Hyuk-Sang Kwon

原文摘要

Longitudinal rodent positron emission tomography (PET) studies of stroke and neuromodulation rely on accurate registration to a common atlas space to detect group-level effects. Prior work applying ultra-high frequency low-intensity magnetic stimulation (UHF-LiMS) to a rat model of chronic capsule infarct used affine normalization to a Sprague Dawley MRI template without evaluating that choice. We compare a two-stage (affine and nonlinear) pipeline against an affine- only one for rat PET-to-atlas alignment on the same dataset. Across 32 longitudinal [18F]-FDG PET scans (8 rats, 4 timepoints), the nonlinear stage gave a higher Dice similarity coefficient on every scan, and the gain was significant at the rat level. Jacobian determinants showed no folding on the sampling grid. Group-level linear mixed-effects maps of change from baseline (AFNI 3dLME), computed identically under both pipelines, differ in the size, number and location of their suprathreshold clusters. In this dataset, the registration pipeline is therefore a variable that can change the interpretation of downstream group-level analyses.

eess.IV · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02599

Camera-Free Object Discovery and Interaction with GazeTags

Vimal Mollyn, Chris Harrison

原文摘要

We present GazeTags, a camera- and computer-vision-free approach for worn head-gaze object awareness. Our small electronic tags cost roughly \$2 each and can operate for more than a year on a coin cell battery. They work in concert with a user-worn device (smartglasses, hearables, etc.) that periodically emits a focused infrared signal aligned with the user's head direction. Only GazeTags within that narrow head-oriented beam wake and respond, providing their identity and optional payloads such as object state or live sensor readings. By avoiding cameras and computer vision pipelines, our approach is very energy efficient, able to operate continuously even in battery-constrained wearables, and sidesteps non-trivial privacy issues. We describe our hardware design, characterize our system performance and operating tradeoffs, and demonstrate how GazeTags can enable practical gaze-aware environments.

cs.HC · cs.CV · cs.GR · cs.MM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02008

NAS-PO: Native Attention-Strategy Policy Optimization for Vision-Language Models

Ke An, Shizhe Xiang, Yue Liu, Liang Chen, Pei Fu, Shaojie Zhang, Xiaoxing Hu, Yupei Wang

原文摘要

Reinforcement learning with verifiable rewards (RLVR) has become an effective post-training paradigm for vision-language models (VLMs). Standard RLVR, however, assigns identical outcome-derived credit to all generated tokens regardless of their perceptual relevance. Recent perception-aware methods estimate visual reliance through image perturbation or masking. These indirect proxies require perturbation branches, while corruption sensitivity does not necessarily identify the visual evidence used for reasoning. We instead shape model perception through native attention, an intrinsic model signal. Rather than treating attention as a post-hoc explanation, we formulate it as a reward-anchored, token-aligned routing signal that encourages attention patterns supporting visually grounded reasoning. To this end, we propose NAS-PO, a Native Attention-Strategy Policy Optimization framework. NAS-PO analyzes native attention with a visual mass policy that quantifies visual allocation and a conditional visual policy that captures spatial routing over visual tokens, making the optimization well-posed. Based on this, Native Attention-Strategy Advantage Shaping (NAS-AS) draws on the visual-mass policy to redistribute signed policy-gradient credit for visually engaged rollouts. The Adaptive Attention-Coupled (AAC) loss then aligns the visual-mass policy on all response tokens and the conditional visual policy on visually active ones, consolidating the visual-routing patterns that drive successful trajectories. Experiments demonstrate that NAS-PO substantially surpasses leading RL-tuned VLMs across 7 out-of-domain reasoning and perception benchmarks, with its effectiveness consistently validated across 8B and 32B. Visual-Routing Sensitivity (VRS) analysis further indicates that the optimized attention prioritizes visually load-bearing regions.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39014

The Search for Statistical Indistinguishability Between Real and Fake Images

Mohaimin Rafi, Otavio Moraes Rosa, Anshuman Chhabra, Utkarsh Ojha

原文摘要

Images produced by generative models appear strikingly similar to real ones. In this work, we study whether perfection has been reached by generated content in terms of matching the properties of real data. We train deep neural networks to separate real data from fake and observe their test accuracies. Only a chance performance has any scope of proving statistical indistinguishability. However, since generative models are known to sometimes introduce small errors, e.g., low level checkerboard artifacts, we create progressive abstractions of the original real/fake images to potentially bring them to a space where those artifacts get wiped away. These include operations such as blurring, downsizing, cropping, converting RGB images into edge/semantic maps, etc. To our surprise, almost none of the abstractions are able to make fake data indistinguishable from real. The only cases when neural network based classifiers fail to separate the two categories is when the data is abstracted to such an extent that the original content seems mostly lost to the human eye, e.g., downsizing a $256 \times 256$ RGB image of human face to $4 \times 4$ resolution. We observe this trend across four different datasets of varying complexity (MNIST, CelebA, FFHQ and GenImage) and across various types of generative models (GANs, VAEs, diffusion models). This shows that the nature of fake artifacts extends all the way from low level to very high level in images. The simple implication is that fake images are much more different from real than we are used to think.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37619

Perceiving and Predicting Dynamic Sports: Spatiotemporal Intelligence in Vision Models for Egocentric Tennis Video

Tudor Braicu, Joseph DelPreto, Jared Boyer, Mónika Farsang, Maximilian Stölzle, Tim Niklas Seyde, Daniela Rus

原文摘要

Agile robots require spatiotemporal intelligence to interpret scenes, reason about interactions, and anticipate future events. Yet it remains unclear how well foundation vision models support these capabilities, particularly from a moving egocentric camera, which is a natural view for robots. To investigate this, we collect an annotated tennis dataset and train a suite of vision models on spatiotemporal tasks. Our dataset contains egocentric video synchronized with five allocentric videos, estimated global positions, labeled contact times, and annotated stroke types. We then evaluate seven prediction tasks from egocentric images: 2D ego player and 3D ball localization, future player and ball trajectories, forehand or backhand stroke anticipation, time to the next ego player ball contact, and 3D ball position at that contact. We compare image encoders, video encoders, and vision-language models with jointly trained prediction heads. The best performing models achieve mean current-frame localization errors of 0.14 m for the ego player and 0.54 m for the ball, future forecasting errors of 0.34 m for the ego player and 0.91 m for the ball over 1.1 s, and a mean contact-time prediction error of 0.14 s. Auxiliary depth information substantially improves 3D ball localization, suggesting that inferring metric depth remains a challenge. Longer image histories do not improve ball forecasting, indicating that representations exploit scene context rather than rely solely on inter-frame motion. Finally, we propose a conditional diffusion model to generate whole-body kinematic tennis motion from vision encoder predictions on egocentric video at real-time rates, adapting movements as predictions are refined. These results move towards a deeper understanding of how foundation vision models can support real-time dynamic robots using egocentric video.

cs.CV · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37178

INTR++: INterpretable TRansformer with better Accuracy, Interpretability, and Adaptability

Dipanjyoti Paul, Arpita Chowdhury, Maneet Chatterjee, Daichi Kitaguchi, Tanya Berger-Wolf, Tetsuya Sakurai, Wei-Lun Chao

原文摘要

Interpretable Transformers often perform well in domain specific settings but lose effectiveness beyond that. Achieving ViT-like broad applicability requires models with consistent performance across domains. Recent works on Interpretable Transformers, INTR and Prompt-CAM, were developed keeping domain adaptability in mind. Prompt-CAM leverages learnable prompts to promote adaptability. However, these prompts are fixed in nature and therefore struggle to adapt to domains with substantial variation in input image characteristics, resulting in reduced interpretability. To address this limitation, we propose INTR++, which extends Prompt-CAM by incorporating a lightweight Prompt Adapter Module (PAM). PAM enables prompts to be adaptive by enriching them with domain-specific knowledge, thereby ensuring consistent interpretability across diverse domains including those with varying image characteristics such as medical imaging, micro-scopic imaging, and remote sensing imagery. INTR++ retains the standard architecture and end-to-end learning of Interpretable Transformer based models, while adding only a small number of additional parameters compared to Prompt-CAM. We demonstrate that INTR++ delivers consistent improvements in all aspects across domains like animals, plants, medical imaging, microscopic imaging, remote sensing imagery and object-centric data.

cs.CV

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.603ACL Anthology · ACL 2026

Scaling Law for Multimodal Large Language Model Supervised Fine-Tuning

YiFan Zhang, Tao Yu, Feng Li, Chaoyou Fu Nanjing University, Yibo Hu Taobao, Kun Wang, Qingsong Wen, Zhang Zhang Institute of automation, Chinese academy of science, Chinese Academy of Sciences 等 10 位作者

原文摘要

The supervised fine-tuning (SFT) stage is crucial for multimodal large language models (MLLMs), yet a comprehensive scaling law to guide the optimal model-data configuration remains lacking. In this paper, we make an initial attempt to address this gap. First, we theoretically demonstrate that directly computing the optimal computation frontier for MLLM-SFT, as we can for traditional LLMs, is a challenging task. This complexity arises because MLLM-SFT is influenced by a broader range of factors, including model size, LLM pre-training tokens, and MLLM SFT tokens. To tackle this issue, we propose two scaling laws based on LLM paradigms: one applicable when training data volumes are well defined by researchers, and another for cases where models are sourced from open communities with unknown training data. Through theoretical modeling and approximations, we provide researchers with valuable recommendations for optimal resource allocation. Furthermore, we establish a strong correlation ( R^2 = 0.98) between training loss and downstream performance, enabling accurate performance estimation without the need for exhaustive benchmarking. To validate our scaling laws, we construct a testbed of 60 models ranging from 50 million to 8 billion parameters, totaling 1,560 checkpoints. Each checkpoint is evaluated on than 10 MLLM benchmarks, ensuring robust fitting of our formulations.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1CVF · CVPR 2026:cvf:538992fd8878320e456093ff579940b9CVF · CVPR 2026

From Measurement to Mitigation: Quantifying and Reducing Identity Leakage in Image Representation Encoders with Linear Subspace Removal

George, Daniel, Yeh, Charles, Lee, Daniel, Zhang, Yifei

原文摘要

Frozen visual embeddings (e.g., CLIP, DINOv2/v3, SSCD) power retrieval and integrity systems, yet their use on face-containing data is constrained by unmeasured identity leakage and a lack of deployable mitigations. We take an attacker-aware view and contribute: (i) a benchmark of visual embeddings that reports open-set verification at low false-accept rates, a calibrated diffusion-based template inversion check, and face-context attribution with equal-area perturbations; and (ii) propose a one-shot linear projector that removes an estimated identity subspace while preserving the complementary space needed for utility, which for brevity we denote as the identity sanitization projection ISP. Across CelebA-20 and VGGFace2, we show that these encoders are robust under open-set linear probes, with CLIP exhibiting relatively higher leakage than DINOv2/v3 and SSCD, robust to template inversion, and are context-dominant. In addition, we show that ISP drives linear access to near-chance while retaining high non-biometric utility, and transfers across datasets with minor degradation. Our results establish the first attacker-calibrated facial privacy audit of non-FR encoders and demonstrate that linear subspace removal achieves strong privacy guarantees while preserving utility for visual search and retrieval.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:d1ae89fec72bb7175591b47f70095664CVF · CVPR 2026

Thinking with Programming Vision: Towards a Unified View for Thinking with Images

Guo, Zirun, Hong, Minjie, Zhang, Feng, Jia, Kai, Jin, Tao

原文摘要

Multimodal large language models (MLLMs) that "think with images" can interactively use tools to reason about visual inputs, but current approaches often rely on a narrow set of tools with limited real-world necessity and scalability. In this work, we first reveal a critical and previously overlooked weakness: even state-of-the-art MLLMs are surprisingly brittle, showing significant performance degradation on images with simple orientation changes, underscoring the need for more robust tool-based reasoning. To address this, we propose CodeVision, a flexible and scalable "code-as-tool" framework where the model generates code as a universal interface to invoke any image operation, moving beyond fixed tool registries. We train our model using a two-stage methodology, beginning with Supervised Fine-Tuning (SFT) on a high-quality dataset curated for complex, multi-turn tool composition and error recovery, followed by Reinforcement Learning (RL) with a dense process reward function to encourage strategic and efficient tool use. To facilitate this research, we construct new SFT and RL datasets and introduce a challenging new benchmark suite designed to rigorously evaluate robustness to orientation changes and multi-tool reasoning. Experiments on Qwen2.5-VL and Qwen3-VL series show that our approach significantly improves model performance and fosters emergent capabilities such as flexible tool composition, efficient chained execution, and robust error recovery from runtime feedback. Code is available at https://github.com/ByteDance-BandAI/CodeVision.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:d8df959982ce66548f49fc7708f1fc63CVF · CVPR 2026

Selfi: Self-improving Reconstruction Engine via 3D Geometric Feature Alignment

Deng, Youming, Peng, Songyou, Zhang, Junyi, Heal, Kathryn, Sun, Tiancheng, Flynn, John, Marschner, Steve, Chai, Lucy

原文摘要

Novel View Synthesis (NVS) has traditionally relied on models with explicit 3D inductive biases combined with known camera parameters from Structure-from-Motion (SfM) beforehand. Recent vision foundation models like VGGT take an orthogonal approach -- 3D knowledge is gained implicitly through training data and loss objectives, enabling feed-forward prediction of both camera parameters and 3D representations directly from a set of uncalibrated images. While flexible, VGGT features lack explicit multi-view geometric consistency, and we find that improving such 3D feature consistency benefits both NVS and pose estimation tasks. We introduce Selfi, a self-improving 3D reconstruction pipeline via feature alignment, transforming a VGGT backbone into a high-fidelity 3D reconstruction engine by leveraging its own outputs as pseudo-ground-truth. Specifically, we train a lightweight feature adapter using a reprojection-based consistency loss, which distills VGGT outputs into a new geometrically-aligned feature space that captures spatial proximity in 3D. This enables state-of-the-art performance in both NVS and camera pose estimation, demonstrating that feature alignment is a highly beneficial step for downstream 3D reasoning. More details on our project page: https://denghilbert.github.io/selfi

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:eccc032d6bb8cec08de5cc04a4de9f32CVF · CVPR 2026

OASIS: On-Demand Hierarchical Event Memory for Streaming Video Reasoning

Liang, Zhijia, Li, Jiaming, Chen, Weikai, Zhang, Yanhao, Lu, Haonan, Li, Guanbin

原文摘要

Streaming video reasoning requires models to operate in a setting where history grows without bound while meaningful evidence remains scarce. In such a landscape, relevant signal is like an oasis -- small, critical, and easily lost in a desert of redundancy. Enlarging memory only widens the desert; aggressive compression dries up the oasis. The real difficulty lies in discovering where to look, not how much to remember. We therefore introduce OASIS, a novel framework for streaming video reasoning that tackles this challenge through structured, on-demand retrieval. It organizes streaming history into hierarchical events and performs reasoning as controlled refinement -- short-context inference first, followed by semantically grounded retrieval only when uncertainty arises. As the retrieval is driven by high-level intent rather than embedding similarity, the retrieved memory is substantially more accurate and less noisy. Additionally, the mechanism is plug-and-play, training-free, and readily attaches to different streaming MLLM backbones. Experiments across multiple benchmarks and backbones show that OASIS achieves strong gains in long-horizon accuracy and compositional reasoning with bounded token cost and low request delay.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
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Self-Diffusion Driven Blind Imaging

Yang, Yanlong, Luo, Guanxiong

原文摘要

Optical imaging systems are inherently imperfect due to diffraction limits, lens manufacturing tolerances, assembly misalignment, and other physical constraints. In addition, unavoidable camera shake and object motion further introduce non-ideal degradations during acquisition. These aberrations and motion-induced variations are typically unknown, difficult to measure, and costly to model or calibrate in practice. Blind inverse problems offer a promising direction by jointly estimating both the latent image and the unknown degradation kernel. However, existing approaches often suffer from convergence instability, limited prior expressiveness, and sensitivity to hyperparameters. Inspired by recent advances in self-diffusion, we propose DeblurSDI, a zero-shot, self-supervised blind imaging framework that requires no pre-training. DeblurSDI formulates blind image recovery as an iterative reverse self-diffusion process that begins from pure noise and progressively refines both the sharp image and the blur kernel. Extensive experiments on combined optical aberrations and motion blur demonstrate that DeblurSDI consistently outperforms other methods by a substantial margin.

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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗