#01Nov 7, 2026
cs.CV
REDiff-AD: No Training and No Target Masks for Industrial Anomaly Image Generation via Diversity-Driven In-Context MMDiT
Chih-Yu Tsai, Chih-Heng Chang, Jian-Jiun Ding
Industrial anomaly detection is challenged by the scarcity and diversity of real defects, leaving insufficient anomalous data to effectively train downstream models. Existing anomaly generation methods often require product-specific fine-tuning, target mask annotation, or costly procedures to achieve realistic and diverse defects. We propose REDiff-AD, a framework that requires neither model training nor target masks and introduces Rectified Flow MMDiT into industrial anomaly generation for efficient sampling with fewer generation steps. Given a normal product image and a single masked reference anomaly, REDiff-AD employs in-context learning to transfer the defect appearance from the reference into the normal image. Internal attention signals generated during this process are further reused to localize the synthesized anomaly, while edge and color cues refine the localization into pixel-level pseudo-masks. Two lightweight modules further improve generation quality: adaptive reference conditioning enhances defect fidelity, while diversity guidance promotes varied defect patterns without additional training. Experiments demonstrate that REDiff-AD can well address the tradeoff between realism and diversity for anomaly pattern generation and achieve competitive downstream anomaly classification, detection, and localization performance. These results show that REDiff-AD is a simple, efficient, and effective solution for large-scale anomaly data augmentation and pixel-level data construction. Github code: https://github.com/efdefe4-droid/REDiff-AD
#02Nov 7, 2026
cs.CV
Semantic Synthesis over Brute-Force Search: LLM-Guided Transformation Design for Efficient Learning
Usha Shrestha, Radu Timofte, Dmitry Ignatov
Data transformation pipelines shape the training distribution in computer vision, but designing them is a computationally expensive problem. We investigate whether large language models can enable efficient discovery by synthesizing executable transformation programs outside predefined augmentation spaces. We compare nine strategies under a unified training protocol, including standard augmentation policies, brute-force search, direct code generation with two LLMs, and performance-guided generation using LLMs fine-tuned on accuracy-ordered transformation pairs. When the best candidates are retrained for 20 epochs, LLM-generated transformation programs lead on every dataset, reaching 0.8877 accuracy on CIFAR-10, 0.5825 on CIFAR-100, and 0.9588 on SVHN, surpassing brute-force search over 6,000 candidates. The best transform on CIFAR-10 and SVHN comes from a fine-tuned generator using 270 candidates, while the best on CIFAR-100 comes from an off-the-shelf generator using only 10. This needs 22x fewer candidate evaluations in the generation step and 600x fewer for off-the-shelf generations, relative to brute-force search. Beyond transformation discovery, we analyze the reliability of the reduced-budget evaluation commonly used for augmentation search. Correlating one-epoch selection scores with 20-epoch outcomes reveals strong, weak, and absent predictive relationships depending on the dataset and architecture, with correlations ranging from 0.756 to near zero. A positive control indicates that range restriction alone is unlikely to explain the low correlations. Our results indicate that semantic synthesis with code LLMs provides an efficient alternative to search-based transformation discovery and highlight the importance of validating evaluation fidelity when optimizing data transformations.
#03Nov 7, 2026
cs.CV
Interactive Person Retrieval with On-the-Fly Visual Feedback
Lyucheng Qian, John Yuehan Zhang, Pingyu Wang
Traditional text-based person retrieval (TPR) assumes that a witness provides a complete description at once, whereas interactive person retrieval (Inter-PR) refines a partial description through question-answer exchanges. In this setting, the questioner inspects retrieved candidates to decide what evidence to request next, while the answerer sees only the witness-side memory and cannot report candidate-specific visual differences. Existing methods mainly learn offline question orderings, leaving this visual-feedback loop underexplored. To address this limitation, we introduce visual-feedback interactive person retrieval (VFI-PR), a new interactive retrieval paradigm in which retrieved candidates produce visual feedback that becomes evidence for subsequent questioning and re-ranking. To support training and evaluation in this setting, we construct VFI-PEDES, a dataset and evaluation protocol for visual-feedback interaction, and propose VFI-ReID, a framework that learns to use visual feedback during interactive person retrieval. Through the Dynamic Accumulated-evidence Retriever (DAR) and retrieval-aware question optimization, VFI-ReID adapts retrieval and question generation to evolving evidence states, turning visual feedback into complementary evidence for subsequent retrieval. Experiments show that VFI-ReID consistently improves retrieval across VFI-PR interaction stages, demonstrating the utility of visual feedback for interactive person retrieval and outperforming existing Inter-PR baselines.
#04Nov 7, 2026
stat.ME
Causal Outcome Learning with Noisy Proxies
Eric V. Strobl
Modern observational biomedical datasets contain increasingly extensive baseline and outcome measurements, yet detecting a clinically meaningful treatment effect remains difficult because investigators must identify an outcome that captures treatment response while adjusting for confounding distributed across numerous noisy baseline variables. We introduce SCOUT (Score-based Causal Outcome Learning Using Treatment signals), a framework that learns a clinically interpretable, treatment-responsive outcome while adjusting for confounding throughout the learning process. SCOUT constructs low-dimensional adjustment representations of high-dimensional baseline measurements to estimate the average treatment effect (ATE) and conditional average treatment effect (CATE) for the learned outcome. Importantly, the final adjustment space is at most two-dimensional, allowing treatment-effect heterogeneity to be estimated in this space rather than in the full high-dimensional covariate space. SCOUT also introduces a fourth-order statistic for testing treatment-effect heterogeneity that emphasizes unusually large deviations from the ATE. In synthetic and semi-synthetic experiments, the algorithm achieved competitive ATE estimation accuracy and substantially lower CATE estimation error. In an observational study of patients with bipolar disorder, SCOUT identified a clinically recognizable lithium-associated adverse-effect profile, detecting both a significant ATE (Holm-adjusted $p$ = 0.0026) and significant treatment-effect heterogeneity (Holm-adjusted $p$ = 0.033).These results demonstrate how jointly learning outcomes and controlling for confounding can facilitate the discovery of treatment effects in high-dimensional observational data.
#05Nov 7, 2026
cs.CV
Beyond Real or Fake: A Dual-Channel Authenticity and Reasoning Protocol for Photographic Assessment
Xiaoxiao Li, Ruinan Jin, Lili Meng and 3 more
Forensic verification has converged on a binary "real vs. fake" label that confates fully synthetic, tampered, and AI-retouched images despite their very different consequences. We argue that GenAI manipulations decompose along two complementary channels: a camera channel of sensor fingerprints (destroyed by neural processing), and a semantic channel of scene-level coherence (disrupted by content edits). Each manipulation type leaves a distinctive two-channel signature. We instantiate this taxonomy in 2CAP (2-Channel Authenticity Protocol), pairing a contrastively-trained camera encoder with a frozen semantic encoder to serve two applications on a shared backbone: (i) four-class authenticity classification via cross-attention fusion with learnable reliability weights; and (ii) evidence generation, where an authentic image serves as a reference for a query and the two encoders produce per-channel alignment scores together with a patch-level saliency map. These signals are supplied as privileged context to a frozen Vision-Language Model (VLM) through an Observe-Generate-Refine loop, yielding explanations with localization without forensic fine-tuning. On a multi-source benchmark, 2CAP attains the best classification performance, including strong retouching detection where existing methods fail, and improves explanation quality.