#01Nov 9, 2026
cs.CV
TIRAD: Text-guided Image Residual Learning for Generalist Anomaly Detection
Seongsu Lee, Seunghyun Oh, Seungjae Moon and 1 more
Recent anomaly detection studies have shown that residual features, which model the discrepancy between normal and abnormal patterns, provide strong generalization to anomalies unseen during training. However, existing residual-based approaches mainly rely on visual discrepancies derived from image representations, while semantic discrepancies that can be explicitly expressed through language remain largely underexplored. To address this problem, we propose TIRAD, Text-guided Image Residual Learning for Generalist Anomaly Detection, a multimodal residual learning framework that defines the discrepancy between textual representations of normal and abnormal states as a Text Residual Feature and uses it to guide the Image Residual Feature. Specifically, a Text Fusion Module converts patch-level text residuals into a gate that modulates the image residual of each patch. The gate retains the visual discrepancies that the text residual identifies as defect-related and attenuates the others, leading to more discriminative anomaly representations. Extensive experiments on industrial benchmarks and industrial-to-medical transfer demonstrate that the proposed method outperforms existing image-centric residual learning methods.
#02Nov 9, 2026
cs.CV
Four-Step Text-Bearing Image Generation via Distillation with Local Response Regularization and Teacher-Guided Training
Kairui Wen, Sheng Ye, Yong-Jin Liu
Generating text-bearing images requires coherent scenes and accurate characters. Large text-to-image models improve both capabilities through repeated denoising, at substantial sampling cost. Few-step distillation reduces this cost but requires the student to organize scenes and refine glyphs within a short trajectory. We present a four-step distillation framework combining local response regularization with teacher-guided text training. Built on distribution matching, the framework distills the GLM-Image DiT while retaining its autoregressive visual prior, glyph encoder, and VAE. A path-length penalty promotes consistent local responses of the conditional denoising field. Progressive training combines synthetic strings with teacher-generated imagery to preserve character accuracy and scene-integrated typography. The student achieves GenEval Overall 0.84 and CVTG-2K word accuracy 0.5045, compared with 0.81 and 0.1690 for four-step TDM. A coefficient study favors $10^{-5}$, and Qwen-Image experiments show additional gains from response regularization and text supervision. Single-image inference takes 1.742 s, compared with 5.818 s for the teacher, yielding a $3.34\times$ speedup at the same 40 GB GPU memory requirement.
#03Nov 9, 2026
cs.CV
Single Image Super-Resolution via Semantic Prompt and Cross-Modal Training on Content Area
Xiao Tan
Enhancing image quality through super-resolution is crucial in image and video processing. While contemporary deep learning models, such as CNNs and Transformers, excel in pixel-level enhancements, they often overlook the rich semantic context within images. To address this, we introduce a novel approach that synergizes pixel intensity with semantic information for superior detail recovery. Our method involves extracting semantic data, including textual descriptors and spatial positioning, and encoding this information. We also present a Cross-Modal Feature Fusion module for generating semantic feature maps via multimodal training, and a Prompt Traction Head to augment the backbone network with semantic insights, thereby refining image details. Our empirical results across various datasets validate the superiority of our approach.
#04Nov 9, 2026
eess.IV
Leveraging High-Activity Post-Therapeutic I-131 Scans for Deep Learning Reconstruction of Extremely Sparse Planar Images in Differentiated Thyroid Cancer
Boyang Pan, Hang Yang, Jianchen Pan and 4 more
Background: Diagnostic I-131 whole-body planar imaging in differentiated thyroid cancer (DTC) is count-limited by low administered activity, and quality worsens under rapid acquisition. Models trained on such data struggle because their targets lack photon statistics. Purpose: To test whether high-quality post-therapeutic (PT) I-131 scans can serve as superior training references for enhancing sparse diagnostic planar images, using transfer learning and input-image addition. Methods: In this single-center prospective study, 111 patients were enrolled in PT training (n = 19), diagnostic follow-up (FU) training (n = 58), and FU paired test (n = 34) cohorts. Five Restormer-based models were trained: FU_DLx2 on FU labels; PT_DLx2 transfer-learned on PT data; PT_DLx1+ and PT_DLx2+ with input-image addition; PT_DLx6 as a PT-only reference. Test-time processing used two-step denormalization and Poisson re-scaling to preserve noise statistics. Quality was assessed by PSNR, SSIM, and a blinded dual-reader evaluation of all 34 test-cohort patients. Results: PT_DLx2 achieved the highest PSNR (34.4 +/- 3.6 dB) and SSIM (0.81 +/- 0.07). PT_DLx1+ and PT_DLx2+ were comparable (33.8 and 33.3 dB; SSIM 0.80). FU_DLx2 ranked below the unprocessed fast scan (30.3 vs. 32.0 dB; 0.76 vs. 0.79). Subjectively, PT_DLx2+ scored highest in image quality (4.54 +/- 0.60) and diagnostic confidence (4.54 +/- 0.60), surpassing the reference standard (4.19 +/- 0.83 and 4.31 +/- 0.81; P = 0.005 and P = 0.008). FU_DLx2 and PT_DLx6 scored lowest. Conclusion: Count-rich PT scans provide effective training priors for reconstructing extremely sparse images. Input-image addition and cross-domain transfer learning from PT data outperformed direct training on count-limited diagnostic data, supporting 2x scan acceleration with quality matching or exceeding the unaccelerated standard scan for selected PT-derived configurations.
#05Nov 9, 2026
cs.AI
Benchmarking language models and agents for scientific reasoning in evolving materials laboratories
Xuyu Dong, Yixuan Li, Jianguo Huang and 5 more
Large language models and the multi-agent systems built around them could accelerate materials discovery by connecting scientific knowledge with experimental decisions. Existing benchmarks establish capabilities in domain knowledge, multimodal interpretation and scientific tool use. As language models increasingly support materials research workflows, their ability to distinguish closely related material states and reason accurately with quantitative evidence remains insufficiently evaluated. These abilities are essential for comparing material responses, assessing mechanisms and maintaining reliable judgements as experimental evidence accumulates. We introduce MatScope to evaluate three connected capabilities. M1 tests unaided, condition-specific reasoning through 200 tasks spanning ten research functions, using aluminium and high-entropy alloys as representative material systems. M2 tests agent-assisted transfer of related knowledge to unresolved materials questions. MatEvoBench-20 tests initial forecasts across scientific dependency chains and revisions of still-hidden responses as observations accumulate. Across eleven model deployments, including GPT-6 Astra and Claude Fable 5.1, given-input calculations and many physical explanations are handled well. Fine-grained response estimates, comparisons between similar states and quantitatively supported choices remain substantially weaker. Related literature improves selected estimates, while condition and baseline mismatches limit transfer. Progressive observations calibrate some downstream responses and destabilize earlier accurate predictions. MatScope establishes measurable targets for developing reliable scientific reasoning in future self-evolving materials laboratories and materials-aware world models.