arXiv AI
Jun 16
Sub-Semantic Image Segmentation
arXiv:2606. 14754v1 Announce Type: cross Abstract: Images can be segmented based on visual cues (i.
Where Does Texture Evidence Live in SAM? Features, Proposal Masks, and Texture Segmentation
arXiv:2606. 14755v1 Announce Type: cross Abstract: Texture segmentation stresses foundation segmentation because meaningful regions are defined by material or repeated appearance rather than object identity.
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arXiv AI
Jun 30
Early Cue Precision Shapes Visual Shortcut Learning in Controlled Cue-Manipulation Benchmarks
arXiv:2606. 30344v1 Announce Type: cross Abstract: Visual classifiers can achieve high matched-distribution accuracy while relying on low-level cues that fail under conflict or suppression.
arXiv AI
Oct 12
When MLLMs miss what humans read: diagnosing and learning to read squint text
The paper introduces the concept of Squint Text—text embedded in an image’s coarse structure that multimodal LLMs often miss but humans can read by squinting. It presents SquintBench, a dataset of 1,076 moderation images, and SquintCtrl, a synthetic suite for diagnosing the issue. The authors show that spatial selection is a key bottleneck and propose Squint OPSD, a training method that uses a downscaled teacher view and privileged transcription to supervise a student model, achieving a significant performance boost on SquintBench.
arXiv Computer Vision
Sep 25
Can Frozen Hyperspherical Features Guide the Selection of Pseudo Masks?
The paper introduces SphereTrust, a method that uses frozen self‑supervised hyperspherical features to evaluate and rank candidate masks produced by foundation segmenters like SAM. By measuring angular contrast, foreground coverage, and image‑frame contact, SphereTrust can select high‑quality masks in 0.55 s per image and outperforms existing baselines on multiple segmentation tasks. The selected masks are then used as priors to train student models, improving performance on several benchmark datasets.
arXiv AI
Jun 12
On the Reliability of Cue Conflict and Beyond
arXiv:2603. 10834v3 Announce Type: replace-cross Abstract: Understanding how neural networks rely on visual cues offers a human-interpretable view of their internal decision processes.
arXiv Computer Vision
Oct 22
LLMSeg: Training-Free Text-Prompted Image Segmentation with B-Splines
arXiv:2610.29409v1 Announce Type: new Abstract: Promptable segmentation models such as SAM 3 achieve remarkable open-vocabulary performance but require dedicated architectures, large-scale annotation...
arXiv Computer Vision
Oct 7
UniCounting: Instance-Aware Proposal Consolidation for Image-Query-Free Multi-Category Counting
arXiv:2610.08379v1 Announce Type: new Abstract: Visual counting is commonly formulated as counting a single specified target, with a model receiving an image-specific exemplar, text query, or target...
arXiv Computer Vision
Oct 8
Masked Feature Encoding for Large-Scale Whole Slide Image Representation
arXiv:2610.10225v1 Announce Type: new Abstract: Whole slide image (WSI) analysis in computational pathology follows a multiple instance learning (MIL) pipeline where patch embeddings are extracted in...
Hugging Face Trending Papers
Jul 12
3D-DefectBench: A Controlled Factorial Study of Vision-Language Model Evaluation Pipelines for Fine-Grained 3D Generation Defects
Automated evaluation is essential for scaling generative 3D systems, where exhaustive human review is costly and slow. However, the reliability of an automated judge depends on the entire evaluation pipeline, not only the underlying vision-language model (VLM), but also how assets are rendered, what visual evidence is provided, how the task is specified, and how human reference labels are constructed.
arXiv Machine Learning
Jun 3
Low-Frequency Shortcuts in Texture-Driven Visual Learning
arXiv:2606. 03493v1 Announce Type: cross Abstract: Neural networks suffer from shortcut learning, where learned features generalize well to the training set but not to in-distribution (ID) or out-of-distribution (OOD) test sets.
arXiv Computer Vision
Aug 27
GraftSR: Grafting Authentic Textures for Real-World Image Super-Resolution via Identical-Instance Guidance
GraftSR is a diffusion-based super‑resolution framework that uses reference images of the same object to guide texture restoration, mitigating hallucination. It introduces a dual‑mask reference guidance mechanism to decouple texture extraction from application, avoiding reliance on spatial alignment. The authors also release TexRefSR‑141K, a large dataset of reference pairs with spatial masks, and show that GraftSR outperforms existing methods on the TexRefSR‑Eval benchmark, reducing LPIPS by 20.2%.
arXiv AI
Jul 14
3D-DefectBench: A Controlled Factorial Study of Vision-Language Model Evaluation Pipelines for Fine-Grained 3D Generation Defects
arXiv:2607. 10826v1 Announce Type: cross Abstract: Automated evaluation is essential for scaling generative 3D systems, where exhaustive human review is costly and slow.