arXiv:2608. 15802v1 Announce Type: cross Abstract: Out-of-distribution (OOD) detection remains challenging for image classifiers, especially when near-OOD samples lie close to in-distribution (ID) class boundaries.
arXiv:2603. 18481v2 Announce Type: replace-cross Abstract: Out-of-distribution (OOD) detection remains a critical challenge in open-world learning, where models must adapt to evolving data distributions.
arXiv:2608. 08308v1 Announce Type: cross Abstract: Modern vision systems must operate in "open-world" settings, where models must recognize known categories and detect unseen or anomalous content.
arXiv:2605. 07821v2 Announce Type: replace-cross Abstract: Out-of-distribution (OOD) detection is crucial for ensuring the reliability of deep learning models.
The paper introduces a technique for examining Vision Transformers by decomposing each affine layer’s weight matrix with Singular Value Decomposition and projecting activations onto the leading right singular vectors, yielding compact, layer‑intrinsic representations. By fitting class‑conditional density models at each layer, the authors generate per‑class typicality scores that are stacked into two‑dimensional typicality maps, summarizing how class‑specific evidence evolves through the network. From these maps, two post‑hoc out‑of‑distribution detection scores are derived: the Prototype Alignment Score (PAS), which measures agreement with class reference prototypes, and the Multi‑Layer Soft Voting (MLSV) score, which captures cross‑layer consensus without stored prototypes, achieving competitive performance on ViT‑B/16 fine‑tuned on CIFAR‑100 without retraining or OOD exposure.
arXiv:2503. 05169v2 Announce Type: replace Abstract: Applying machine learning to increasingly high-dimensional problems with sparse or biased training data increases the risk that a model is used on inputs outside its training domain.
arXiv:2602. 18094v2 Announce Type: replace-cross Abstract: Existing Visual-Language Models (VLMs) have achieved significant progress by being trained on massive-scale datasets, typically under the assumption that data are independent and identically distributed (IID).
arXiv:2606. 26734v1 Announce Type: cross Abstract: The impact of real-world noise on Open Vocabulary Object Detectors (OV-ODs) remains poorly understood due to their architectural complexity.
Large vision-language models can recognize the objects and attributes in a crowded scene yet assign an attribute to the wrong same-class instance. Generic visual-question-answering accuracy marks the...
arXiv:2608. 16805v1 Announce Type: cross Abstract: Large vision-language models can recognize the objects and attributes in a crowded scene yet assign an attribute to the wrong same-class instance.
Crane is a CLIP‑based framework for zero‑shot anomaly detection that enhances dense localization by adapting the vision encoder with a correlation‑based attention module and conditioning learnable prompts on global image context. It further fuses anomaly‑relevant patch features into the global representation for more sensitive image‑level detection, and a variant called Crane+ leverages DINOv2 spatial correlations for stronger pixel‑level performance. Across seven industrial benchmarks, Crane raises mean image‑level AP by 4.5% and Crane+ boosts mean pixel‑level AUPRO by 9.0%.
arXiv:2607. 01759v1 Announce Type: cross Abstract: Open-vocabulary object detection aims to localize and classify objects beyond the fixed set of categories seen dur ing training.