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Tube-sheet endoscopic inspection images pose a challenging defect localization problem because the valid inspection region occupies only part of the frame, while defects are typically small, localized, and visually subtle. This paper proposes an ROI-aware CLIP-based defect-localization framework for tube-sheet endoscopic inspection using ViP2-CLIP.

Drowsiness is a major cause of vehicular and in
dustrial accidents, motivating the need for real-time monitoring
systems that are accurate, non-intrusive, and deployable on low
power platforms. While physiological signal–based approaches
(e.g., EEG/EOG) provide high reliability, their dependence on
sensor attachment makes them intrusive for everyday use.
Camera-based solutions are more practical but face two key
challenges.
First, state-of-the-art deep learning–based face recognition and

Reconstructing thin 3D structures is challenging due to their sparsity, scale variation, and complex geometry. Such structures arise in a wide range of domains, including medical imaging of vascular systems and industrial pipe systems. While recent neural methods perform well on dense surfaces, they often fail to recover fine thin geometries. We propose a reconstruction approach based on local depth projections, which provide an efficient and informative 2D representation of thin structures.

Driver fatigue remains a leading cause of road accidents, responsible for 24% of crashes. While yawning serves as an early behavioral indicator of fatigue, existing approaches face significant challenges due to the presence of systematic noise in video-annotated datasets arising from coarse temporal annotations. Training robust machine learning (ML) models requires rich supervisory labels that help learn salient features from the training data.

Reconstructing complete 3D geometry from monocular clinical endoscopic videos is challenging due to weak texture, repetitive tissue patterns, and severe illumination artifacts. Although emerging dense matching methods exhibit improved resilience to textureless regions, they often produce abundant spurious correspondences across non-overlapping views, which corrupts the correspondence graph and cause structure-from-motion (SfM) pipelines to fail.

Cooperative Perception (CP) supports autonomous driving by enabling connected autonomous vehicles (CAVs) to share sensor data, thereby increasing situational awareness and safety. However, training CP models requires diverse traffic scenarios, and real-world datasets are limited due to the high cost of multi-agent setups and the labor-intensive nature of labeling. Synthetic datasets offer a scalable alternative, but models trained exclusively on virtual data often face domain adaptation challenges.

Zero-shot anomaly detection aims to identify defects in unseen categories without target-specific training. Existing methods usually apply the same feature transformation to all samples, treating normal and anomalous data uniformly despite their fundamentally asymmetric distributions, compact normals versus diverse anomalies. We instead exploit this natural asymmetry by proposing AVA-DINO, an anomaly-aware vision-language adaptation framework with dual specialized branches for normal and anomalous patterns that adapt frozen DINOv3 visual features.

This work introduces a novel variational approach to uncalibrated photometric stereo for robustly recovering 3D surface shape, reflectance, and lighting from images under unknown illumination. Unlike classical methods that require calibration, this framework formulates the task as an unsupervised joint optimization problem for both diffuse and specular reflectance. The proposed method incorporates a specialized depth regularizer that acts separately on interior regions and object boundaries, substantially improving depth and albedo estimation.

Granulometric analysis of wood strands ensures structural integrity and production efficiency of Oriented Strand Board (OSB). Current vision-based studies have demonstrated the potential of automated image analysis for particle size estimation, but their progress is significantly hindered by the absence of public, high-quality, and domain-specific datasets. This paper introduces Granulo-10k, the first curated, open dataset of multiple-view wood-strand imagery designed specifically for research on OSB strand segmentation and multiple-view granulometry, covering height, width, and thickness.

This work addresses a significant gap in existing
image and video databases commonly used in computer vision
applications by introducing a unique and comprehensive
database named Photo-realistic Multi-Distortion Video Dataset
for Benchmarking and Developing Robust Computer Vision
Models (PVD4RCV). A key innovation of PVD4RCV lies in
its incorporation of some relevant physical factors (e.g. depth
information, interaction of light with scene contents) inherent to
video signal acquisition in constrained and complex real-world