Detection, segmentation, depth and recognition research, plus the vision backbones that keep displacing the last generation.
arXiv:2609.22315v1 Announce Type: new Abstract: Automated segmentation of CT images has become increasingly important to enhance the reliability of simulations through the generation of high fidelity...
arXiv:2609.24075v1 Announce Type: new Abstract: Computer-vision systems used for construction monitoring can degrade under adverse environmental and visual conditions, yet such conditions remain unde...
arXiv:2609.23714v1 Announce Type: new Abstract: Infectious bovine pinkeye is a contagious ocular disease that adversely affects cattle health, welfare, and agricultural productivity. Conventional dia...
arXiv:2609.23815v1 Announce Type: new Abstract: Medical image segmentation models typically rely on large amounts of densely annotated volumetric data, limiting their scalability across tasks and ima...
arXiv:2609.24787v1 Announce Type: new Abstract: Forest inventories increasingly rely on artificial intelligence (AI) models to derive forest attributes from large-scale 3D point clouds. Current model...
arXiv:2609.22687v1 Announce Type: new Abstract: We present PanoSeg3R, a feed-forward framework for 3D panoramic semantic segmentation. Unlike existing methods designed for perspective inputs, PanoSeg...
arXiv:2609.22210v1 Announce Type: new Abstract: SALSA (Semi-Autonomous Literature Summarization Assistant) is an open- source, human-in-the-loop platform for extracting structured scientific datasets...
arXiv:2609.23067v1 Announce Type: new Abstract: Referring surgical video instrument segmentation (RSVIS) aims at segmenting the instrument in a surgical video, given a textual description. Despite re...
arXiv:2605.13165v2 Announce Type: replace Abstract: Long chain-of-thought (Long CoT) reasoning improves performance on multi-step problems, but it also induces overthinking. This inefficiency is espe...
arXiv:2609.23673v1 Announce Type: new Abstract: In vision-based off-road navigation, a robot needs to know not only which obstacles to avoid but also which terrain is better. The first is handled by...
arXiv:2510.22172v2 Announce Type: replace-cross Abstract: The Continuous Integrate-and-Fire (CIF) mechanism provides effective alignment for non-autoregressive (NAR) speech recognition. This mechanis...
arXiv:2509.22650v3 Announce Type: replace Abstract: Most existing approaches to referring segmentation achieve strong performance only through fine-tuning or by composing multiple pre-trained models,...
arXiv:2609.22124v1 Announce Type: new Abstract: This paper describes the system submitted to the UNLP 2026 Shared Task on Multi-Domain Document Understanding. The challenge required extracting precis...
arXiv:2609.24494v1 Announce Type: new Abstract: Accurate monocular depth estimation serves as a core enabler for single camera scene understanding. However, existing self-supervised monocular depth e...
arXiv:2602.06674v2 Announce Type: replace-cross Abstract: High-quality annotated datasets are crucial for advancing machine learning in medical image analysis. However, a critical gap exists: most da...
arXiv:2609.22397v1 Announce Type: cross Abstract: Purpose: Contrast-enhanced breast MRI depends on intravenous gadolinium-based contrast agents, motivating methods that synthesise post-contrast appea...
The paper introduces the Visage Face dataset, containing 16,234 face samples captured in varied classroom conditions such as different angles, lighting, expressions, and partial occlusions. Using a YOLO-based detector, the authors evaluate seven state‑of‑the‑art face recognition models across thirteen configurations, finding that FaceLiVTv2‑M achieves the highest performance with 99.75% Top‑1/Top‑5 accuracy and a 6.459 ms inference time. These findings demonstrate that the Visage Face dataset provides a realistic and challenging benchmark for classroom attendance systems.
arXiv:2506.11784v2 Announce Type: replace Abstract: Vision Transformers (ViTs) are essential in computer vision but are computationally intensive, too. Model quantization, particularly to low bit-wid...
arXiv:2407.09693v3 Announce Type: replace Abstract: The field of Neural-Symbolic (NeSy) systems is growing rapidly. Proposed approaches show great promise in achieving symbiotic unions of neural and...
arXiv:2609.22323v1 Announce Type: cross Abstract: Few-shot learning research is predominantly evaluated on accuracy alone, with limited attention to the parameter and training-sample budgets required...