Tootfinder

Traceable Evidence Enhanced Visual Grounded Reasoning: Evaluation and Methodology
Haochen Wang, Xiangtai Li, Zilong Huang, Anran Wang, Jiacong Wang, Tao Zhang, Jiani Zheng, Sule Bai, Zijian Kang, Jiashi Feng, Zhuochen Wang, Zhaoxiang Zhang
https://arxiv.org/abs/2507.07999

Traceable Evidence Enhanced Visual Grounded Reasoning: Evaluation and Methodology
Models like OpenAI-o3 pioneer visual grounded reasoning by dynamically referencing visual regions, just like human "thinking with images". However, no benchmark exists to evaluate these capabilities holistically. To bridge this gap, we propose TreeBench (Traceable Evidence Evaluation Benchmark), a diagnostic benchmark built on three principles: (1) focused visual perception of subtle targets in complex scenes, (2) traceable evidence via bounding box evaluation, and (3) second-order reasoning to…

DOBB-BVH: Efficient Ray Traversal by Transforming Wide BVHs into Oriented Bounding Box Trees using Discrete Rotations
Michael A. Kern, Alain Galvan, David Oldcorn, Daniel Skinner, Rohan Mehalwal, Leo Reyes Lozano, Matth\"aus G. Chajdas
https://arxiv.org/abs/2506.22849

DOBB-BVH: Efficient Ray Traversal by Transforming Wide BVHs into Oriented Bounding Box Trees using Discrete Rotations
Oriented bounding box (OBB) bounding volume hierarchies offer a more precise fit than axis-aligned bounding box hierarchies in scenarios with thin elongated and arbitrarily rotated geometry, enhancing intersection test performance in ray tracing. However, determining optimally oriented bounding boxes can be computationally expensive and have high memory requirements. Recent research has shown that pre-built hierarchies can be efficiently converted to OBB hierarchies on the GPU in a bottom-up pa…

Prompt learning with bounding box constraints for medical image segmentation
M\'elanie Gaillochet, Mehrdad Noori, Sahar Dastani, Christian Desrosiers, Herv\'e Lombaert
https://arxiv.org/abs/2507.02743

Prompt learning with bounding box constraints for medical image segmentation
Pixel-wise annotations are notoriously labourious and costly to obtain in the medical domain. To mitigate this burden, weakly supervised approaches based on bounding box annotations-much easier to acquire-offer a practical alternative. Vision foundation models have recently shown noteworthy segmentation performance when provided with prompts such as points or bounding boxes. Prompt learning exploits these models by adapting them to downstream tasks and automating segmentation, thereby reducing …

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/5]:
- OralBBNet: Spatially Guided Dental Segmentation of Panoramic X-Rays with Bounding Box Priors
Budagam, Imanbayev, Akhmetov, Sinitca, Antonov, Kaplun

A recent discussion on open-source Photoshop alternatives (https://mastodon.design/@candidexmedia/114558704815706042) led me down a rabbit hole of image background removal in @…

Collaborative Charging Scheduling via Balanced Bounding Box Methods
Fangting Zhou, Bal\'azs Kulcs\'ar, Jiaming Wu
https://arxiv.org/abs/2506.14461 …

Collaborative Charging Scheduling via Balanced Bounding Box Methods
Electric mobility faces several challenges, most notably the high cost of infrastructure development and the underutilization of charging stations. The concept of shared charging offers a promising solution. The paper explores sustainable urban logistics through horizontal collaboration between two fleet operators and addresses a scheduling problem for the shared use of charging stations. To tackle this, the study formulates a collaborative scheduling problem as a bi-objective nonlinear integer…

Evaluating Integrative Strategies for Incorporating Phenotypic Features in Spatial Transcriptomics
Levin M Moser, Ahmad Kamal Hamid, Esteban Miglietta, Nodar Gogoberidze, Beth A Cimini
https://arxiv.org/abs/2507.22212

Evaluating Integrative Strategies for Incorporating Phenotypic Features in Spatial Transcriptomics
Spatial transcriptomics (ST) technologies not only offer an unprecedented opportunity to interrogate intact biological samples in a spatially informed manner, but also set the stage for integration with other imaging-based modalities. However, how to best exploit spatial context and integrate ST with imaging-based modalities remains an open question. To address this, particularly under real-world experimental constraints such as limited dataset size, class imbalance, and bounding-box-based segm…

Partial Weakly-Supervised Oriented Object Detection
Mingxin Liu, Peiyuan Zhang, Yuan Liu, Wei Zhang, Yue Zhou, Ning Liao, Ziyang Gong, Junwei Luo, Zhirui Wang, Yi Yu, Xue Yang
https://arxiv.org/abs/2507.02751

Partial Weakly-Supervised Oriented Object Detection
The growing demand for oriented object detection (OOD) across various domains has driven significant research in this area. However, the high cost of dataset annotation remains a major concern. Current mainstream OOD algorithms can be mainly categorized into three types: (1) fully supervised methods using complete oriented bounding box (OBB) annotations, (2) semi-supervised methods using partial OBB annotations, and (3) weakly supervised methods using weak annotations such as horizontal boxes o…

🧩 Returns structured output as hierarchical JSON plus ready-to-render #Markdown format
👁️ Optional bounding-box snippets & f
ull-page visualizations for ground-truth verification

Perspective-Invariant 3D Object Detection
Ao Liang, Lingdong Kong, Dongyue Lu, Youquan Liu, Jian Fang, Huaici Zhao, Wei Tsang Ooi
https://arxiv.org/abs/2507.17665 https://

Perspective-Invariant 3D Object Detection
With the rise of robotics, LiDAR-based 3D object detection has garnered significant attention in both academia and industry. However, existing datasets and methods predominantly focus on vehicle-mounted platforms, leaving other autonomous platforms underexplored. To bridge this gap, we introduce Pi3DET, the first benchmark featuring LiDAR data and 3D bounding box annotations collected from multiple platforms: vehicle, quadruped, and drone, thereby facilitating research in 3D object detection fo…