Tootfinder

Towards Reliable and Interpretable Document Question Answering via VLMs
Alessio Chen, Simone Giovannini, Andrea Gemelli, Fabio Coppini, Simone Marinai
https://arxiv.org/abs/2509.10129

Towards Reliable and Interpretable Document Question Answering via VLMs
Vision-Language Models (VLMs) have shown strong capabilities in document understanding, particularly in identifying and extracting textual information from complex documents. Despite this, accurately localizing answers within documents remains a major challenge, limiting both interpretability and real-world applicability. To address this, we introduce \textit{DocExplainerV0}, a plug-and-play bounding-box prediction module that decouples answer generation from spatial localization. This design m…

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

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 …

Visual Prompting for Robotic Manipulation with Annotation-Guided Pick-and-Place Using ACT
Muhammad A. Muttaqien, Tomohiro Motoda, Ryo Hanai, Yukiyasu Domae
https://arxiv.org/abs/2508.08748

Visual Prompting for Robotic Manipulation with Annotation-Guided Pick-and-Place Using ACT
Robotic pick-and-place tasks in convenience stores pose challenges due to dense object arrangements, occlusions, and variations in object properties such as color, shape, size, and texture. These factors complicate trajectory planning and grasping. This paper introduces a perception-action pipeline leveraging annotation-guided visual prompting, where bounding box annotations identify both pickable objects and placement locations, providing structured spatial guidance. Instead of traditional ste…

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…

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…

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

New #rstats https://github.com/e-kotov/gridmaker Creates Eurostat GISCO compatible and INSPIRE-compliant grids with IDs that look like ‘CRS3035RES1000mN3497000E4448000’ or ‘1kmN3497E4447’. Input can be sf, or …

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…

VisioFirm: Cross-Platform AI-assisted Annotation Tool for Computer Vision
Safouane El Ghazouali, Umberto Michelucci
https://arxiv.org/abs/2509.04180 https://

VisioFirm: Cross-Platform AI-assisted Annotation Tool for Computer Vision
AI models rely on annotated data to learn pattern and perform prediction. Annotation is usually a labor-intensive step that require associating labels ranging from a simple classification label to more complex tasks such as object detection, oriented bounding box estimation, and instance segmentation. Traditional tools often require extensive manual input, limiting scalability for large datasets. To address this, we introduce VisioFirm, an open-source web application designed to streamline imag…

Model-based Multi-object Visual Tracking: Identification and Standard Model Limitations
Jan Krej\v{c}\'i, Oliver Kost, Yuxuan Xia, Lennart Svensson, Ond\v{r}ej Straka
https://arxiv.org/abs/2508.13647

Model-based Multi-object Visual Tracking: Identification and Standard Model Limitations
This paper uses multi-object tracking methods known from the radar tracking community to address the problem of pedestrian tracking using 2D bounding box detections. The standard point-object (SPO) model is adopted, and the posterior density is computed using the Poisson multi-Bernoulli mixture (PMBM) filter. The selection of the model parameters rooted in continuous time is discussed, including the birth and survival probabilities. Some parameters are selected from the first principles, while …

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…

V2P: From Background Suppression to Center Peaking for Robust GUI Grounding Task
Jikai Chen, Long Chen, Dong Wang, Leilei Gan, Chenyi Zhuang, Jinjie Gu
https://arxiv.org/abs/2508.13634

V2P: From Background Suppression to Center Peaking for Robust GUI Grounding Task
Precise localization of GUI elements is crucial for the development of GUI agents. Traditional methods rely on bounding box or center-point regression, neglecting spatial interaction uncertainty and visual-semantic hierarchies. Recent methods incorporate attention mechanisms but still face two key issues: (1) ignoring processing background regions causes attention drift from the desired area, and (2) uniform labeling fails to distinguish between center and edges of the target UI element, leadin…

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…