Det‐LIME is proposed, a detector‐aware, multi‐instance adaptation of Local Interpretable Model Agnostic Explanations that produced instance‐specific, box‐aligned explanations by combining per‐detection weighting, a proximity kernel that emphasizes regions near each box, and Intersection‐over‐Union‐based matching to tra...
Jia-Yi Zhou, David W. Johnston, Brinnae Bent· Marine mammal science· 0 citations
An occlusion-robust target tracking framework that maintains target identity and trajectory continuity through the integration of YOLOv11n object detection, Kalman Filter motion prediction, and occlusion-aware appearance-based re-identification is proposed.
Mais.M Mohammed, Sharifa Mohammed, Hanan Awadh et al.· 0 citations
Oracle bone script (OBS), one of the earliest Chinese writing systems, plays an important role in the study of Chinese etymology. Traditional decipherment relies heavily on domain experts who analyze characters through semantic context and structural evolution. To assist this labor-intensive process, we formulate OBS d...
Yanbin Hou, Biao Xiong, Guojun Xu et al.· 0 citations
The analysis and classification of cultural heritage architectural styles remain challenging due to the complexity of visual images of buildings, which are highly relied on in traditional CNN-based classification approaches in comparison to textual descriptions, and the relative lack of non-western region-specific data...
Ahmed Ammar Kubba, Manar Abu Talib, Iman Ibrahim et al.· 0 citations
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An efficient diffusion framework that jointly diffuses a baseline scan and its follow-up residual, summed to synthesize the follow-up scan, while concurrently predicting a spatial uncertainty map, in a single reverse diffusion process is proposed.
A. Oliveras, Roger Marí, Rafael Redondo et al.· 1 citation· ⚡1
It is shown that conventional in-distribution evaluation can obscure clinically important generalization failures and support explicit cross-dataset testing as a key component of CHD segmentation evaluation, and conventional in-distribution evaluation can obscure clinically important generalization failures.
Aniketh Vijesh, Shrisharanyan Vasu, Abhijit Ramesh et al.· 0 citations
This work presents the VOR Dataset (VORD), the first benchmark dataset providing both paired edited videos and graffiti masks, and introduces VOR-MDSM, the first perception-driven VLM-based scoring model specifically designed for mask-guided VOR.
Hao-Nan Huang, Tian-Rui Qiu, Xiang-Hao Zang et al.· 0 citations
Region-level retrieval aims to align user-specified image regions with relevant regions or textual descriptions, playing a crucial role in realworld applications such as e-commerce product search and RAG. Although recent Large Multimodal Models (LMMs) have made significant strides in multimodal retrieval, they primaril...
Xun Liang, Hong-Hui Yang, Weihang Pan et al.· 0 citations
StackTok is introduced, a training-free selector that treats query relevance as the objective and visual coverage as budget-calibrated support in vision-language models and ranks first among training-free selectors in every tested model--budget setting.
Zhen-Bin Wang, Lei Zhang, Li-Tuan Wang et al.· 0 citations
This work presents the first systematic robustness benchmark for local watermark robustness across 55 image transformations, and finds that signal distortions are often tolerated by the strongest methods, while geometric misalignment and generative local edits can completely impair payload recovery.
Kai Yao, Bence Szilágyi, Sebestyén Kamp et al.· 0 citations
The developed BrainVLM to classify all 12 World Health Organization (WHO) 2021 brain tumor types and demonstrated its utility in preoperative molecular subgroup prediction for adult-type diffuse gliomas, using a multi-center cohort of 632 patients.
Y. Wang, Jian-Wen Chen, Zhou Chen et al.· 0 citations
This work proposes an adaptive diffusion controller that dynamically adjusts the number of steps to generate high-quality images efficiently, without additional model training, by leveraging a mixture of step schedules with varying step sizes.
Kuluhan Binici, Cihan Acar, Shivam Aggarwal et al.· International Conference on...· 0 citations
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
