It is demonstrated that separately encoding text and visual instructions, combined with co-training using shallow feature control, is essential to mitigate task ambiguity and enhance multi-task generalization and pave the way for more robust and generalizable low-level vision systems.
Yuandong Pu, Le Zhuo, Kaiwen Zhu et al.· arXiv.org· 19 citations
A unified 3D pancreas segmentation framework that applies domain-adversarial learning to 4,604 heterogeneous CT and MRI scans to learn anatomical representations is developed and demonstrated that a unified anatomical representation can support both cross-modality pancreas segmentation and label-efficient downstream tr...
Zi-Liang Hong, Hong-Yi Pan, H. Aktas et al.· 0 citations
Label-Guided Knowledge Distillation (LGKD) is proposed to guide the distillation of student model features using ground truth labels to enhance the student's ability to learn higher-dimensional semantic information and improving model generalization.
Flat roofs are among the most influential components of the building envelope, governing both structural performance and thermal efficiency, and thereby contributing directly to household energy consumption, carbon emissions, and long-term environmental sustainability. Timely detection of roof defects is essential for...
Samuel Dunthorne, Hashim A. Hashim· Journal of Safety Science an...· 1 citation
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This paper explores unsupervised TBPS, with only unlabeled images, and proposes GTR, a two-stage generation-then-retrieval framework for text-based person search, and contributes LargeFine-Person, a large-scale TBPS dataset with high-quality, fine-grained, and diverse textual annotations.
Mang Ye, Yucheng Ji, Yang Bai et al.· IEEE Transactions on Pattern...· 0 citations
UniPart, a feed-forward cross-modal 3D Transformer that conditions CLIP text embedding, is introduced, a feed-forward cross-modal 3D Transformer that achieves strong zero-shot results on open-vocabulary part benchmarks and transfers to language-conditioned part grasping in real world.
Xinqiang Yu, Zekun Qi, Jia-Wei He et al.· 0 citations
Datasets are a crucial element in the development of perception algorithms. They relate sensor measurement data to annotated reference information and allow for the deduction of sensor and object characteristics. In autonomous driving, the reference data commonly consist of semantic image segmentation, point-wise assoc...
Philipp Berthold, Bianca Forkel, Mirko Maehlisch· 0 citations
Computed tomography (CT) and positron emission tomography (PET) provide complementary anatomical and functional information for cancer diagnosis and treatment planning. However, the widespread use of PET is limited by high radiation exposure, elevated costs, and restricted availability. To address these limitations, de...
Sarita Mourya, Francesco Di Feola, Pierangelo Veltri et al.· 0 citations
This work presents VideoXAgent, a purely online video-agent harness for long video understanding that starts from the given video file and user query, plans and decomposes the task, invokes specialized expert tools on demand, and aggregates multimodal evidence to produce a final answer while resolving conflicts among o...
Sen Yang, Bo-Qiang Duan, Jing Yang et al.· 2 citations
Real-world Earth observation (EO) agents must translate high-level scientific questions into executable workflows to acquire observations, prepare data, perform domain computations, and derive conclusions from runtime evidence. Existing EO agents typically start from supplied observations, while benchmarks typically pr...
Zhu-Tao Lv, Chen-Hao Dang, Yi-Cong Feng et al.· 0 citations
A demonstration showed that a lighting-control policy can be learned and applied by observing only crop images that were generated with LettuceVisSim, providing a proof of concept of vision-based reinforcement learning in CEA using LettuceVisSim.
Zi-Ye Zhu, Bert Ooster, Cong-Cong Sun et al.· 0 citations
Vision-Language Models such as CLIP enable effective few-shot medical anomaly detection (AD) via strong image-text semantic alignment. However, their globally contrastive pretraining lacks explicit spatial supervision, limiting precise lesion localization. In contrast, Vision Foundation Models (VFMs) such as DINO learn...
Juzheng Miao, Yu-Chen Yuan, Cheng Chen et al.· 0 citations
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
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.
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