Through pre-training on extensive text and image datasets, current multi-modal large language models (MLLMs) achieve strong performance on general tasks. However, circuit schematics present a unique challenge for MLLMs due to their dense component layouts and distinct topological logic, demanding fine-grained structura...
Jinyuan Deng, Yuqi Jiang, Wenjing Huang et al.· 0 citations
KaiNinja, a part-level extension of TRELLIS.2 built on a dual-volume form of its O-Voxel representation, is the first 3D generative model trained on agent-authored part data and finds that whole-object fidelity improves over the same backbone fine-tuned on the same dataset.
Rui-Han Yu, Lian Fu, Mu-Yao Niu et al.· 0 citations
ModaLens, a paired image-swap audit, measures how report availability changes image sensitivity and limits conclusions about visual correctness; the direction replicates in two further model lineages.
S. Ordóñez, Maximin Lange, Quang Bui et al.· 1 citation
The VANGUARD team proposes a human-centered multimodal framework that analyses and incorporates the psychological and demographic characteristics of human annotators into the detection pipeline and releases the full pipeline and analysis to support reproducible human-centered modeling.
A. Mocanu, Sebastian Mocanu, Ciprian-Octavian Truica et al.· 0 citations
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VideoScout is proposed, a multi-turn reasoning agent that instantiates the SEA paradigm through adaptive reasoning pacing and enables efficient traversal of long videos within a bounded visual context window, allowing the agent to access more video content while balancing content analysis depth with reading efficiency.
Weixin Xu, Zhenyu Yang, Bing Wang et al.· 0 citations
The model outperformed PET2REP and a CT-based baseline across standard report-generation metrics, improved performance across VQA question types, and achieved higher Dice and lesion-level overlap F1 than SegAnyPET and nnUNet.
Y. Xing, Jiong Wu, Savas Ozdemir et al.· 0 citations
PIVOT, a physics-grounded AIGC detector instantiated here for audio-video clips, that estimates physical quantities from video and audio, selects physical laws relevant to each clip, and verifies their measurable constraints, demonstrates the practical promise of physical-consistency verification as a structured and in...
Bo-Yuan Zheng, Kang-Ran Zhao, Xiao-Yu Zhang et al.· 0 citations
Using food package images to monitor sodium and salt content against South Africa's R214 sodium limits is challenging when screening decisions require product identity, nutrition facts panel evidence, reporting basis, and category-specific thresholds. This study presents a conservative image-based workflow that combine...
Mayimunah Nagayi, A. Khan, Tamryn Frank et al.· 0 citations
Accurate cell detection and classification are crucial for pathological analysis, directly affecting diagnostic accuracy and treatment planning. To capture complex cellular interactions beyond visual appearance within the tumor microenvironment, several approaches have employed graph neural networks to model spatial an...
Ruochen Liu, Yalin Zheng, Jingxin Liu et al.· 0 citations
Experiments on BUS-CoT and IU X-ray datasets demonstrate consistent improvements in diagnostic accuracy, concept consistency, and report quality over strong general-purpose and medical MLLMs, indicating that concept-grounded reasoning better aligns generation with clinical decision processes.
Xin-Yue Xu, Hong-Bin Lin, Juan-Gui Xu et al.· 0 citations
We propose a pre-fine-tuning probing method for Parameter-Efficient Fine-Tuning (PEFT) layer selection, aiming to obtain more stable and higher gains with fewer trainable parameters when adapting large vision--language models (VLMs). Unlike the common practice of applying LoRA and other adapters to all layers at once--...
Qingtao Xia, Jiahua Bao, Siyao Cheng et al.· 0 citations
This paper proposes PACE, a two-stage framework that progressively expands both the representation and trainable parameter spaces, and introduces Focal Embedding Loss, a confidence-adaptive objective that downweights queries with high positive retrieval confidence while emphasizing ambiguous queries with competitive ne...
Yan-Ping Li, Wei Zhou, Ya-Wen Liu et al.· 1 citation
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.
