Zobrazeno 1 - 10
of 539
pro vyhledávání: '"Zhang, Jingyang"'
Generalist segmentation models are increasingly favored for diverse tasks involving various objects from different image sources. Task-Incremental Learning (TIL) offers a privacy-preserving training paradigm using tasks arriving sequentially, instead
Externí odkaz:
http://arxiv.org/abs/2406.19796
The ability to learn sequentially from different data sites is crucial for a deep network in solving practical medical image diagnosis problems due to privacy restrictions and storage limitations. However, adapting on incoming site leads to catastrop
Externí odkaz:
http://arxiv.org/abs/2406.18037
Autor:
Inkawhich, Matthew, Inkawhich, Nathan, Yang, Hao, Zhang, Jingyang, Linderman, Randolph, Chen, Yiran
An object detector's ability to detect and flag \textit{novel} objects during open-world deployments is critical for many real-world applications. Unfortunately, much of the work in open object detection today is disjointed and fails to adequately ad
Externí odkaz:
http://arxiv.org/abs/2404.10865
Autor:
Zhang, Jingyang, Sun, Jingwei, Yeats, Eric, Ouyang, Yang, Kuo, Martin, Zhang, Jianyi, Yang, Hao Frank, Li, Hai
The problem of pre-training data detection for large language models (LLMs) has received growing attention due to its implications in critical issues like copyright violation and test data contamination. Despite improved performance, existing methods
Externí odkaz:
http://arxiv.org/abs/2404.02936
Autor:
Miyai, Atsuyuki, Yang, Jingkang, Zhang, Jingyang, Ming, Yifei, Yu, Qing, Irie, Go, Li, Yixuan, Li, Hai, Liu, Ziwei, Aizawa, Kiyoharu
This paper introduces a novel and significant challenge for Vision Language Models (VLMs), termed Unsolvable Problem Detection (UPD). UPD examines the VLM's ability to withhold answers when faced with unsolvable problems in the context of Visual Ques
Externí odkaz:
http://arxiv.org/abs/2403.20331
Scene graph generation (SGG) of surgical procedures is crucial in enhancing holistically cognitive intelligence in the operating room (OR). However, previous works have primarily relied on the multi-stage learning that generates semantic scene graphs
Externí odkaz:
http://arxiv.org/abs/2402.14461
Autor:
Lu, Yuanxun, Zhang, Jingyang, Li, Shiwei, Fang, Tian, McKinnon, David, Tsin, Yanghai, Quan, Long, Cao, Xun, Yao, Yao
Recent advances in generative AI have unveiled significant potential for the creation of 3D content. However, current methods either apply a pre-trained 2D diffusion model with the time-consuming score distillation sampling (SDS), or a direct 3D diff
Externí odkaz:
http://arxiv.org/abs/2311.15980
Natural Adversarial Examples (NAEs), images arising naturally from the environment and capable of deceiving classifiers, are instrumental in robustly evaluating and identifying vulnerabilities in trained models. In this work, unlike prior works that
Externí odkaz:
http://arxiv.org/abs/2311.12981
Autor:
Zhang, Jingyang, Li, Shiwei, Lu, Yuanxun, Fang, Tian, McKinnon, David, Tsin, Yanghai, Quan, Long, Yao, Yao
We introduce JointNet, a novel neural network architecture for modeling the joint distribution of images and an additional dense modality (e.g., depth maps). JointNet is extended from a pre-trained text-to-image diffusion model, where a copy of the o
Externí odkaz:
http://arxiv.org/abs/2310.06347
Autor:
Gong, Shizhan, Zhong, Yuan, Ma, Wenao, Li, Jinpeng, Wang, Zhao, Zhang, Jingyang, Heng, Pheng-Ann, Dou, Qi
Despite that the segment anything model (SAM) achieved impressive results on general-purpose semantic segmentation with strong generalization ability on daily images, its demonstrated performance on medical image segmentation is less precise and not
Externí odkaz:
http://arxiv.org/abs/2306.13465