Search Results - "Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition"

Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning by Qu, Liangqiong, Zhou, Yuyin, Liang, Paul Pu, Xia, Yingda, Wang, Feifei, Adeli, Ehsan, Fei-Fei, Li, Rubin, Daniel

“…Federated learning is an emerging research paradigm enabling collaborative training of machine learning models among different organizations while keeping data…”
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CRAFT: Concept Recursive Activation FacTorization for Explainability by Fel, Thomas, Picard, Agustin, Bethune, Louis, Boissin, Thibaut, Vigouroux, David, Colin, Julien, Cadenc, Remi, Serre, Thomas

“…Attribution methods, which employ heatmaps to identify the most influential regions of an image that impact model decisions, have gained widespread popularity…”
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Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning by Guo, Pengfei, Wang, Puyang, Zhou, Jinyuan, Jiang, Shanshan, Patel, Vishal M.

“…Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled data is important in many clinical applications. In recent years, deep…”
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Directional Connectivity-based Segmentation of Medical Images by Yang, Ziyun, Farsiu, Sina

“…Anatomical consistency in biomarker segmentation is crucial for many medical image analysis tasks. A promising paradigm for achieving anatomically consistent…”
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Deep Unlearning via Randomized Conditionally Independent Hessians by Mehta, Ronak, Pal, Sourav, Singh, Vikas, Ravi, Sathya N.

“…Recent legislation has led to interest in machine unlearning, i. e., removing specific training samples from a predictive model as if they never existed in the…”
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DiRA: Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysis by Haghighi, Fatemeh, Taher, Mohammad Reza Hosseinzadeh, Gotway, Michael B., Liang, Jianming

“…Discriminative learning, restorative learning, and adversarial learning have proven beneficial for self-supervised learning schemes in computer vision and…”
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Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping by Fu, Huan, Gong, Mingming, Wang, Chaohui, Batmanghelich, Kayhan, Zhang, Kun, Tao, Dacheng

“…Unsupervised domain mapping aims to learn a function GXY to translate domain X to Y in the absence of paired examples. Finding the optimal GXY without paired…”
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Predicting Goal-Directed Human Attention Using Inverse Reinforcement Learning by Yang, Zhibo, Huang, Lihan, Chen, Yupei, Wei, Zijun, Ahn, Seoyoung, Zelinsky, Gregory, Samaras, Dimitris, Hoai, Minh

“…Human gaze behavior prediction is important for behavioral vision and for computer vision applications. Most models mainly focus on predicting free-viewing…”
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Networks for Joint Affine and Non-Parametric Image Registration by Shen, Zhengyang, Han, Xu, Xu, Zhenlin, Niethammer, Marc

“…We introduce an end-to-end deep-learning framework for 3D medical image registration. In contrast to existing approaches, our framework combines two…”
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Learned Representation-Guided Diffusion Models for Large-Image Generation by Graikos, Alexandros, Yellapragada, Srikar, Le, Minh-Quan, Kapse, Saarthak, Prasanna, Prateek, Saltz, Joel, Samaras, Dimitris

“…To synthesize high-fidelity samples, diffusion models typically require auxiliary data to guide the generation process. However, it is impractical to procure…”
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Metric Learning for Image Registration by Niethammer, Marc, Kwitt, Roland, Vialard, Francois-Xavier

“…Image registration is a key technique in medical image analysis to estimate deformations between image pairs. A good deformation model is important for…”
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Robust Histopathology Image Analysis: To Label or to Synthesize? by Hou, Le, Agarwal, Ayush, Samaras, Dimitris, Kurc, Tahsin M., Gupta, Rajarsi R., Saltz, Joel H.

“…Detection, segmentation and classification of nuclei are fundamental analysis operations in digital pathology. Existing state-of-the-art approaches demand…”
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Augmenting Colonoscopy Using Extended and Directional CycleGAN for Lossy Image Translation by Mathew, Shawn, Nadeem, Saad, Kumari, Sruti, Kaufman, Arie

“…Colorectal cancer screening modalities, such as optical colonoscopy (OC) and virtual colonoscopy (VC), are critical for diagnosing and ultimately removing…”
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Calibrating Multi-modal Representations: A Pursuit of Group Robustness without Annotations by You, Chenyu, Mint, Yifei, Dai, Weicheng, Sekhon, Jasjeet S., Staib, Lawrence, Duncan, James S.

“…Fine-tuning pre-trained vision-language models, like CLIP, has yielded success on diverse downstream tasks. However, several pain points persist for this…”
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Seeing Far and Clearly: Mitigating Hallucinations in MLLMs with Attention Causal Decoding by Tang, Feilong, Liu, Chengzhi, Xu, Zhongxing, Hu, Ming, Huang, Zile, Xue, Haochen, Chen, Ziyang, Peng, Zelin, Yang, Zhiwei, Zhou, Sijin, Li, Wenxue, Li, Yulong, Song, Wenxuan, Su, Shiyan, Feng, Wei, Su, Jionglong, Lin, Minquan, Peng, Yifan, Cheng, Xuelian, Razzak, Imran, Ge, Zongyuan

“…Recent advancements in multimodal large language models (MLLMs) have significantly improved performance in visual question answering. However, they often…”
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GradICON: Approximate Diffeomorphisms via Gradient Inverse Consistency by Tian, Lin, Greer, Hastings, Vialard, Francois-Xavier, Kwitt, Roland, Estepar, Raul San Jose, Rushmore, Richard Jarrett, Makris, Nikolaos, Bouix, Sylvain, Niethammer, Marc

“…We present an approach to learning regular spatial transformations between image pairs in the context of medical image registration. Contrary to…”
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MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling by Zhang, Xuzhe, Wu, Yuhao, Angelini, Elsa, Li, Ang, Guo, Jia, Rasmussen, Jerod M., OConnor, Thomas G., Wadhwa, Pathik D., Jackowski, Andrea Parolin, Li, Hai, Posner, Jonathan, Laine, Andrew F., Wang, Yun

“…Robust segmentation is critical for deriving quantitative measures from large-scale, multi-center, and longitudinal medical scans. Manually annotating medical…”
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PrPSeg: Universal Proposition Learning for Panoramic Renal Pathology Segmentation by Deng, Ruining, Liu, Quan, Cui, Can, Yao, Tianyuan, Yue, Jialin, Xiong, Juming, Yu, Lining, Wu, Yifei, Yin, Mengmeng, Wang, Yu, Zhao, Shilin, Tang, Yucheng, Yang, Haichun, Huo, Yuankai

“…Understanding the anatomy of renal pathology is crucial for advancing disease diagnostics, treatment evaluation, and clinical research. The complex kidney…”
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Task Programming: Learning Data Efficient Behavior Representations by Sun, Jennifer J., Kennedy, Ann, Zhan, Eric, Anderson, David J., Yue, Yisong, Perona, Pietro

“…Specialized domain knowledge is often necessary to accurately annotate training sets for in-depth analysis, but can be burdensome and time-consuming to acquire…”
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Adventurer: Optimizing Vision Mamba Architecture Designs for Efficiency by Wang, Feng, Yang, Timing, Yu, Yaodong, Ren, Sucheng, Wei, Guoyizhe, Wang, Angtian, Shao, Wei, Zhou, Yuyin, Yuille, Alan, Xie, Cihang

“…In this work, we introduce the Adventurer series models where we treat images as sequences of patch tokens and employ uni-directional language models to learn…”
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