118 citations · 256 across the 31 of their papers we have counts for
9 papers · 1 filter
Discovering Novel Biological Traits From Images Using Phylogeny-Guided Neural Networks
Mohannad Elhamod, Mridul Khurana, Harish Babu Manogaran +15
Discovering evolutionary traits that are heritable across species on the tree of life (also referred to as a phylogenetic tree) is of great interest to biologists to understand how…
Towards Open-World Segmentation of Parts
Tai-Yu Pan, Qing Liu, Wei-Lun Chao +1
Segmenting object parts such as cup handles and animal bodies is important in many real-world applications but requires more annotation effort. The largest dataset nowadays contain…
Probabilistic Uncertainty Quantification of Prediction Models with Application to Visual Localization
Junan Chen, Josephine Monica, Wei-Lun Chao +1
The uncertainty quantification of prediction models (e.g., neural networks) is crucial for their adoption in many robotics applications. This is arguably as important as making acc…
Segment Anything Model (SAM) Enhanced Pseudo Labels for Weakly Supervised Semantic Segmentation
Tianle Chen, Zheda Mai, Ruiwen Li +1
Weakly supervised semantic segmentation (WSSS) aims to bypass the need for laborious pixel-level annotation by using only image-level annotation. Most existing methods rely on Clas…
Federated Learning of Shareable Bases for Personalization-Friendly Image Classification
Hong-You Chen, Jike Zhong, Mingda Zhang +5
Personalized federated learning (PFL) aims to harness the collective wisdom of clients' data while building personalized models tailored to individual clients' data distributions.…
Unified Out-Of-Distribution Detection: A Model-Specific Perspective
Reza Averly, Wei-Lun Chao
Out-of-distribution (OOD) detection aims to identify test examples that do not belong to the training distribution and are thus unlikely to be predicted reliably. Despite a plethor…
Unsupervised Adaptation from Repeated Traversals for Autonomous Driving
Yurong You, Cheng Perng Phoo, Katie Z Luo +5
For a self-driving car to operate reliably, its perceptual system must generalize to the end-user's environment -- ideally without additional annotation efforts. One potential solu…
Learning Fractals by Gradient Descent
Cheng-Hao Tu, Hong-You Chen, David Carlyn +1
Fractals are geometric shapes that can display complex and self-similar patterns found in nature (e.g., clouds and plants). Recent works in visual recognition have leveraged this p…
Making Batch Normalization Great in Federated Deep Learning
Jike Zhong, Hong-You Chen, Wei-Lun Chao
Batch Normalization (BN) is widely used in {centralized} deep learning to improve convergence and generalization. However, in {federated} learning (FL) with decentralized data, pri…