200 citations · 550 across the 28 of their papers we have counts for
10 papers · 1 filter
Token Fusion: Bridging the Gap between Token Pruning and Token Merging
Minchul Kim, Shangqian Gao, Yen-Chang Hsu +2
Vision Transformers (ViTs) have emerged as powerful backbones in computer vision, outperforming many traditional CNNs. However, their computational overhead, largely attributed to…
Continual Diffusion with STAMINA: STack-And-Mask INcremental Adapters
James Seale Smith, Yen-Chang Hsu, Zsolt Kira +2
Recent work has demonstrated a remarkable ability to customize text-to-image diffusion models to multiple, fine-grained concepts in a sequential (i.e., continual) manner while only…
Continual Diffusion: Continual Customization of Text-to-Image Diffusion with C-LoRA
James Seale Smith, Yen-Chang Hsu, Lingyu Zhang +4
Recent works demonstrate a remarkable ability to customize text-to-image diffusion models while only providing a few example images. What happens if you try to customize such model…
A Geometric Perspective towards Neural Calibration via Sensitivity Decomposition
Junjiao Tian, Dylan Yung, Yen-Chang Hsu +1
It is well known that vision classification models suffer from poor calibration in the face of data distribution shifts. In this paper, we take a geometric approach to this problem…
Always Be Dreaming: A New Approach for Data-Free Class-Incremental Learning
James Smith, Yen-Chang Hsu, Jonathan Balloch +3
Modern computer vision applications suffer from catastrophic forgetting when incrementally learning new concepts over time. The most successful approaches to alleviate this forgett…
Memory-Efficient Semi-Supervised Continual Learning: The World is its Own Replay Buffer
James Smith, Jonathan Balloch, Yen-Chang Hsu +1
Rehearsal is a critical component for class-incremental continual learning, yet it requires a substantial memory budget. Our work investigates whether we can significantly reduce t…
Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data
Yen-Chang Hsu, Yilin Shen, Hongxia Jin +1
Deep neural networks have attained remarkable performance when applied to data that comes from the same distribution as that of the training set, but can significantly degrade othe…
A probabilistic constrained clustering for transfer learning and image category discovery
Yen-Chang Hsu, Zhaoyang Lv, Joel Schlosser +2
Neural network-based clustering has recently gained popularity, and in particular a constrained clustering formulation has been proposed to perform transfer learning and image cate…
Learning to Cluster for Proposal-Free Instance Segmentation
Yen-Chang Hsu, Zheng Xu, Zsolt Kira +1
This work proposed a novel learning objective to train a deep neural network to perform end-to-end image pixel clustering. We applied the approach to instance segmentation, which i…
Deep Image Category Discovery using a Transferred Similarity Function
Yen-Chang Hsu, Zhaoyang Lv, Zsolt Kira
Automatically discovering image categories in unlabeled natural images is one of the important goals of unsupervised learning. However, the task is challenging and even human being…