25 citations · 70 across the 11 of their papers we have counts for
11 papers
ScaleDet: A Scalable Multi-Dataset Object Detector
Yanbei Chen, Manchen Wang, Abhay Mittal +4
Multi-dataset training provides a viable solution for exploiting heterogeneous large-scale datasets without extra annotation cost. In this work, we propose a scalable multi-dataset…
Benchmarking Zero-Shot Recognition with Vision-Language Models: Challenges on Granularity and Specificity
Zhenlin Xu, Yi Zhu, Tiffany Deng +6
This paper presents novel benchmarks for evaluating vision-language models (VLMs) in zero-shot recognition, focusing on granularity and specificity. Although VLMs excel in tasks li…
Compositional Generalization in Unsupervised Compositional Representation Learning: A Study on Disentanglement and Emergent Language
Zhenlin Xu, Marc Niethammer, Colin Raffel
Deep learning models struggle with compositional generalization, i.e. the ability to recognize or generate novel combinations of observed elementary concepts. In hopes of enabling…
SimpleClick: Interactive Image Segmentation with Simple Vision Transformers
Qin Liu, Zhenlin Xu, Gedas Bertasius +1
Click-based interactive image segmentation aims at extracting objects with a limited user clicking. A hierarchical backbone is the de-facto architecture for current methods. Recent…
Improving Dense Contrastive Learning with Dense Negative Pairs
Berk Iskender, Zhenlin Xu, Simon Kornblith +2
Many contrastive representation learning methods learn a single global representation of an entire image. However, dense contrastive representation learning methods such as DenseCL…
iSegFormer: Interactive Segmentation via Transformers with Application to 3D Knee MR Images
Qin Liu, Zhenlin Xu, Yining Jiao +1
We propose iSegFormer, a memory-efficient transformer that combines a Swin transformer with a lightweight multilayer perceptron (MLP) decoder. With the efficient Swin transformer b…
A Deep Network for Joint Registration and Reconstruction of Images with Pathologies
Xu Han, Zhengyang Shen, Zhenlin Xu +5
Registration of images with pathologies is challenging due to tissue appearance changes and missing correspondences caused by the pathologies. Moreover, mass effects as observed fo…
Anatomical Data Augmentation via Fluid-based Image Registration
Zhengyang Shen, Zhenlin Xu, Sahin Olut +1
We introduce a fluid-based image augmentation method for medical image analysis. In contrast to existing methods, our framework generates anatomically meaningful images via interpo…
Robust and Generalizable Visual Representation Learning via Random Convolutions
Zhenlin Xu, Deyi Liu, Junlin Yang +2
While successful for various computer vision tasks, deep neural networks have shown to be vulnerable to texture style shifts and small perturbations to which humans are robust. In…
DeepAtlas: Joint Semi-Supervised Learning of Image Registration and Segmentation
Zhenlin Xu, Marc Niethammer
Deep convolutional neural networks (CNNs) are state-of-the-art for semantic image segmentation, but typically require many labeled training samples. Obtaining 3D segmentations of m…
Networks for Joint Affine and Non-parametric Image Registration
Zhengyang Shen, Xu Han, Zhenlin Xu +1
We introduce an end-to-end deep-learning framework for 3D medical image registration. In contrast to existing approaches, our framework combines two registration methods: an affine…