Search Results for author:
Found 13 papers, 7 papers with code
Adversarial Data Augmentation via Deformation Statistics
Data augmentation or semi-supervised approaches are commonly used to cope with limited labeled training data.
Self-Supervised Multi-Object Tracking with Path Consistency
In this paper, we propose a novel concept of path consistency to learn robust object matching without using manual object identity supervision.
Benchmarking Zero-Shot Recognition with Vision-Language Models: Challenges on Granularity and Specificity
This paper introduces innovative benchmarks to evaluate Vision-Language Models (VLMs) in real-world zero-shot recognition tasks, focusing on the granularity and specificity of prompting text.
ScaleDet: A Scalable Multi-Dataset Object Detector
Our results show that ScaleDet achieves compelling strong model performance with an mAP of 50. 7 on LVIS, 58. 8 on COCO, 46. 8 on Objects365, 76. 2 on OpenImages, and 71. 8 on ODinW, surpassing state-of-the-art detectors with the same backbone.
Ranked #1 on
Object Detection
on OpenImages-v6
(using extra training data)
SimpleClick: Interactive Image Segmentation with Simple Vision Transformers
Although this design is simple and has been proven effective, it has not yet been explored for interactive image segmentation.
Ranked #2 on
Interactive Segmentation
on SBD
Improving Dense Contrastive Learning with Dense Negative Pairs
Many contrastive representation learning methods learn a single global representation of an entire image.
Compositional Generalization in Unsupervised Compositional Representation Learning: A Study on Disentanglement and Emergent Language
In hopes of enabling compositional generalization, various unsupervised learning algorithms have been proposed with inductive biases that aim to induce compositional structure in learned representations (e. g. disentangled representation and emergent language learning).
iSegFormer: Interactive Segmentation via Transformers with Application to 3D Knee MR Images
We propose iSegFormer, a memory-efficient transformer that combines a Swin transformer with a lightweight multilayer perceptron (MLP) decoder.
A Deep Network for Joint Registration and Reconstruction of Images with Pathologies
They are therefore not designed for the registration of images with strong pathologies for example in the context of brain tumors, and traumatic brain injuries.
Robust and Generalizable Visual Representation Learning via Random Convolutions
In this work, we show that the robustness of neural networks can be greatly improved through the use of random convolutions as data augmentation.
Ranked #110 on
Domain Generalization
on PACS
Anatomical Data Augmentation via Fluid-based Image Registration
We introduce a fluid-based image augmentation method for medical image analysis.
DeepAtlas: Joint Semi-Supervised Learning of Image Registration and Segmentation
Specifically, in a one-shot-scenario (with only one manually labeled image) our approach increases Dice scores (%) over an unsupervised registration network by 2. 7 and 1. 8 on the knee and brain images respectively.
Networks for Joint Affine and Non-parametric Image Registration
In contrast to existing approaches, our framework combines two registration methods: an affine registration and a vector momentum-parameterized stationary velocity field (vSVF) model.
Ranked #2 on
Image Registration
on Osteoarthritis Initiative
