17 citations · 133 across the 50 of their papers we have counts for
13 papers · 1 filter
PartImageNet: A Large, High-Quality Dataset of Parts
Ju He, Shuo Yang, Shaokang Yang +7
It is natural to represent objects in terms of their parts. This has the potential to improve the performance of algorithms for object recognition and segmentation but can also hel…
OOD-CV: A Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images
Bingchen Zhao, Shaozuo Yu, Wufei Ma +6
Enhancing the robustness of vision algorithms in real-world scenarios is challenging. One reason is that existing robustness benchmarks are limited, as they either rely on syntheti…
Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose
Angtian Wang, Shenxiao Mei, Alan Yuille +1
We study the problem of learning to estimate the 3D object pose from a few labelled examples and a collection of unlabelled data. Our main contribution is a learning framework, neu…
A Light-weight Interpretable Compositional Model for Nuclei Detection and Weakly-Supervised Segmentation
Yixiao Zhang, Adam Kortylewski, Qing Liu +9
The field of computational pathology has witnessed great advancements since deep neural networks have been widely applied. These networks usually require large numbers of annotated…
Locally Enhanced Self-Attention: Combining Self-Attention and Convolution as Local and Context Terms
Chenglin Yang, Siyuan Qiao, Adam Kortylewski +1
Self-Attention has become prevalent in computer vision models. Inspired by fully connected Conditional Random Fields (CRFs), we decompose self-attention into local and context term…
Simulated Adversarial Testing of Face Recognition Models
Nataniel Ruiz, Adam Kortylewski, Weichao Qiu +4
Most machine learning models are validated and tested on fixed datasets. This can give an incomplete picture of the capabilities and weaknesses of the model. Such weaknesses can be…
Rethinking Re-Sampling in Imbalanced Semi-Supervised Learning
Ju He, Adam Kortylewski, Shaokang Yang +4
Semi-Supervised Learning (SSL) has shown its strong ability in utilizing unlabeled data when labeled data is scarce. However, most SSL algorithms work under the assumption that the…
A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representation
Jiteng Mu, Weichao Qiu, Adam Kortylewski +3
Recent work has made significant progress on using implicit functions, as a continuous representation for 3D rigid object shape reconstruction. However, much less effort has been d…
Learning Part Segmentation through Unsupervised Domain Adaptation from Synthetic Vehicles
Qing Liu, Adam Kortylewski, Zhishuai Zhang +7
Part segmentations provide a rich and detailed part-level description of objects. However, their annotation requires an enormous amount of work, which makes it difficult to apply s…
TransFG: A Transformer Architecture for Fine-grained Recognition
Ju He, Jie-Neng Chen, Shuai Liu +4
Fine-grained visual classification (FGVC) which aims at recognizing objects from subcategories is a very challenging task due to the inherently subtle inter-class differences. Most…
Understanding Catastrophic Forgetting and Remembering in Continual Learning with Optimal Relevance Mapping
Prakhar Kaushik, Alex Gain, Adam Kortylewski +1
Catastrophic forgetting in neural networks is a significant problem for continual learning. A majority of the current methods replay previous data during training, which violates t…
NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation
Angtian Wang, Adam Kortylewski, Alan Yuille
3D pose estimation is a challenging but important task in computer vision. In this work, we show that standard deep learning approaches to 3D pose estimation are not robust when ob…
CORL: Compositional Representation Learning for Few-Shot Classification
Ju He, Adam Kortylewski, Alan Yuille
Few-shot image classification consists of two consecutive learning processes: 1) In the meta-learning stage, the model acquires a knowledge base from a set of training classes. 2)…