849 citations · 1k across the 18 of their papers we have counts for
18 papers
Open-Vocabulary Object Detection with Meta Prompt Representation and Instance Contrastive Optimization
Zhao Wang, Aoxue Li, Fengwei Zhou +2
Classical object detectors are incapable of detecting novel class objects that are not encountered before. Regarding this issue, Open-Vocabulary Object Detection (OVOD) is proposed…
Explore and Exploit the Diverse Knowledge in Model Zoo for Domain Generalization
Yimeng Chen, Tianyang Hu, Fengwei Zhou +2
The proliferation of pretrained models, as a result of advancements in pretraining techniques, has led to the emergence of a vast zoo of publicly available models. Effectively util…
Heavy-Tailed Regularization of Weight Matrices in Deep Neural Networks
Xuanzhe Xiao, Zeng Li, Chuanlong Xie +1
Unraveling the reasons behind the remarkable success and exceptional generalization capabilities of deep neural networks presents a formidable challenge. Recent insights from rando…
Fair-CDA: Continuous and Directional Augmentation for Group Fairness
Rui Sun, Fengwei Zhou, Zhenhua Dong +6
In this work, we propose {\it Fair-CDA}, a fine-grained data augmentation strategy for imposing fairness constraints. We use a feature disentanglement method to extract the feature…
ZooD: Exploiting Model Zoo for Out-of-Distribution Generalization
Qishi Dong, Awais Muhammad, Fengwei Zhou +5
Recent advances on large-scale pre-training have shown great potentials of leveraging a large set of Pre-Trained Models (PTMs) for improving Out-of-Distribution (OoD) generalizatio…
Your Contrastive Learning Is Secretly Doing Stochastic Neighbor Embedding
Tianyang Hu, Zhili Liu, Fengwei Zhou +2
Contrastive learning, especially self-supervised contrastive learning (SSCL), has achieved great success in extracting powerful features from unlabeled data. In this work, we contr…
MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps
Muhammad Awais, Fengwei Zhou, Chuanlong Xie +3
Deep neural networks are susceptible to adversarially crafted, small and imperceptible changes in the natural inputs. The most effective defense mechanism against these examples is…
NAS-OoD: Neural Architecture Search for Out-of-Distribution Generalization
Haoyue Bai, Fengwei Zhou, Lanqing Hong +3
Recent advances on Out-of-Distribution (OoD) generalization reveal the robustness of deep learning models against distribution shifts. However, existing works focus on OoD algorith…
Adversarial Robustness for Unsupervised Domain Adaptation
Muhammad Awais, Fengwei Zhou, Hang Xu +4
Extensive Unsupervised Domain Adaptation (UDA) studies have shown great success in practice by learning transferable representations across a labeled source domain and an unlabeled…
DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture
Kaichen Zhou, Lanqing Hong, Shoukang Hu +4
Automated machine learning (AutoML) usually involves several crucial components, such as Data Augmentation (DA) policy, Hyper-Parameter Optimization (HPO), and Neural Architecture…
OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalization
Nanyang Ye, Kaican Li, Haoyue Bai +5
Deep learning has achieved tremendous success with independent and identically distributed (i.i.d.) data. However, the performance of neural networks often degenerates drastically…
Relaxed Conditional Image Transfer for Semi-supervised Domain Adaptation
Qijun Luo, Zhili Liu, Lanqing Hong +7
Semi-supervised domain adaptation (SSDA), which aims to learn models in a partially labeled target domain with the assistance of the fully labeled source domain, attracts increasin…
MetaAugment: Sample-Aware Data Augmentation Policy Learning
Fengwei Zhou, Jiawei Li, Chuanlong Xie +4
Automated data augmentation has shown superior performance in image recognition. Existing works search for dataset-level augmentation policies without considering individual sample…
DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation
Haoyue Bai, Rui Sun, Lanqing Hong +5
While deep learning demonstrates its strong ability to handle independent and identically distributed (IID) data, it often suffers from out-of-distribution (OoD) generalization, wh…
Multi-objective Neural Architecture Search via Non-stationary Policy Gradient
Zewei Chen, Fengwei Zhou, George Trimponias +1
Multi-objective Neural Architecture Search (NAS) aims to discover novel architectures in the presence of multiple conflicting objectives. Despite recent progress, the problem of ap…
Formulating Camera-Adaptive Color Constancy as a Few-shot Meta-Learning Problem
Steven McDonagh, Sarah Parisot, Fengwei Zhou +4
Digital camera pipelines employ color constancy methods to estimate an unknown scene illuminant, in order to re-illuminate images as if they were acquired under an achromatic light…
Deep Meta-Learning: Learning to Learn in the Concept Space
Fengwei Zhou, Bin Wu, Zhenguo Li
Few-shot learning remains challenging for meta-learning that learns a learning algorithm (meta-learner) from many related tasks. In this work, we argue that this is due to the lack…
Meta-SGD: Learning to Learn Quickly for Few-Shot Learning
Zhenguo Li, Fengwei Zhou, Fei Chen +1
Few-shot learning is challenging for learning algorithms that learn each task in isolation and from scratch. In contrast, meta-learning learns from many related tasks a meta-learne…