Search Results for author:
Found 14 papers, 9 papers with code
Distilling Vision-Language Foundation Models: A Data-Free Approach via Prompt Diversification
In this paper, we discuss the extension of DFKD to Vision-Language Foundation Models without access to the billion-level image-text datasets.
MEAT: Median-Ensemble Adversarial Training for Improving Robustness and Generalization
Self-ensemble adversarial training methods improve model robustness by ensembling models at different training epochs, such as model weight averaging (WA).
Parameter Exchange for Robust Dynamic Domain Generalization
The parameters of dynamic networks can be decoupled into a static and a dynamic component, which are designed to learn domain-invariant and domain-specific features, respectively.
Periodically Exchange Teacher-Student for Source-Free Object Detection
To address this issue, we propose the Periodically Exchange Teacher-Student (PETS) method, a simple yet novel approach that introduces a multiple-teacher framework consisting of a static teacher, a dynamic teacher, and a student model.
MetaFBP: Learning to Learn High-Order Predictor for Personalized Facial Beauty Prediction
To this end, we propose a novel MetaFBP framework, in which we devise a universal feature extractor to capture the aesthetic commonality and then optimize to adapt the aesthetic individuality by shifting the decision boundary of the predictor via a meta-learning mechanism.
Adapt Anything: Tailor Any Image Classifiers across Domains And Categories Using Text-to-Image Diffusion Models
Such a one-for-all adaptation paradigm allows us to adapt anything in the world using only one text-to-image generator as well as the corresponding unlabeled target data.
Unsupervised Prompt Tuning for Text-Driven Object Detection
In this paper, we delve into this problem and propose an Unsupervised Prompt Tuning framework for text-driven object detection, which is composed of two novel mean teaching mechanisms.
Slimmable Domain Adaptation
In this paper, we introduce a simple framework, Slimmable Domain Adaptation, to improve cross-domain generalization with a weight-sharing model bank, from which models of different capacities can be sampled to accommodate different accuracy-efficiency trade-offs.
Dynamic Domain Generalization
Specifically, we leverage a meta-adjuster to twist the network parameters based on the static model with respect to different data from different domains.
Ranked #26 on
Domain Generalization
on DomainNet
Semi-Supervised Domain Generalization with Evolving Intermediate Domain
From this perspective, we introduce a novel paradigm of DG, termed as Semi-Supervised Domain Generalization (SSDG), to explore how the labeled and unlabeled source domains can interact, and establish two settings, including the close-set and open-set SSDG.
Self-Supervised Noisy Label Learning for Source-Free Unsupervised Domain Adaptation
Usually, the given source domain pre-trained model is expected to optimize with only unlabeled target data, which is termed as source-free unsupervised domain adaptation.
Unsupervised Image Classification for Deep Representation Learning
Extensive experiments on ImageNet dataset have been conducted to prove the effectiveness of our method.
SynSig2Vec: Learning Representations from Synthetic Dynamic Signatures for Real-world Verification
To tackle this issue, this paper proposes to learn dynamic signature representations through ranking synthesized signatures.
SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction
Previous works have formulated the recognition of facial beauty as a specific supervised learning problem of classification, regression or ranking, which indicates that FBP is intrinsically a computation problem with multiple paradigms.
Ranked #2 on
Facial Beauty Prediction
on SCUT-FBP
