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Found 14 papers, 7 papers with code
Quality and Quantity: Unveiling a Million High-Quality Images for Text-to-Image Synthesis in Fashion Design
Moreover, to foster standardization in the T2I-based fashion design field, we propose a new benchmark comprising multiple datasets for evaluating the performance of fashion design models.
Tailored Visions: Enhancing Text-to-Image Generation with Personalized Prompt Rewriting
Despite significant progress in the field, it is still challenging to create personalized visual representations that align closely with the desires and preferences of individual users.
Towards Continual Egocentric Activity Recognition: A Multi-modal Egocentric Activity Dataset for Continual Learning
However, the deficiency of related dataset hinders the development of multi-modal deep learning for egocentric activity recognition.
CafeBoost: Causal Feature Boost To Eliminate Task-Induced Bias for Class Incremental Learning
We place continual learning into a causal framework, based on which we find the task-induced bias is reduced naturally by two underlying mechanisms in task and domain incremental learning.
Self-Paced Deep Regression Forests with Consideration of Ranking Fairness
To answer this question, this paper proposes a new SPL method: easy and underrepresented examples first, for learning DDMs.
Boosting Few-Shot Classification with View-Learnable Contrastive Learning
The goal of few-shot classification is to classify new categories with few labeled examples within each class.
Self-supervised Discriminative Feature Learning for Deep Multi-view Clustering
To leverage the multi-view complementary information, we concatenate all views' embedded features to form the global features, which can overcome the negative impact of some views' unclear clustering structures.
Contrastive Disentanglement in Generative Adversarial Networks
Disentanglement is defined as the problem of learninga representation that can separate the distinct, informativefactors of variations of data.
Auto-view contrastive learning for few-shot image recognition
Few-shot learning aims to recognize new classes with few annotated instances within each category.
Deep Embedded Multi-view Clustering with Collaborative Training
Firstly, the embedded representations of multiple views are learned individually by deep autoencoders.
Self-Paced Deep Regression Forests with Consideration on Underrepresented Examples
Deep discriminative models (e. g. deep regression forests, deep neural decision forests) have achieved remarkable success recently to solve problems such as facial age estimation and head pose estimation.
Self-Paced Deep Regression Forests for Facial Age Estimation
Facial age estimation is an important and challenging problem in computer vision.
Semi-supervised deep embedded clustering
Deep embedded clustering (DEC) is one of the state-of-the-art deep clustering methods.
Latent Dirichlet Allocation in Generative Adversarial Networks
We study the problem of multimodal generative modelling of images based on generative adversarial networks (GANs).
