Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Hajimiri, Sina"'
Despite the significant progress in deep learning for dense visual recognition problems, such as semantic segmentation, traditional methods are constrained by fixed class sets. Meanwhile, vision-language foundation models, such as CLIP, have showcase
Externí odkaz:
http://arxiv.org/abs/2404.08181
Efficient transfer learning (ETL) is receiving increasing attention to adapt large pre-trained language-vision models on downstream tasks with a few labeled samples. While significant progress has been made, we reveal that state-of-the-art ETL approa
Externí odkaz:
http://arxiv.org/abs/2312.12730
This paper introduces a generalized few-shot segmentation framework with a straightforward training process and an easy-to-optimize inference phase. In particular, we propose a simple yet effective model based on the well-known InfoMax principle, whe
Externí odkaz:
http://arxiv.org/abs/2211.14126
In recent years, extending variational autoencoder's framework to learn disentangled representations has received much attention. We address this problem by proposing a framework capable of disentangling class-related and class-independent factors of
Externí odkaz:
http://arxiv.org/abs/2102.00892