1 code implementation • 4 Feb 2024 • Li Ren, Chen Chen, Liqiang Wang, Kien Hua
As a result of the success of recent pre-trained models trained from larger-scale datasets, it is challenging to adapt the model to the DML tasks in the local data domain while retaining the previously gained knowledge.
Ranked #2 on Image Retrieval on iNaturalist
1 code implementation • 1 Jan 2024 • Li Ren, Chen Chen, Liqiang Wang, Kien Hua
Our experiments on benchmarks, including the popular CUB-200-2011, CARS196, Stanford Online Products, and In-Shop Clothes Retrieval, show that our learning algorithm significantly improves the existing proxy losses and achieves superior results compared to the existing methods.
no code implementations • 20 Oct 2023 • Jacob Galajda, Kien Hua
We explore the possibility of learning a specific individual's creative reasoning in order to leverage the learned expertise and talent to invent new information.
no code implementations • 23 Oct 2020 • Li Ren, Kai Li, Liqiang Wang, Kien Hua
In this paper, we address this limitation with an efficient learning objective that considers the discriminative feature distributions between the visual objects and sentence words.