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Found 4 papers, 2 papers with code
Learning Semantic Proxies from Visual Prompts for Parameter-Efficient Fine-Tuning in Deep Metric Learning
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
Towards Improved Proxy-based Deep Metric Learning via Data-Augmented Domain Adaptation
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.
Beyond the Deep Metric Learning: Enhance the Cross-Modal Matching with Adversarial Discriminative Domain Regularization
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.
Localize Me Anywhere, Anytime: A Multi-Task Point-Retrieval Approach
The main contribution of our paper is that we use a 3D model reconstructed by a short video as the query to realize 3D-to-3D localization under a multi-task point retrieval framework.
