Deep metric learning aims to learn a function mapping image pixels to embedding feature
vectors that model the similarity between images. Two major applications of metric learning …

We study the query-based attack against image retrieval to evaluate its robustness against
adversarial examples under the black-box setting, where the adversary only has query …

Large-scale pretraining of visual representations has led to state-of-the-art performance on a
range of benchmark computer vision tasks, yet the benefits of these techniques at extreme …

In this paper, we propose a multimodal search engine that combines visual and textual cues
to retrieve items from a multimedia database aesthetically similar to the query. The goal of …

Like numerous new websites created globally every day on the web, new spaces are
constantly produced in the metaverse and interconnected to each other through the Internet …

At Pinterest, we utilize image embeddings throughout our search and recommendation
systems to help our users navigate through visual content by powering experiences like …

In this paper, we present GrokNet, a deployed image recognition system for commerce
applications. GrokNet leverages a multi-task learning approach to train a single computer …

The abundance of photos on the internet, along with smartphones that could implement
computer vision technologies allow for a unique way to browse the web. These technologies …

Human activity recognition (HAR) is a popular and challenging research topic, driven by a
variety of applications. More recently, with significant progress in the development of deep …

In modern internet industries, deep learning based recommender systems have became an
indispensable building block for a wide spectrum of applications, such as search engine …