no code implementations • 12 Aug 2021 • Josh Beal, Hao-Yu Wu, Dong Huk Park, Andrew Zhai, Dmitry Kislyuk
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 scale in complex production systems has been relatively unexplored.
Ranked #26 on Image Classification on ObjectNet (using extra training data)
no code implementations • 24 Jun 2020 • Kangfu Mei, Yao Lu, Qiaosi Yi, Hao-Yu Wu, Juncheng Li, Rui Huang
Perceptual learning approaches like perceptual loss are empirically powerful for such tasks but they usually rely on the pre-trained classification network to provide features, which are not necessarily optimal in terms of visual perception of image transformation.
no code implementations • 18 Jun 2020 • Raymond Shiau, Hao-Yu Wu, Eric Kim, Yue Li Du, Anqi Guo, Zhiyuan Zhang, Eileen Li, Kunlong Gu, Charles Rosenberg, Andrew Zhai
As online content becomes ever more visual, the demand for searching by visual queries grows correspondingly stronger.
1 code implementation • 19 Nov 2019 • Kangfu Mei, Juncheng Li, Jiajie Zhang, Hao-Yu Wu, Jie Li, Rui Huang
However, plenty of studies have shown that global information is crucial for image restoration tasks like image demosaicing and enhancing.
1 code implementation • 18 Nov 2019 • Muhammet Bastan, Hao-Yu Wu, Tian Cao, Bhargava Kota, Mehmet Tek
We present an open-set logo detection (OSLD) system, which can detect (localize and recognize) any number of unseen logo classes without re-training; it only requires a small set of canonical logo images for each logo class.
no code implementations • 5 Aug 2019 • Andrew Zhai, Hao-Yu Wu, Eric Tzeng, Dong Huk Park, Charles Rosenberg
The solution we present not only allows us to train for multiple application objectives in a single deep neural network architecture, but takes advantage of correlated information in the combination of all training data from each application to generate a unified embedding that outperforms all specialized embeddings previously deployed for each product.
2 code implementations • 30 Nov 2018 • Andrew Zhai, Hao-Yu Wu
Deep metric learning aims to learn a function mapping image pixels to embedding feature vectors that model the similarity between images.
Ranked #3 on Image Retrieval on CARS196