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Found 7 papers, 3 papers with code
Classification is a Strong Baseline for Deep Metric Learning
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
Learning a Unified Embedding for Visual Search at Pinterest
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.
Large Scale Open-Set Deep Logo Detection
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.
HighEr-Resolution Network for Image Demosaicing and Enhancing
However, plenty of studies have shown that global information is crucial for image restoration tasks like image demosaicing and enhancing.
Shop The Look: Building a Large Scale Visual Shopping System at Pinterest
As online content becomes ever more visual, the demand for searching by visual queries grows correspondingly stronger.
Disentangle Perceptual Learning through Online Contrastive Learning
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.
Billion-Scale Pretraining with Vision Transformers for Multi-Task Visual Representations
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)
