The deep learning technology has shown impressive performance in various vision tasks
such as image classification, object detection and semantic segmentation. In particular …

Attention mechanism has demonstrated great potential in fine-grained visual recognition
tasks. In this paper, we present a counterfactual attention learning method to learn more …

In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …

What does a neural network encode about a concept as we traverse through the layers?
Interpretability in machine learning is undoubtedly important, but the calculations of neural …

Contexts play an important role in the saliency detection task. However, given a context
region, not all contextual information is helpful for the final task. In this paper, we propose a …

Abstract Since Convolutional Neural Network (CNN) won the image classification
competition 202 (ILSVRC12), a lot of attention has been paid to deep layer CNN study. The …

Fine-grained recognition poses the unique challenge of capturing subtle inter-class
differences under considerable intra-class variances (eg, beaks for bird species) …

Abstract Convolutional Neural Networks define an exceptionallypowerful class of model, but
are still limited by the lack of abilityto be spatially invariant to the input data in a …

This paper introduces the Deep Recurrent Attentive Writer (DRAW) architecture for image
generation with neural networks. DRAW networks combine a novel spatial attention …

In this work we introduce a fully end-to-end approach for action detection in videos that
learns to directly predict the temporal bounds of actions. Our intuition is that the process of …