As an emerging and challenging problem in the computer vision community, weakly
supervised object localization and detection plays an important role for develo** new …

Object detection in optical remote sensing images, being a fundamental but challenging
problem in the field of aerial and satellite image analysis, plays an important role for a wide …

In this paper, we propose a conceptually simple but very effective attention module for
Convolutional Neural Networks (ConvNets). In contrast to existing channel-wise and spatial …

Most person re-identification (re-ID) approaches are based on supervised learning, which
requires intensive manual annotation for training data. However, it is not only …

Unlike human learning, machine learning often fails to handle changes between training
(source) and test (target) input distributions. Such domain shifts, common in practical …

In this paper, we propose a recurrent framework for joint unsupervised learning of deep
representations and image clusters. In our framework, successive operations in a clustering …

In this paper, we consider the problem of pedestrian detection in natural scenes. Intuitively,
instances of pedestrians with different spatial scales may exhibit dramatically different …

Is strong supervision necessary for learning a good visual representation? Do we really
need millions of semantically-labeled images to train a Convolutional Neural Network …

In this paper we address the problem of automatically recognizing pictured dishes. To this
end, we introduce a novel method to mine discriminative parts using Random Forests (rf) …

Compared to earlier multistage frameworks using CNN features, recent end-to-end deep
approaches for fine-grained recognition essentially enhance the mid-level learning …