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Deep Learning Face Attributes in the Wild

A novel deep learning framework for attribute prediction in the wild that cascades two CNNs, LNet and ANet, which are fine-tuned jointly with attribute tags, but pre-trained differently.
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Pyramid Scene Parsing Network

This paper exploits the capability of global context information by different-region-based context aggregation through the pyramid pooling module together with the proposed pyramid scene parsing network (PSPNet) to produce good quality results on the scene parsing task.
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DeepReID: Deep Filter Pairing Neural Network for Person Re-identification

A novel filter pairing neural network (FPNN) to jointly handle misalignment, photometric and geometric transforms, occlusions and background clutter is proposed and significantly outperforms state-of-the-art methods on this dataset.
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DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations

This work introduces DeepFashion1, a large-scale clothes dataset with comprehensive annotations, and proposes a new deep model, namely FashionNet, which learns clothing features by jointly predicting clothing attributes and landmarks.
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StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks

This paper proposes Stacked Generative Adversarial Networks (StackGAN) to generate 256 photo-realistic images conditioned on text descriptions and introduces a novel Conditioning Augmentation technique that encourages smoothness in the latent conditioning manifold.
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Residual Attention Network for Image Classification

The proposed Residual Attention Network is a convolutional neural network using attention mechanism which can incorporate with state-of-art feed forward network architecture in an end-to-end training fashion and can be easily scaled up to hundreds of layers.
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Deep Learning Face Representation by Joint Identification-Verification

This paper shows that the face identification-verification task can be well solved with deep learning and using both face identification and verification signals as supervision, and the error rate has been significantly reduced.
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Joint Detection and Identification Feature Learning for Person Search

A new deep learning framework for person search that jointly handles pedestrian detection and person re-identification in a single convolutional neural network and converges much faster and better than the conventional Softmax loss.
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Human Reidentification with Transferred Metric Learning

Experiments on the VIPeR dataset and the dataset show that the proposed transferred metric learning significantly outperforms directly matching visual features or using a single generic metric learned from the whole training set.
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Spatial As Deep: Spatial CNN for Traffic Scene Understanding

This paper proposes Spatial CNN (SCNN), which generalizes traditional deep layer- by-layer convolutions to slice-by-slice convolutions within feature maps, thus enabling message passings between pixels across rows and columns in a layer.
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