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Deep Learning Face Attributes in the Wild
TLDR
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.
Pyramid Scene Parsing Network
TLDR
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.
DeepReID: Deep Filter Pairing Neural Network for Person Re-identification
TLDR
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.
DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations
TLDR
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.
StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks
TLDR
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.
Deep Learning Face Representation by Joint Identification-Verification
TLDR
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.
Residual Attention Network for Image Classification
TLDR
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.
Human Reidentification with Transferred Metric Learning
TLDR
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.
Unsupervised Salience Learning for Person Re-identification
TLDR
A novel perspective for person re-identification based on unsupervised salience learning, which applies adjacency constrained patch matching to build dense correspondence between image pairs, which shows effectiveness in handling misalignment caused by large viewpoint and pose variations.
Joint Detection and Identification Feature Learning for Person Search
TLDR
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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