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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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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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WIDER FACE: A Face Detection Benchmark

There is a gap between current face detection performance and the real world requirements, and the WIDER FACE dataset, which is 10 times larger than existing datasets is introduced, which contains rich annotations, including occlusions, poses, event categories, and face bounding boxes.
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SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

SegFormer is presented, a simple, efficient yet powerful semantic segmentation framework which unifies Transformers with lightweight multilayer perceptron (MLP) decoders and shows excellent zero-shot robustness on Cityscapes-C.
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A large-scale car dataset for fine-grained categorization and verification

This paper presents an on-going effort in collecting a large-scale dataset, “CompCars”, that covers not only different car views, but also their different internal and external parts, and rich attributes, and demonstrates a few important applications exploiting the dataset.
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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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Facial Landmark Detection by Deep Multi-task Learning

A novel tasks-constrained deep model is formulated, with task-wise early stopping to facilitate learning convergence and reduces model complexity drastically compared to the state-of-the-art method based on cascaded deep model.
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MaskGAN: Towards Diverse and Interactive Facial Image Manipulation

This work proposes a novel framework termed MaskGAN, enabling diverse and interactive face manipulation, and finds that semantic masks serve as a suitable intermediate representation for flexible face manipulation with fidelity preservation.
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Pedestrian Attribute Recognition At Far Distance

A new pedestrian attribute dataset is released, which is by far the largest and most diverse of its kind and it is shown that the large-scale dataset facilitates the learning of robust attribute detectors with good generalization performance.
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Sparse R-CNN: End-to-End Object Detection with Learnable Proposals

Sparse R-CNN demonstrates accuracy, run-time and training convergence performance on par with the well-established detector baselines on the challenging COCO dataset, e.g., achieving 45.0 AP in standard 3× training schedule and running at 22 fps using ResNet-50 FPN model.
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