# Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection

In this paper we propose a method that leverages temporal context from the unlabeled frames of a novel camera to improve performance at that camera.

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# EfficientDet: Scalable and Efficient Object Detection

Model efficiency has become increasingly important in computer vision.

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# MnasFPN: Learning Latency-aware Pyramid Architecture for Object Detection on Mobile Devices

We propose MnasFPN, a mobile-friendly search space for the detection head, and combine it with latency-aware architecture search to produce efficient object detection models.

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# Distilling Effective Supervision from Severe Label Noise

For instance, on CIFAR100 with a $40\%$ uniform noise ratio and only 10 trusted labeled data per class, our method achieves $80. 2{\pm}0. 3\%$ classification accuracy, where the error rate is only $1. 4\%$ higher than a neural network trained without label noise.

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# PointRend: Image Segmentation as Rendering

We present a new method for efficient high-quality image segmentation of objects and scenes.

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# Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection

In this paper, we first point out that the essential difference between anchor-based and anchor-free detection is actually how to define positive and negative training samples, which leads to the performance gap between them.

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# Analyzing and Improving the Image Quality of StyleGAN

Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality.

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# Adversarial Examples Improve Image Recognition

We show that AdvProp improves a wide range of models on various image recognition tasks and performs better when the models are bigger.

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# Designing Network Design Spaces

In this work, we present a new network design paradigm.

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# 3D Photography using Context-aware Layered Depth Inpainting

We propose a method for converting a single RGB-D input image into a 3D photo - a multi-layer representation for novel view synthesis that contains hallucinated color and depth structures in regions occluded in the original view.

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