# Detect-to-Retrieve: Efficient Regional Aggregation for Image Search

Then, we demonstrate how a trained landmark detector, using our new dataset, can be leveraged to index image regions and improve retrieval accuracy while being much more efficient than existing regional methods.

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# FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentation

Many of the recent successful methods for video object segmentation (VOS) are overly complicated, heavily rely on fine-tuning on the first frame, and/or are slow, and are hence of limited practical use.

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# A Style-Based Generator Architecture for Generative Adversarial Networks

We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature.

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# Temporal Cycle-Consistency Learning

We introduce a self-supervised representation learning method based on the task of temporal alignment between videos.

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# Pushing the Boundaries of View Extrapolation with Multiplane Images

We present a theoretical analysis showing how the range of views that can be rendered from an MPI increases linearly with the MPI disparity sampling frequency, as well as a novel MPI prediction procedure that theoretically enables view extrapolations of up to $4\times$ the lateral viewpoint movement allowed by prior work.

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# Unprocessing Images for Learned Raw Denoising

Machine learning techniques work best when the data used for training resembles the data used for evaluation.

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# Panoptic Feature Pyramid Networks

In this work, we perform a detailed study of this minimally extended version of Mask R-CNN with FPN, which we refer to as Panoptic FPN, and show it is a robust and accurate baseline for both tasks.

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# Deformable ConvNets v2: More Deformable, Better Results

The superior performance of Deformable Convolutional Networks arises from its ability to adapt to the geometric variations of objects.

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