# 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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# 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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# 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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# 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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# 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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In exploring a more effective approach, we find that the key to a successful instance segmentation cascade is to fully leverage the reciprocal relationship between detection and segmentation.

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# Region Proposal by Guided Anchoring

State-of-the-art detectors mostly rely on a dense anchoring scheme, where anchors are sampled uniformly over the spatial domain with a predefined set of scales and aspect ratios.

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# Libra R-CNN: Towards Balanced Learning for Object Detection

In this work, we carefully revisit the standard training practice of detectors, and find that the detection performance is often limited by the imbalance during the training process, which generally consists in three levels - sample level, feature level, and objective level.

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# Grid R-CNN

This paper proposes a novel object detection framework named Grid R-CNN, which adopts a grid guided localization mechanism for accurate object detection.

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