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Large scale weakly and semi-supervised learning for low-resource video ASR
Many semi- and weakly-supervised approaches have been investigated for o...
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Designing Network Design Spaces
In this work, we present a new network design paradigm. Our goal is to h...
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Are Labels Necessary for Neural Architecture Search?
Existing neural network architectures in computer vision — whether desig...
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Improved Baselines with Momentum Contrastive Learning
Contrastive unsupervised learning has recently shown encouraging progres...
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PointRend: Image Segmentation as Rendering
We present a new method for efficient high-quality image segmentation of...
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A Multigrid Method for Efficiently Training Video Models
Training competitive deep video models is an order of magnitude slower t...
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Momentum Contrast for Unsupervised Visual Representation Learning
We present Momentum Contrast (MoCo) for unsupervised visual representati...
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Training ASR models by Generation of Contextual Information
Supervised ASR models have reached unprecedented levels of accuracy, tha...
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PHYRE: A New Benchmark for Physical Reasoning
Understanding and reasoning about physics is an important ability of int...
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LVIS: A Dataset for Large Vocabulary Instance Segmentation
Progress on object detection is enabled by datasets that focus the resea...
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Exploring Randomly Wired Neural Networks for Image Recognition
Neural networks for image recognition have evolved through extensive man...
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TensorMask: A Foundation for Dense Object Segmentation
Sliding-window object detectors that generate bounding-box object predic...
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Panoptic Feature Pyramid Networks
The recently introduced panoptic segmentation task has renewed our commu...
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Long-Term Feature Banks for Detailed Video Understanding
To understand the world, we humans constantly need to relate the present...
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Rethinking ImageNet Pre-training
We report competitive results on object detection and instance segmentat...
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Exploring the Limits of Weakly Supervised Pretraining
State-of-the-art visual perception models for a wide range of tasks rely...
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Low-Shot Learning from Imaginary Data
Humans can quickly learn new visual concepts, perhaps because they can e...
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Panoptic Segmentation
We propose and study a novel 'Panoptic Segmentation' (PS) task. Panoptic...
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Data Distillation: Towards Omni-Supervised Learning
We investigate omni-supervised learning, a special regime of semi-superv...
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Learning by Asking Questions
We introduce an interactive learning framework for the development and t...
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Learning to Segment Every Thing
Existing methods for object instance segmentation require all training i...
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Non-local Neural Networks
Both convolutional and recurrent operations are building blocks that pro...
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Focal Loss for Dense Object Detection
The highest accuracy object detectors to date are based on a two-stage a...
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Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
Deep learning thrives with large neural networks and large datasets. How...
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Inferring and Executing Programs for Visual Reasoning
Existing methods for visual reasoning attempt to directly map inputs to ...
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Mask R-CNN
We present a conceptually simple, flexible, and general framework for ob...
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CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
When building artificial intelligence systems that can reason and answer...
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Learning Features by Watching Objects Move
This paper presents a novel yet intuitive approach to unsupervised featu...
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Feature Pyramid Networks for Object Detection
Feature pyramids are a basic component in recognition systems for detect...
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Aggregated Residual Transformations for Deep Neural Networks
We present a simple, highly modularized network architecture for image c...
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Low-shot Visual Recognition by Shrinking and Hallucinating Features
Low-shot visual learning---the ability to recognize novel object categor...
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Visual Storytelling
We introduce the first dataset for sequential vision-to-language, and ex...
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Deep3D: Fully Automatic 2D-to-3D Video Conversion with Deep Convolutional Neural Networks
As 3D movie viewing becomes mainstream and Virtual Reality (VR) market e...
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Training Region-based Object Detectors with Online Hard Example Mining
The field of object detection has made significant advances riding on th...
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Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels
When human annotators are given a choice about what to label in an image...
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Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
It is well known that contextual and multi-scale representations are imp...
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Reducing Overfitting in Deep Networks by Decorrelating Representations
One major challenge in training Deep Neural Networks is preventing overf...
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You Only Look Once: Unified, Real-Time Object Detection
We present YOLO, a new approach to object detection. Prior work on objec...
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
State-of-the-art object detection networks depend on region proposal alg...
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Contextual Action Recognition with R*CNN
There are multiple cues in an image which reveal what action a person is...
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Fast R-CNN
This paper proposes a Fast Region-based Convolutional Network method (Fa...
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Object Detection Networks on Convolutional Feature Maps
Most object detectors contain two important components: a feature extrac...
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Inferring 3D Object Pose in RGB-D Images
The goal of this work is to replace objects in an RGB-D scene with corre...
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Actions and Attributes from Wholes and Parts
We investigate the importance of parts for the tasks of action and attri...
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Hypercolumns for Object Segmentation and Fine-grained Localization
Recognition algorithms based on convolutional networks (CNNs) typically ...
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Deformable Part Models are Convolutional Neural Networks
Deformable part models (DPMs) and convolutional neural networks (CNNs) a...
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Learning Rich Features from RGB-D Images for Object Detection and Segmentation
In this paper we study the problem of object detection for RGB-D images ...
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LSDA: Large Scale Detection Through Adaptation
A major challenge in scaling object detection is the difficulty of obtai...
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Part-based R-CNNs for Fine-grained Category Detection
Semantic part localization can facilitate fine-grained categorization by...
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Simultaneous Detection and Segmentation
We aim to detect all instances of a category in an image and, for each i...
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