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Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection
Training on datasets with long-tailed distributions has been challenging...
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Contrastive Syn-to-Real Generalization
Training on synthetic data can be beneficial for label or data-scarce sc...
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UFO^2: A Unified Framework towards Omni-supervised Object Detection
Existing work on object detection often relies on a single form of annot...
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Distributionally Robust Learning for Unsupervised Domain Adaptation
We propose a distributionally robust learning (DRL) method for unsupervi...
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Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning
Humans have an inherent ability to learn novel concepts from only a few ...
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Delving Deeper into Anti-aliasing in ConvNets
Aliasing refers to the phenomenon that high frequency signals degenerate...
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Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification
Although a significant progress has been witnessed in supervised person ...
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Unsupervised Controllable Generation with Self-Training
Recent generative adversarial networks (GANs) are able to generate impre...
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Neural Networks with Recurrent Generative Feedback
Neural networks are vulnerable to input perturbations such as additive n...
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Transposer: Universal Texture Synthesis Using Feature Maps as Transposed Convolution Filter
Conventional CNNs for texture synthesis consist of a sequence of (de)-co...
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Automated Synthetic-to-Real Generalization
Models trained on synthetic images often face degraded generalization to...
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Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation
Although having achieved great success in medical image segmentation, de...
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Instance-aware, Context-focused, and Memory-efficient Weakly Supervised Object Detection
Weakly supervised learning has emerged as a compelling tool for object d...
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Angular Visual Hardness
Although convolutional neural networks (CNNs) are inspired by the mechan...
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Confidence Regularized Self-Training
Recent advances in domain adaptation show that deep self-training presen...
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Compressive Hyperspherical Energy Minimization
Recent work on minimum hyperspherical energy (MHE) has demonstrated its ...
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Joint Discriminative and Generative Learning for Person Re-identification
Person re-identification (re-id) remains challenging due to significant ...
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Partial Convolution based Padding
In this paper, we present a simple yet effective padding scheme that can...
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Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training
Recent deep networks achieved state of the art performance on a variety ...
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Simultaneous Edge Alignment and Learning
Edge detection is among the most fundamental vision problems for its rol...
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Learning towards Minimum Hyperspherical Energy
Neural networks are a powerful class of nonlinear functions that can be ...
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Decoupled Networks
Inner product-based convolution has been a central component of convolut...
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Learning Strict Identity Mappings in Deep Residual Networks
A family of super deep networks, referred to as residual networks or Res...
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Deep Hyperspherical Learning
Convolution as inner product has been the founding basis of convolutiona...
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CASENet: Deep Category-Aware Semantic Edge Detection
Boundary and edge cues are highly beneficial in improving a wide variety...
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Structured Hough Voting for Vision-based Highway Border Detection
We propose a vision-based highway border detection algorithm using struc...
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KCRC-LCD: Discriminative Kernel Collaborative Representation with Locality Constrained Dictionary for Visual Categorization
We consider the image classification problem via kernel collaborative re...
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Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering
Under the framework of graph-based learning, the key to robust subspace ...
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