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09/13/2022
ComplETR: Reducing the cost of annotations for object detection in dense scenes with vision transformers
Annotating bounding boxes for object detection is expensive, time-consum...
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07/22/2022
Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark
Most existing works on few-shot object detection (FSOD) focus on a setti...
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11/18/2021
Improving Transferability of Representations via Augmentation-Aware Self-Supervision
Recent unsupervised representation learning methods have shown to be eff...
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10/17/2020
i-Mix: A Strategy for Regularizing Contrastive Representation Learning
Contrastive representation learning has shown to be an effective way of ...
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05/24/2020
ShapeAdv: Generating Shape-Aware Adversarial 3D Point Clouds
We introduce ShapeAdv, a novel framework to study shape-aware adversaria...
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10/11/2019
A Simple Randomization Technique for Generalization in Deep Reinforcement Learning
Deep reinforcement learning (RL) agents often fail to generalize to unse...
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03/29/2019
Incremental Learning with Unlabeled Data in the Wild
Deep neural networks are known to suffer from catastrophic forgetting in...
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01/31/2019
Robust Inference via Generative Classifiers for Handling Noisy Labels
Large-scale datasets may contain significant proportions of noisy (incor...
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07/10/2018
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
Detecting test samples drawn sufficiently far away from the training dis...
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04/02/2018
Hierarchical Novelty Detection for Visual Object Recognition
Deep neural networks have achieved impressive success in large-scale vis...
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11/26/2017
Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples
The problem of detecting whether a test sample is from in-distribution (...
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05/24/2017
Towards Understanding the Invertibility of Convolutional Neural Networks
Several recent works have empirically observed that Convolutional Neural...
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06/21/2016
