b'Boqing Gong'

research

∙ 04/11/2022

medXGAN: Visual Explanations for Medical Classifiers through a Generative Latent Space

Despite the surge of deep learning in the past decade, some users are sk...

Amil Dravid, et al. ∙

research

∙ 03/15/2022

Surrogate Gap Minimization Improves Sharpness-Aware Training

The recently proposed Sharpness-Aware Minimization (SAM) improves genera...

Juntang Zhuang, et al. ∙

research

∙ 09/18/2021

Anti-Neuron Watermarking: Protecting Personal Data Against Unauthorized Neural Model Training

In this paper, we raise up an emerging personal data protection problem ...

Zihang Zou, et al. ∙

research

∙ 06/18/2021

Bridging the Gap Between Object Detection and User Intent via Query-Modulation

When interacting with objects through cameras, or pictures, users often ...

Marco Fornoni, et al. ∙

research

∙ 06/03/2021

When Vision Transformers Outperform ResNets without Pretraining or Strong Data Augmentations

Vision Transformers (ViTs) and MLPs signal further efforts on replacing ...

Xiangning Chen, et al. ∙

research

∙ 06/01/2021

Adversarially Adaptive Normalization for Single Domain Generalization

Single domain generalization aims to learn a model that performs well on...

Xinjie Fan, et al. ∙

research

∙ 04/22/2021

VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text

We present a framework for learning multimodal representations from unla...

Hassan Akbari, et al. ∙

research

∙ 04/12/2021

Class-Balanced Distillation for Long-Tailed Visual Recognition

Real-world imagery is often characterized by a significant imbalance of ...

Ahmet Iscen, et al. ∙

research

∙ 03/23/2021

Robust and Accurate Object Detection via Adversarial Learning

Data augmentation has become a de facto component for training high-perf...

Xiangning Chen, et al. ∙

research

∙ 03/21/2021

MoViNets: Mobile Video Networks for Efficient Video Recognition

We present Mobile Video Networks (MoViNets), a family of computation and...

Dan Kondratyuk, et al. ∙

research

∙ 02/17/2021

A Simple and Effective Use of Object-Centric Images for Long-Tailed Object Detection

Object frequencies in daily scenes follow a long-tailed distribution. Ma...

Cheng Zhang, et al. ∙

research

∙ 02/14/2021

Large-Scale Meta-Learning with Continual Trajectory Shifting

Meta-learning of shared initialization parameters has shown to be highly...

Jaewoong Shin, et al. ∙

research

∙ 12/13/2020

Contrastive Learning for Label-Efficient Semantic Segmentation

Collecting labeled data for the task of semantic segmentation is expensi...

Xiangyun Zhao, et al. ∙

research

∙ 11/23/2020

Ranking Neural Checkpoints

This paper is concerned with ranking many pre-trained deep neural networ...

Yandong Li, et al. ∙

research

∙ 07/17/2020

Improving Object Detection with Selective Self-supervised Self-training

We study how to leverage Web images to augment human-curated object dete...

Yandong Li, et al. ∙

research

∙ 07/16/2020

Complete Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds

We study an unsupervised domain adaptation problem for the semantic labe...

Li Yi, et al. ∙

research

∙ 06/25/2020

Smooth Adversarial Training

It is commonly believed that networks cannot be both accurate and robust...

Cihang Xie, et al. ∙

research

∙ 05/01/2020

When Ensembling Smaller Models is More Efficient than Single Large Models

Ensembling is a simple and popular technique for boosting evaluation per...

Dan Kondratyuk, et al. ∙

research

∙ 03/31/2020

Neural Networks Are More Productive Teachers Than Human Raters: Active Mixup for Data-Efficient Knowledge Distillation from a Blackbox Model

We study how to train a student deep neural network for visual recogniti...

Dongdong Wang, et al. ∙

research

∙ 03/24/2020

Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective

Object frequency in the real world often follows a power law, leading to...

Muhammad Abdullah Jamal, et al. ∙

research

∙ 01/08/2020

MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius

Adversarial training is one of the most popular ways to learn robust mod...

Runtian Zhai, et al. ∙

research

∙ 12/25/2019

Look, Listen, and Act: Towards Audio-Visual Embodied Navigation

A crucial aspect of mobile intelligent agents is their ability to integr...

Chuang Gan, et al. ∙

research

∙ 11/21/2019

Adversarial Examples Improve Image Recognition

Adversarial examples are commonly viewed as a threat to ConvNets. Here w...

Cihang Xie, et al. ∙

research

∙ 09/08/2019

Compound Domain Adaptation in an Open World

Existing works on domain adaptation often assume clear boundaries betwee...

Ziwei Liu, et al. ∙

research

∙ 09/02/2019

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data

We propose to harness the potential of simulation for the semantic segme...

Xiangyu Yue, et al. ∙

research

∙ 08/26/2019

Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach

We propose a new approach, called self-motivated pyramid curriculum doma...

Qing Lian, et al. ∙

research

∙ 08/18/2019

A Fast and Accurate One-Stage Approach to Visual Grounding

We propose a simple, fast, and accurate one-stage approach to visual gro...

Zhengyuan Yang, et al. ∙

research

∙ 06/16/2019

Defending Against Adversarial Attacks Using Random Forests

As deep neural networks (DNNs) have become increasingly important and po...

Yifan Ding, et al. ∙

research

∙ 05/01/2019

NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks

Powerful adversarial attack methods are vital for understanding how to c...

Yandong Li, et al. ∙

research

∙ 04/10/2019

Large-Scale Long-Tailed Recognition in an Open World

Real world data often have a long-tailed and open-ended distribution. A ...

Ziwei Liu, et al. ∙

research

∙ 04/05/2019

Synthesized Policies for Transfer and Adaptation across Tasks and Environments

The ability to transfer in reinforcement learning is key towards buildin...

Hexiang Hu, et al. ∙

research

∙ 02/25/2019

Joint Modeling of Dense and Incomplete Trajectories for Citywide Traffic Volume Inference

Real-time traffic volume inference is key to an intelligent city. It is ...

Xianfeng Tang, et al. ∙

research

∙ 12/24/2018

A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban Scenes

During the last half decade, convolutional neural networks (CNNs) have t...

Yang Zhang, et al. ∙

research

∙ 12/16/2018

Classifier and Exemplar Synthesis for Zero-Shot Learning

Zero-shot learning (ZSL) enables solving a task without the need to see ...

Soravit Changpinyo, et al. ∙

research

∙ 08/09/2018

Controllable Image-to-Video Translation: A Case Study on Facial Expression Generation

The recent advances in deep learning have made it possible to generate p...

Lijie Fan, et al. ∙

research

∙ 07/28/2018

Improving Sequential Determinantal Point Processes for Supervised Video Summarization

It is now much easier than ever before to produce videos. While the ubiq...

Aidean Sharghi, et al. ∙

research

∙ 07/20/2018

Optimize Deep Convolutional Neural Network with Ternarized Weights and High Accuracy

Deep convolution neural network has achieved great success in many artif...

Zhezhi He, et al. ∙

research

∙ 07/18/2018

Defend Deep Neural Networks Against Adversarial Examples via Fixed andDynamic Quantized Activation Functions

Recent studies have shown that deep neural networks (DNNs) are vulnerabl...

Adnan Siraj Rakin, et al. ∙

research

∙ 07/11/2018

How Local is the Local Diversity? Reinforcing Sequential Determinantal Point Processes with Dynamic Ground Sets for Supervised Video Summarization

The large volume of video content and high viewing frequency demand auto...

Yandong Li, et al. ∙

research

∙ 04/02/2018

End-to-End Learning of Motion Representation for Video Understanding

Despite the recent success of end-to-end learned representations, hand-c...

Lijie Fan, et al. ∙

research

∙ 03/21/2018

End-to-End Video Captioning with Multitask Reinforcement Learning

Although end-to-end (E2E) learning has led to promising performance on a...

Lijun Li, et al. ∙

research

∙ 03/05/2018

Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect

Despite being impactful on a variety of problems and applications, the g...

Xiang Wei, et al. ∙

research

∙ 02/08/2018

A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels

The recent success of deep neural networks is powered in part by large-s...

Yifan Ding, et al. ∙

research

∙ 02/05/2018

Blind Pre-Processing: A Robust Defense Method Against Adversarial Examples

Deep learning algorithms and networks are vulnerable to perturbed inputs...

Adnan Siraj Rakin, et al. ∙

research

∙ 02/05/2018

Robust Pre-Processing: A Robust Defense Method Against Adversary Attack

Deep learning algorithms and networks are vulnerable to perturbed inputs...

Adnan Siraj Rakin, et al. ∙

research

∙ 08/15/2017

VQS: Linking Segmentations to Questions and Answers for Supervised Attention in VQA and Question-Focused Semantic Segmentation

Rich and dense human labeled datasets are among the main enabling factor...

Chuang Gan, et al. ∙

research

∙ 07/29/2017

Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes

During the last half decade, convolutional neural networks (CNNs) have t...

Yang Zhang, et al. ∙

research

∙ 07/16/2017

Query-Focused Video Summarization: Dataset, Evaluation, and A Memory Network Based Approach

Recent years have witnessed a resurgence of interest in video summarizat...

Aidean Sharghi, et al. ∙

research

∙ 05/31/2016

Fast Zero-Shot Image Tagging

The well-known word analogy experiments show that the recent word vector...

Yang Zhang, et al. ∙

research

∙ 03/02/2016

Synthesized Classifiers for Zero-Shot Learning

Given semantic descriptions of object classes, zero-shot learning aims t...

Soravit Changpinyo, et al. ∙

Boqing Gong

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