Search Results for author: MingJie Sun

Found 10 papers, 5 papers with code

Discriminative Triad Matching and Reconstruction for Weakly Referring Expression Grounding

1 code implementation8 Jun 2021 MingJie Sun, Jimin Xiao, Eng Gee Lim, Si Liu, John Y. Goulermas

In this paper, we are tackling the weakly-supervised referring expression grounding task, for the localization of a referent object in an image according to a query sentence, where the mapping between image regions and queries are not available during the training stage.

Referring Expression

Iterative Shrinking for Referring Expression Grounding Using Deep Reinforcement Learning

1 code implementation CVPR 2021 MingJie Sun, Jimin Xiao, Eng Gee Lim

In this paper, we are tackling the proposal-free referring expression grounding task, aiming at localizing the target object according to a query sentence, without relying on off-the-shelf object proposals.

Referring Expression reinforcement-learning

Can Shape Structure Features Improve Model Robustness Under Diverse Adversarial Settings?

no code implementations ICCV 2021 MingJie Sun, Zichao Li, Chaowei Xiao, Haonan Qiu, Bhavya Kailkhura, Mingyan Liu, Bo Li

Specifically, EdgeNetRob and EdgeGANRob first explicitly extract shape structure features from a given image via an edge detection algorithm.

Edge Detection

Extreme Value Preserving Networks

no code implementations17 Nov 2020 MingJie Sun, Jianguo Li, ChangShui Zhang

Recent evidence shows that convolutional neural networks (CNNs) are biased towards textures so that CNNs are non-robust to adversarial perturbations over textures, while traditional robust visual features like SIFT (scale-invariant feature transforms) are designed to be robust across a substantial range of affine distortion, addition of noise, etc with the mimic of human perception nature.

Poisoned classifiers are not only backdoored, they are fundamentally broken

1 code implementation18 Oct 2020 MingJie Sun, Siddhant Agarwal, J. Zico Kolter

Under this threat model, we propose a test-time, human-in-the-loop attack method to generate multiple effective alternative triggers without access to the initial backdoor and the training data.

Fast Template Matching and Update for Video Object Tracking and Segmentation

1 code implementation CVPR 2020 Mingjie Sun, Jimin Xiao, Eng Gee Lim, Bingfeng Zhang, Yao Zhao

Specifically, the reinforcement learning agent learns to decide whether to update the target template according to the quality of the predicted result.

Frame reinforcement-learning +5

Adaptive ROI Generation for Video Object Segmentation Using Reinforcement Learning

1 code implementation27 Sep 2019 Mingjie Sun, Jimin Xiao, Eng Gee Lim, Yanchu Xie, Jiashi Feng

In this paper, we aim to tackle the task of semi-supervised video object segmentation across a sequence of frames where only the ground-truth segmentation of the first frame is provided.

Frame reinforcement-learning +3

Extreme Values are Accurate and Robust in Deep Networks

no code implementations25 Sep 2019 Jianguo Li, MingJie Sun, ChangShui Zhang

Recent evidence shows that convolutional neural networks (CNNs) are biased towards textures so that CNNs are non-robust to adversarial perturbations over textures, while traditional robust visual features like SIFT (scale-invariant feature transforms) are designed to be robust across a substantial range of affine distortion, addition of noise, etc with the mimic of human perception nature.

Data Poisoning Attack against Unsupervised Node Embedding Methods

no code implementations30 Oct 2018 Mingjie Sun, Jian Tang, Huichen Li, Bo Li, Chaowei Xiao, Yao Chen, Dawn Song

In this paper, we take the task of link prediction as an example, which is one of the most fundamental problems for graph analysis, and introduce a data positioning attack to node embedding methods.

Data Poisoning Link Prediction

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