958 citations · 1.2k across the 17 of their papers we have counts for
17 papers
Dance with You: The Diversity Controllable Dancer Generation via Diffusion Models
Siyue Yao, Mingjie Sun, Bingliang Li +3
Recently, digital humans for interpersonal interaction in virtual environments have gained significant attention. In this paper, we introduce a novel multi-dancer synthesis task ca…
A Simple and Effective Pruning Approach for Large Language Models
Mingjie Sun, Zhuang Liu, Anna Bair +1
As their size increases, Large Languages Models (LLMs) are natural candidates for network pruning methods: approaches that drop a subset of network weights while striving to preser…
Fully and Weakly Supervised Referring Expression Segmentation with End-to-End Learning
Hui Li, Mingjie Sun, Jimin Xiao +2
Referring Expression Segmentation (RES), which is aimed at localizing and segmenting the target according to the given language expression, has drawn increasing attention. Existing…
Test-Time Adaptation via Conjugate Pseudo-labels
Sachin Goyal, Mingjie Sun, Aditi Raghunathan +1
Test-time adaptation (TTA) refers to adapting neural networks to distribution shifts, with access to only the unlabeled test samples from the new domain at test-time. Prior TTA met…
(Certified!!) Adversarial Robustness for Free!
Nicholas Carlini, Florian Tramer, Krishnamurthy Dj Dvijotham +3
In this paper we show how to achieve state-of-the-art certified adversarial robustness to 2-norm bounded perturbations by relying exclusively on off-the-shelf pretrained models. To…
Discriminative Triad Matching and Reconstruction for Weakly Referring Expression Grounding
Mingjie Sun, Jimin Xiao, Eng Gee Lim +2
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…
Iterative Shrinking for Referring Expression Grounding Using Deep Reinforcement Learning
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-t…
Extreme Value Preserving Networks
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 traditiona…
Poisoned classifiers are not only backdoored, they are fundamentally broken
Mingjie Sun, Siddhant Agarwal, J. Zico Kolter
Under a commonly-studied backdoor poisoning attack against classification models, an attacker adds a small trigger to a subset of the training data, such that the presence of this…
Fast Template Matching and Update for Video Object Tracking and Segmentation
Mingjie Sun, Jimin Xiao, Eng Gee Lim +2
In this paper, the main task we aim to tackle is the multi-instance semi-supervised video object segmentation across a sequence of frames where only the first-frame box-level groun…
Denoised Smoothing: A Provable Defense for Pretrained Classifiers
Hadi Salman, Mingjie Sun, Greg Yang +2
We present a method for provably defending any pretrained image classifier against adversarial attacks. This method, for instance, allows public vision API providers and u…
Reliability Does Matter: An End-to-End Weakly Supervised Semantic Segmentation Approach
Bingfeng Zhang, Jimin Xiao, Yunchao Wei +2
Weakly supervised semantic segmentation is a challenging task as it only takes image-level information as supervision for training but produces pixel-level predictions for testing.…
Progressive Sample Mining and Representation Learning for One-Shot Person Re-identification with Adversarial Samples
Hui Li, Jimin Xiao, Mingjie Sun +2
In this paper, we aim to tackle the one-shot person re-identification problem where only one image is labelled for each person, while other images are unlabelled. This task is chal…
Adaptive ROI Generation for Video Object Segmentation Using Reinforcement Learning
Mingjie Sun, Jimin Xiao, Eng Gee Lim +2
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 pro…
Characterizing Attacks on Deep Reinforcement Learning
Xinlei Pan, Chaowei Xiao, Warren He +8
Recent studies show that Deep Reinforcement Learning (DRL) models are vulnerable to adversarial attacks, which attack DRL models by adding small perturbations to the observations.…
Data Poisoning Attack against Unsupervised Node Embedding Methods
Mingjie Sun, Jian Tang, Huichen Li +4
Unsupervised node embedding methods (e.g., DeepWalk, LINE, and node2vec) have attracted growing interests given their simplicity and effectiveness. However, although these methods…
Rethinking the Value of Network Pruning
Zhuang Liu, Mingjie Sun, Tinghui Zhou +2
Network pruning is widely used for reducing the heavy inference cost of deep models in low-resource settings. A typical pruning algorithm is a three-stage pipeline, i.e., training…