315 citations · 631 across the 22 of their papers we have counts for
23 papers
Camouflaged Image Synthesis Is All You Need to Boost Camouflaged Detection
Haichao Zhang, Can Qin, Yu Yin +1
Camouflaged objects that blend into natural scenes pose significant challenges for deep-learning models to detect and synthesize. While camouflaged object detection is a crucial ta…
UniControl: A Unified Diffusion Model for Controllable Visual Generation In the Wild
Can Qin, Shu Zhang, Ning Yu +10
Achieving machine autonomy and human control often represent divergent objectives in the design of interactive AI systems. Visual generative foundation models such as Stable Diffus…
Mask-free OVIS: Open-Vocabulary Instance Segmentation without Manual Mask Annotations
Vibashan VS, Ning Yu, Chen Xing +5
Existing instance segmentation models learn task-specific information using manual mask annotations from base (training) categories. These mask annotations require tremendous human…
Image as Set of Points
Xu Ma, Yuqian Zhou, Huan Wang +4
What is an image and how to extract latent features? Convolutional Networks (ConvNets) consider an image as organized pixels in a rectangular shape and extract features via convolu…
GlueGen: Plug and Play Multi-modal Encoders for X-to-image Generation
Can Qin, Ning Yu, Chen Xing +6
Text-to-image (T2I) models based on diffusion processes have achieved remarkable success in controllable image generation using user-provided captions. However, the tight coupling…
HIVE: Harnessing Human Feedback for Instructional Visual Editing
Shu Zhang, Xinyi Yang, Yihao Feng +9
Incorporating human feedback has been shown to be crucial to align text generated by large language models to human preferences. We hypothesize that state-of-the-art instructional…
Why is the State of Neural Network Pruning so Confusing? On the Fairness, Comparison Setup, and Trainability in Network Pruning
Huan Wang, Can Qin, Yue Bai +1
The state of neural network pruning has been noticed to be unclear and even confusing for a while, largely due to "a lack of standardized benchmarks and metrics" [3]. To standardiz…
Making Reconstruction-based Method Great Again for Video Anomaly Detection
Yizhou Wang, Can Qin, Yue Bai +3
Anomaly detection in videos is a significant yet challenging problem. Previous approaches based on deep neural networks employ either reconstruction-based or prediction-based appro…
A Close Look at Spatial Modeling: From Attention to Convolution
Xu Ma, Huan Wang, Can Qin +4
Vision Transformers have shown great promise recently for many vision tasks due to the insightful architecture design and attention mechanism. By revisiting the self-attention resp…
Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework
Xu Ma, Can Qin, Haoxuan You +2
Point cloud analysis is challenging due to irregularity and unordered data structure. To capture the 3D geometries, prior works mainly rely on exploring sophisticated local geometr…
Semi-supervised Domain Adaptive Structure Learning
Can Qin, Lichen Wang, Qianqian Ma +3
Semi-supervised domain adaptation (SSDA) is quite a challenging problem requiring methods to overcome both 1) overfitting towards poorly annotated data and 2) distribution shift ac…
The 5th Recognizing Families in the Wild Data Challenge: Predicting Kinship from Faces
Joseph P. Robinson, Can Qin, Ming Shao +3
Recognizing Families In the Wild (RFIW), held as a data challenge in conjunction with the 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG), is a la…
SLAP: Self-supervised Anomaly Detection with Adversarial Perturbation
Yizhou Wang, Can Qin, Rongzhe Wei +3
Anomaly detection is a fundamental yet challenging problem in machine learning due to the lack of label information. In this work, we propose a novel and powerful framework, dubbed…
Rethinking Adam: A Twofold Exponential Moving Average Approach
Yizhou Wang, Yue Kang, Can Qin +4
Adaptive gradient methods, e.g. \textsc{Adam}, have achieved tremendous success in machine learning. Scaling the learning rate element-wisely by a certain form of second moment est…
Dynamical Isometry: The Missing Ingredient for Neural Network Pruning
Huan Wang, Can Qin, Yue Bai +1
Several recent works [40, 24] observed an interesting phenomenon in neural network pruning: A larger finetuning learning rate can improve the final performance significantly. Unfor…
Balancing Biases and Preserving Privacy on Balanced Faces in the Wild
Joseph P Robinson, Can Qin, Yann Henon +2
There are demographic biases present in current facial recognition (FR) models. To measure these biases across different ethnic and gender subgroups, we introduce our Balanced Face…
Recent Advances on Neural Network Pruning at Initialization
Huan Wang, Can Qin, Yue Bai +2
Neural network pruning typically removes connections or neurons from a pretrained converged model; while a new pruning paradigm, pruning at initialization (PaI), attempts to prune…
SuperFront: From Low-resolution to High-resolution Frontal Face Synthesis
Yu Yin, Joseph P. Robinson, Songyao Jiang +3
Advances in face rotation, along with other face-based generative tasks, are more frequent as we advance further in topics of deep learning. Even as impressive milestones are achie…
Neural Pruning via Growing Regularization
Huan Wang, Can Qin, Yulun Zhang +1
Regularization has long been utilized to learn sparsity in deep neural network pruning. However, its role is mainly explored in the small penalty strength regime. In this work, we…
Face Recognition: Too Bias, or Not Too Bias?
Joseph P Robinson, Gennady Livitz, Yann Henon +3
We reveal critical insights into problems of bias in state-of-the-art facial recognition (FR) systems using a novel Balanced Faces In the Wild (BFW) dataset: data balanced for gend…
Contradictory Structure Learning for Semi-supervised Domain Adaptation
Can Qin, Lichen Wang, Qianqian Ma +3
Current adversarial adaptation methods attempt to align the cross-domain features, whereas two challenges remain unsolved: 1) the conditional distribution mismatch and 2) the bias…
Self-Directed Online Machine Learning for Topology Optimization
Changyu Deng, Yizhou Wang, Can Qin +2
Topology optimization by optimally distributing materials in a given domain requires non-gradient optimizers to solve highly complicated problems. However, with hundreds of design…
PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation
Can Qin, Haoxuan You, Lichen Wang +2
Domain Adaptation (DA) approaches achieved significant improvements in a wide range of machine learning and computer vision tasks (i.e., classification, detection, and segmentation…