Search Results for author: Can Qin

Found 32 papers, 20 papers with code

M3SOT: Multi-frame, Multi-field, Multi-space 3D Single Object Tracking

2 code implementations11 Dec 2023 Jiaming Liu, Yue Wu, Maoguo Gong, Qiguang Miao, Wenping Ma, Can Qin

3D Single Object Tracking (SOT) stands a forefront task of computer vision, proving essential for applications like autonomous driving.

3D Single Object Tracking Autonomous Driving +1

Camouflaged Image Synthesis Is All You Need to Boost Camouflaged Detection

no code implementations13 Aug 2023 Haichao Zhang, Can Qin, Yu Yin, Yun Fu

This approach can serve as a plug-and-play data generation and augmentation module for existing camouflaged object detection tasks and provides a novel way to introduce more diversity and distributions into current camouflage datasets.

Image Generation Object +2

Mask-free OVIS: Open-Vocabulary Instance Segmentation without Manual Mask Annotations

no code implementations CVPR 2023 Vibashan VS, Ning Yu, Chen Xing, Can Qin, Mingfei Gao, Juan Carlos Niebles, Vishal M. Patel, ran Xu

In summary, an OV method learns task-specific information using strong supervision from base annotations and novel category information using weak supervision from image-captions pairs.

Image Captioning Instance Segmentation +2

GlueGen: Plug and Play Multi-modal Encoders for X-to-image Generation

1 code implementation ICCV 2023 Can Qin, Ning Yu, Chen Xing, Shu Zhang, Zeyuan Chen, Stefano Ermon, Yun Fu, Caiming Xiong, ran Xu

Empirical results show that GlueNet can be trained efficiently and enables various capabilities beyond previous state-of-the-art models: 1) multilingual language models such as XLM-Roberta can be aligned with existing T2I models, allowing for the generation of high-quality images from captions beyond English; 2) GlueNet can align multi-modal encoders such as AudioCLIP with the Stable Diffusion model, enabling sound-to-image generation; 3) it can also upgrade the current text encoder of the latent diffusion model for challenging case generation.

Image Generation

HIVE: Harnessing Human Feedback for Instructional Visual Editing

1 code implementation16 Mar 2023 Shu Zhang, Xinyi Yang, Yihao Feng, Can Qin, Chia-Chih Chen, Ning Yu, Zeyuan Chen, Huan Wang, Silvio Savarese, Stefano Ermon, Caiming Xiong, ran Xu

Incorporating human feedback has been shown to be crucial to align text generated by large language models to human preferences.

Text-based Image Editing

Image as Set of Points

2 code implementations2 Mar 2023 Xu Ma, Yuqian Zhou, Huan Wang, Can Qin, Bin Sun, Chang Liu, Yun Fu

Context clusters (CoCs) view an image as a set of unorganized points and extract features via simplified clustering algorithm.

Clustering

Making Reconstruction-based Method Great Again for Video Anomaly Detection

1 code implementation28 Jan 2023 Yizhou Wang, Can Qin, Yue Bai, Yi Xu, Xu Ma, Yun Fu

With the same perturbation magnitude, the testing reconstruction error of the normal frames lowers more than that of the abnormal frames, which contributes to mitigating the overfitting problem of reconstruction.

Anomaly Detection Optical Flow Estimation +1

Why is the State of Neural Network Pruning so Confusing? On the Fairness, Comparison Setup, and Trainability in Network Pruning

2 code implementations12 Jan 2023 Huan Wang, Can Qin, Yue Bai, Yun Fu

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].

Fairness Network Pruning

A Close Look at Spatial Modeling: From Attention to Convolution

1 code implementation23 Dec 2022 Xu Ma, Huan Wang, Can Qin, Kunpeng Li, Xingchen Zhao, Jie Fu, Yun Fu

Vision Transformers have shown great promise recently for many vision tasks due to the insightful architecture design and attention mechanism.

Instance Segmentation object-detection +2

Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework

1 code implementation ICLR 2022 Xu Ma, Can Qin, Haoxuan You, Haoxi Ran, Yun Fu

We notice that detailed local geometrical information probably is not the key to point cloud analysis -- we introduce a pure residual MLP network, called PointMLP, which integrates no sophisticated local geometrical extractors but still performs very competitively.

3D Point Cloud Classification Point Cloud Segmentation

Semi-supervised Domain Adaptive Structure Learning

1 code implementation12 Dec 2021 Can Qin, Lichen Wang, Qianqian Ma, Yu Yin, Huan Wang, Yun Fu

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 across domains.

Domain Adaptation Representation Learning +1

Slow Learning and Fast Inference: Efficient Graph Similarity Computation via Knowledge Distillation

1 code implementation NeurIPS 2021 Can Qin, Handong Zhao, Lichen Wang, Huan Wang, Yulun Zhang, Yun Fu

For slow learning of graph similarity, this paper proposes a novel early-fusion approach by designing a co-attention-based feature fusion network on multilevel GNN features.

Anomaly Detection Graph Similarity +3

Aligned Structured Sparsity Learning for Efficient Image Super-Resolution

1 code implementation NeurIPS 2021 Yulun Zhang, Huan Wang, Can Qin, Yun Fu

To address the above issues, we propose aligned structured sparsity learning (ASSL), which introduces a weight normalization layer and applies $L_2$ regularization to the scale parameters for sparsity.

Image Super-Resolution Knowledge Distillation +3

SLA$^2$P: Self-supervised Anomaly Detection with Adversarial Perturbation

no code implementations25 Nov 2021 Yizhou Wang, Can Qin, Rongzhe Wei, Yi Xu, Yue Bai, Yun Fu

Next we add adversarial perturbation to the transformed features to decrease their softmax scores of the predicted labels and design anomaly scores based on the predictive uncertainties of the classifier on these perturbed features.

Pseudo Label Self-Supervised Anomaly Detection +3

The 5th Recognizing Families in the Wild Data Challenge: Predicting Kinship from Faces

1 code implementation31 Oct 2021 Joseph P. Robinson, Can Qin, Ming Shao, Matthew A. Turk, Rama Chellappa, Yun Fu

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 large-scale, multi-track visual kinship recognition evaluation.

Gesture Recognition Kinship Verification +1

MemREIN: Rein the Domain Shift for Cross-Domain Few-Shot Learning

no code implementations29 Sep 2021 Yi Xu, Lichen Wang, Yizhou Wang, Can Qin, Yulun Zhang, Yun Fu

In this paper, we propose a novel framework, MemREIN, which considers Memorized, Restitution, and Instance Normalization for cross-domain few-shot learning.

Contrastive Learning cross-domain few-shot learning

Rethinking Again the Value of Network Pruning -- A Dynamical Isometry Perspective

no code implementations29 Sep 2021 Huan Wang, Can Qin, Yue Bai, Yun Fu

Several recent works questioned the value of inheriting weight in structured neural network pruning because they empirically found training from scratch can match or even outperform finetuning a pruned model.

Network Pruning

Rethinking Adam: A Twofold Exponential Moving Average Approach

no code implementations22 Jun 2021 Yizhou Wang, Yue Kang, Can Qin, Huan Wang, Yi Xu, Yulun Zhang, Yun Fu

The intuition is that gradient with momentum contains more accurate directional information and therefore its second moment estimation is a more favorable option for learning rate scaling than that of the raw gradient.

Dynamical Isometry: The Missing Ingredient for Neural Network Pruning

no code implementations12 May 2021 Huan Wang, Can Qin, Yue Bai, Yun Fu

This paper is meant to explain it through the lens of dynamical isometry [42].

Network Pruning

Balancing Biases and Preserving Privacy on Balanced Faces in the Wild

1 code implementation16 Mar 2021 Joseph P Robinson, Can Qin, Yann Henon, Samson Timoner, Yun Fu

This scheme boosts the average performance and preserves identity information while removing demographic knowledge.

Decision Making Domain Adaptation

Recent Advances on Neural Network Pruning at Initialization

2 code implementations11 Mar 2021 Huan Wang, Can Qin, Yue Bai, Yulun Zhang, Yun Fu

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 a randomly initialized network.

Benchmarking Network Pruning

Context Reasoning Attention Network for Image Super-Resolution

no code implementations ICCV 2021 Yulun Zhang, Donglai Wei, Can Qin, Huan Wang, Hanspeter Pfister, Yun Fu

However, the basic convolutional layer in CNNs is designed to extract local patterns, lacking the ability to model global context.

Image Super-Resolution

Aspect-based Sentiment Classification via Reinforcement Learning

no code implementations1 Jan 2021 Lichen Wang, Bo Zong, Yunyu Liu, Can Qin, Wei Cheng, Wenchao Yu, Xuchao Zhang, Haifeng Chen, Yun Fu

As texts always contain a large proportion of task-irrelevant words, accurate alignment between aspects and their sentimental descriptions is the most crucial and challenging step.

Classification General Classification +4

SuperFront: From Low-resolution to High-resolution Frontal Face Synthesis

no code implementations7 Dec 2020 Yu Yin, Joseph P. Robinson, Songyao Jiang, Yue Bai, Can Qin, Yun Fu

Even as impressive milestones are achieved in synthesizing faces, the importance of preserving identity is needed in practice and should not be overlooked.

Face Generation Generative Adversarial Network +2

Face Recognition: Too Bias, or Not Too Bias?

1 code implementation16 Feb 2020 Joseph P. Robinson, Gennady Livitz, Yann Henon, Can Qin, Yun Fu, Samson Timoner

Thus, the conventional approach of learning a global threshold for all pairs resulting in performance gaps among subgroups.

Face Recognition

Contradictory Structure Learning for Semi-supervised Domain Adaptation

1 code implementation6 Feb 2020 Can Qin, Lichen Wang, Qianqian Ma, Yu Yin, Huan Wang, Yun Fu

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 of the decision boundary towards the source domain.

Clustering Domain Adaptation +1

Self-Directed Online Machine Learning for Topology Optimization

1 code implementation4 Feb 2020 Changyu Deng, Yizhou Wang, Can Qin, Yun Fu, Wei Lu

A small number of training data is generated dynamically based on the DNN's prediction of the optimum.

BIG-bench Machine Learning Stochastic Optimization

PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation

2 code implementations NeurIPS 2019 Can Qin, Haoxuan You, Lichen Wang, C. -C. Jay Kuo, Yun Fu

Specifically, most general-purpose DA methods that struggle for global feature alignment and ignore local geometric information are not suitable for 3D domain alignment.

Unsupervised Domain Adaptation

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