202 citations · 543 across the 24 of their papers we have counts for
24 papers
DUAW: Data-free Universal Adversarial Watermark against Stable Diffusion Customization
Xiaoyu Ye, Hao Huang, Jiaqi An +1
Stable Diffusion (SD) customization approaches enable users to personalize SD model outputs, greatly enhancing the flexibility and diversity of AI art. However, they also allow ind…
DynamicDet: A Unified Dynamic Architecture for Object Detection
Zhihao Lin, Yongtao Wang, Jinhe Zhang +1
Dynamic neural network is an emerging research topic in deep learning. With adaptive inference, dynamic models can achieve remarkable accuracy and computational efficiency. However…
A Simple and Generic Framework for Feature Distillation via Channel-wise Transformation
Ziwei Liu, Yongtao Wang, Xiaojie Chu
Knowledge distillation is a popular technique for transferring the knowledge from a large teacher model to a smaller student model by mimicking. However, distillation by directly a…
BEV-MAE: Bird's Eye View Masked Autoencoders for Point Cloud Pre-training in Autonomous Driving Scenarios
Zhiwei Lin, Yongtao Wang, Shengxiang Qi +2
Existing LiDAR-based 3D object detection methods for autonomous driving scenarios mainly adopt the training-from-scratch paradigm. Unfortunately, this paradigm heavily relies on la…
T-SEA: Transfer-based Self-Ensemble Attack on Object Detection
Hao Huang, Ziyan Chen, Huanran Chen +2
Compared to query-based black-box attacks, transfer-based black-box attacks do not require any information of the attacked models, which ensures their secrecy. However, most existi…
Differentiable Architecture Search with Random Features
Xuanyang Zhang, Yonggang Li, Xiangyu Zhang +2
Differentiable architecture search (DARTS) has significantly promoted the development of NAS techniques because of its high search efficiency and effectiveness but suffers from per…
FlowNAS: Neural Architecture Search for Optical Flow Estimation
Zhiwei Lin, Tingting Liang, Taihong Xiao +3
Existing optical flow estimators usually employ the network architectures typically designed for image classification as the encoder to extract per-pixel features. However, due to…
Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detection
Kaicheng Yu, Tang Tao, Hongwei Xie +10
There are two critical sensors for 3D perception in autonomous driving, the camera and the LiDAR. The camera provides rich semantic information such as color, texture, and the LiDA…
BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework
Tingting Liang, Hongwei Xie, Kaicheng Yu +6
Fusing the camera and LiDAR information has become a de-facto standard for 3D object detection tasks. Current methods rely on point clouds from the LiDAR sensor as queries to lever…
IterVM: Iterative Vision Modeling Module for Scene Text Recognition
Xiaojie Chu, Yongtao Wang
Scene text recognition (STR) is a challenging problem due to the imperfect imagery conditions in natural images. State-of-the-art methods utilize both visual cues and linguistic kn…
Training Protocol Matters: Towards Accurate Scene Text Recognition via Training Protocol Searching
Xiaojie Chu, Yongtao Wang, Chunhua Shen +2
The development of scene text recognition (STR) in the era of deep learning has been mainly focused on novel architectures of STR models. However, training protocol (i.e., settings…
Continual Contrastive Learning for Image Classification
Zhiwei Lin, Yongtao Wang, Hongxiang Lin
Recently, self-supervised representation learning gives further development in multimedia technology. Most existing self-supervised learning methods are applicable to packaged data…
CBNet: A Composite Backbone Network Architecture for Object Detection
Tingting Liang, Xiaojie Chu, Yudong Liu +5
Modern top-performing object detectors depend heavily on backbone networks, whose advances bring consistent performance gains through exploring more effective network structures. I…
CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes
Hao Huang, Yongtao Wang, Zhaoyu Chen +7
Malicious applications of deepfakes (i.e., technologies generating target facial attributes or entire faces from facial images) have posed a huge threat to individuals' reputation…
RPATTACK: Refined Patch Attack on General Object Detectors
Hao Huang, Yongtao Wang, Zhaoyu Chen +3
Nowadays, general object detectors like YOLO and Faster R-CNN as well as their variants are widely exploited in many applications. Many works have revealed that these detectors are…
OPANAS: One-Shot Path Aggregation Network Architecture Search for Object Detection
Tingting Liang, Yongtao Wang, Zhi Tang +2
Recently, neural architecture search (NAS) has been exploited to design feature pyramid networks (FPNs) and achieved promising results for visual object detection. Encouraged by th…
Learning a Single Model with a Wide Range of Quality Factors for JPEG Image Artifacts Removal
Jianwei Li, Yongtao Wang, Haihua Xie +1
Lossy compression brings artifacts into the compressed image and degrades the visual quality. In recent years, many compression artifacts removal methods based on convolutional neu…
GSTO: Gated Scale-Transfer Operation for Multi-Scale Feature Learning in Pixel Labeling
Zhuoying Wang, Yongtao Wang, Zhi Tang +4
Existing CNN-based methods for pixel labeling heavily depend on multi-scale features to meet the requirements of both semantic comprehension and detail preservation. State-of-the-a…
MixTConv: Mixed Temporal Convolutional Kernels for Efficient Action Recogntion
Kaiyu Shan, Yongtao Wang, Zhuoying Wang +4
To efficiently extract spatiotemporal features of video for action recognition, most state-of-the-art methods integrate 1D temporal convolution into a conventional 2D CNN backbone.…
MFPN: A Novel Mixture Feature Pyramid Network of Multiple Architectures for Object Detection
Tingting Liang, Yongtao Wang, Qijie Zhao +3
Feature pyramids are widely exploited in many detectors to solve the scale variation problem for object detection. In this paper, we first investigate the Feature Pyramid Network (…
CBNet: A Novel Composite Backbone Network Architecture for Object Detection
Yudong Liu, Yongtao Wang, Siwei Wang +4
In existing CNN based detectors, the backbone network is a very important component for basic feature extraction, and the performance of the detectors highly depends on it. In this…
M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network
Qijie Zhao, Tao Sheng, Yongtao Wang +4
Feature pyramids are widely exploited by both the state-of-the-art one-stage object detectors (e.g., DSSD, RetinaNet, RefineDet) and the two-stage object detectors (e.g., Mask R-CN…
Deep Dual Pyramid Network for Barcode Segmentation using Barcode-30k Database
Qijie Zhao, Feng Ni, Yang Song +2
Digital signs(such as barcode or QR code) are widely used in our daily life, and for many applications, we need to localize them on images. However, difficult cases such as targets…
CFENet: An Accurate and Efficient Single-Shot Object Detector for Autonomous Driving
Qijie Zhao, Tao Sheng, Yongtao Wang +2
The ability to detect small objects and the speed of the object detector are very important for the application of autonomous driving, and in this paper, we propose an effective ye…