226 citations · 590 across the 36 of their papers we have counts for
38 papers
UltraMatch: Transport Path Routing for Ultra-Fast and Memory-Efficient Image Matching
Jiajun Le, Yifan Lu, Zizhuo Li +3
Despite recent advances in accuracy and efficiency, coarse matching remains an indispensable yet costly stage in existing semi-dense matchers due to dense token-level matching. We…
A Comprehensive Survey on 3D Content Generation
Jian Liu, Xiaoshui Huang, Tianyu Huang +8
Recent years have witnessed remarkable advances in artificial intelligence generated content(AIGC), with diverse input modalities, e.g., text, image, video, audio and 3D. The 3D is…
HiCAST: Highly Customized Arbitrary Style Transfer with Adapter Enhanced Diffusion Models
Hanzhang Wang, Haoran Wang, Jinze Yang +7
The goal of Arbitrary Style Transfer (AST) is injecting the artistic features of a style reference into a given image/video. Existing methods usually focus on pursuing the balance…
Semantic Ensemble Loss and Latent Refinement for High-Fidelity Neural Image Compression
Daxin Li, Yuanchao Bai, Kai Wang +2
Recent advancements in neural compression have surpassed traditional codecs in PSNR and MS-SSIM measurements. However, at low bit-rates, these methods can introduce visually disple…
Transforming Image Super-Resolution: A ConvFormer-based Efficient Approach
Gang Wu, Junjun Jiang, Junpeng Jiang +1
Recent progress in single-image super-resolution (SISR) has achieved remarkable performance, yet the computational costs of these methods remain a challenge for deployment on resou…
On the Dynamics Under the Unhinged Loss and Beyond
Xiong Zhou, Xianming Liu, Hanzhang Wang +3
Recent works have studied implicit biases in deep learning, especially the behavior of last-layer features and classifier weights. However, they usually need to simplify the interm…
Learning from History: Task-agnostic Model Contrastive Learning for Image Restoration
Gang Wu, Junjun Jiang, Kui Jiang +1
Contrastive learning has emerged as a prevailing paradigm for high-level vision tasks, which, by introducing properly negative samples, has also been exploited for low-level vision…
Fully Convolutional Network for Lightweight Image Super-Resolution
Gang Wu, Junjun Jiang, Kui Jiang +1
Deep models have achieved significant process on single image super-resolution (SISR) tasks, in particular large models with large kernel ( or more). However, the heavy c…
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation
Lingdong Kong, Yaru Niu, Shaoyuan Xie +39
Accurate depth estimation under out-of-distribution (OoD) scenarios, such as adverse weather conditions, sensor failure, and noise contamination, is desirable for safety-critical a…
Backdoor Attacks Against Incremental Learners: An Empirical Evaluation Study
Yiqi Zhong, Xianming Liu, Deming Zhai +2
Large amounts of incremental learning algorithms have been proposed to alleviate the catastrophic forgetting issue arises while dealing with sequential data on a time series. Howev…
Super-Resolving Face Image by Facial Parsing Information
Chenyang Wang, Junjun Jiang, Zhiwei Zhong +2
Face super-resolution is a technology that transforms a low-resolution face image into the corresponding high-resolution one. In this paper, we build a novel parsing map guided fac…
Incorporating Transformer Designs into Convolutions for Lightweight Image Super-Resolution
Gang Wu, Junjun Jiang, Yuanchao Bai +1
In recent years, the use of large convolutional kernels has become popular in designing convolutional neural networks due to their ability to capture long-range dependencies and pr…
Augment and Criticize: Exploring Informative Samples for Semi-Supervised Monocular 3D Object Detection
Zhenyu Li, Zhipeng Zhang, Heng Fan +4
In this paper, we improve the challenging monocular 3D object detection problem with a general semi-supervised framework. Specifically, having observed that the bottleneck of this…
Guided Depth Map Super-resolution: A Survey
Zhiwei Zhong, Xianming Liu, Junjun Jiang +2
Guided depth map super-resolution (GDSR), which aims to reconstruct a high-resolution (HR) depth map from a low-resolution (LR) observation with the help of a paired HR color image…
Image Deblurring by Exploring In-depth Properties of Transformer
Pengwei Liang, Junjun Jiang, Xianming Liu +1
Image deblurring continues to achieve impressive performance with the development of generative models. Nonetheless, there still remains a displeasing problem if one wants to impro…
Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report
Andrey Ignatov, Grigory Malivenko, Radu Timofte +36
Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks. Thus…
Multi-Camera Collaborative Depth Prediction via Consistent Structure Estimation
Jialei Xu, Xianming Liu, Yuanchao Bai +4
Depth map estimation from images is an important task in robotic systems. Existing methods can be categorized into two groups including multi-view stereo and monocular depth estima…
LiteDepth: Digging into Fast and Accurate Depth Estimation on Mobile Devices
Zhenyu Li, Zehui Chen, Jialei Xu +2
Monocular depth estimation is an essential task in the computer vision community. While tremendous successful methods have obtained excellent results, most of them are computationa…
Prototype-Anchored Learning for Learning with Imperfect Annotations
Xiong Zhou, Xianming Liu, Deming Zhai +3
The success of deep neural networks greatly relies on the availability of large amounts of high-quality annotated data, which however are difficult or expensive to obtain. The resu…
Learning Towards the Largest Margins
Xiong Zhou, Xianming Liu, Deming Zhai +3
One of the main challenges for feature representation in deep learning-based classification is the design of appropriate loss functions that exhibit strong discriminative power. Th…
Towards Model Generalization for Monocular 3D Object Detection
Zhenyu Li, Zehui Chen, Ang Li +4
Monocular 3D object detection (Mono3D) has achieved tremendous improvements with emerging large-scale autonomous driving datasets and the rapid development of deep learning techniq…
GLaMa: Joint Spatial and Frequency Loss for General Image Inpainting
Zeyu Lu, Junjun Jiang, Junqin Huang +2
The purpose of image inpainting is to recover scratches and damaged areas using context information from remaining parts. In recent years, thanks to the resurgence of convolutional…
ReSmooth: Detecting and Utilizing OOD Samples when Training with Data Augmentation
Chenyang Wang, Junjun Jiang, Xiong Zhou +1
Data augmentation (DA) is a widely used technique for enhancing the training of deep neural networks. Recent DA techniques which achieve state-of-the-art performance always meet th…
Unsupervised Domain Adaptation for Monocular 3D Object Detection via Self-Training
Zhenyu Li, Zehui Chen, Ang Li +4
Monocular 3D object detection (Mono3D) has achieved unprecedented success with the advent of deep learning techniques and emerging large-scale autonomous driving datasets. However,…