20 citations · 112 across the 18 of their papers we have counts for
18 papers
Center Focusing Network for Real-Time LiDAR Panoptic Segmentation
Xiaoyan Li, Gang Zhang, Boyue Wang +2
LiDAR panoptic segmentation facilitates an autonomous vehicle to comprehensively understand the surrounding objects and scenes and is required to run in real time. The recent propo…
Open-TransMind: A New Baseline and Benchmark for 1st Foundation Model Challenge of Intelligent Transportation
Yifeng Shi, Feng Lv, Xinliang Wang +5
With the continuous improvement of computing power and deep learning algorithms in recent years, the foundation model has grown in popularity. Because of its powerful capabilities…
A Unified Continual Learning Framework with General Parameter-Efficient Tuning
Qiankun Gao, Chen Zhao, Yifan Sun +4
The "pre-training downstream adaptation" presents both new opportunities and challenges for Continual Learning (CL). Although the recent state-of-the-art in CL is ach…
CEDNet: A Cascade Encoder-Decoder Network for Dense Prediction
Gang Zhang, Ziyi Li, Chufeng Tang +2
Multi-scale features are essential for dense prediction tasks, such as object detection, instance segmentation, and semantic segmentation. The prevailing methods usually utilize a…
Dual Relation Knowledge Distillation for Object Detection
Zhenliang Ni, Fukui Yang, Shengzhao Wen +1
Knowledge distillation is an effective method for model compression. However, it is still a challenging topic to apply knowledge distillation to detection tasks. There are two key…
Group DETR v2: Strong Object Detector with Encoder-Decoder Pretraining
Qiang Chen, Jian Wang, Chuchu Han +12
We present a strong object detector with encoder-decoder pretraining and finetuning. Our method, called Group DETR v2, is built upon a vision transformer encoder ViT-Huge~\cite{dos…
Active Pointly-Supervised Instance Segmentation
Chufeng Tang, Lingxi Xie, Gang Zhang +3
The requirement of expensive annotations is a major burden for training a well-performed instance segmentation model. In this paper, we present an economic active learning setting,…
UFO: Unified Feature Optimization
Teng Xi, Yifan Sun, Deli Yu +13
This paper proposes a novel Unified Feature Optimization (UFO) paradigm for training and deploying deep models under real-world and large-scale scenarios, which requires a collecti…
The Winning Solution to the iFLYTEK Challenge 2021 Cultivated Land Extraction from High-Resolution Remote Sensing Image
Zhen Zhao, Yuqiu Liu, Gang Zhang +2
Extracting cultivated land accurately from high-resolution remote images is a basic task for precision agriculture. This report introduces our solution to the iFLYTEK challenge 202…
Equalized Focal Loss for Dense Long-Tailed Object Detection
Bo Li, Yongqiang Yao, Jingru Tan +4
Despite the recent success of long-tailed object detection, almost all long-tailed object detectors are developed based on the two-stage paradigm. In practice, one-stage detectors…
Dynamic Class Queue for Large Scale Face Recognition In the Wild
Bi Li, Teng Xi, Gang Zhang +5
Learning discriminative representation using large-scale face datasets in the wild is crucial for real-world applications, yet it remains challenging. The difficulties lie in many…
RefineMask: Towards High-Quality Instance Segmentation with Fine-Grained Features
Gang Zhang, Xin Lu, Jingru Tan +4
The two-stage methods for instance segmentation, e.g. Mask R-CNN, have achieved excellent performance recently. However, the segmented masks are still very coarse due to the downsa…
Equalization Loss v2: A New Gradient Balance Approach for Long-tailed Object Detection
Jingru Tan, Xin Lu, Gang Zhang +2
Recently proposed decoupled training methods emerge as a dominant paradigm for long-tailed object detection. But they require an extra fine-tuning stage, and the disjointed optimiz…
AutoPruning for Deep Neural Network with Dynamic Channel Masking
Baopu Li, Yanwen Fan, Zhihong Pan +1
Modern deep neural network models are large and computationally intensive. One typical solution to this issue is model pruning. However, most current pruning algorithms depend on h…
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
Pengxu Wei, Hannan Lu, Radu Timofte +68
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020. This ch…
1st Place Solution of LVIS Challenge 2020: A Good Box is not a Guarantee of a Good Mask
Jingru Tan, Gang Zhang, Hanming Deng +4
This article introduces the solutions of the team lvisTraveler for LVIS Challenge 2020. In this work, two characteristics of LVIS dataset are mainly considered: the long-tailed dis…
Real Image Super Resolution Via Heterogeneous Model Ensemble using GP-NAS
Zhihong Pan, Baopu Li, Teng Xi +5
With advancement in deep neural network (DNN), recent state-of-the-art (SOTA) image superresolution (SR) methods have achieved impressive performance using deep residual network wi…
NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results
Abdelrahman Abdelhamed, Mahmoud Afifi, Radu Timofte +87
This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results. The challenge is a new versi…