18.1k citations · 21.9k across the 47 of their papers we have counts for
21 papers · 1 filter
XnODR and XnIDR: Two Accurate and Fast Fully Connected Layers For Convolutional Neural Networks
Jian Sun, Ali Pourramezan Fard, Mohammad H. Mahoor
Capsule Network is powerful at defining the positional relationship between features in deep neural networks for visual recognition tasks, but it is computationally expensive and n…
Instance-Conditional Knowledge Distillation for Object Detection
Zijian Kang, Peizhen Zhang, Xiangyu Zhang +2
Knowledge distillation has shown great success in classification, however, it is still challenging for detection. In a typical image for detection, representations from different l…
Partial to Whole Knowledge Distillation: Progressive Distilling Decomposed Knowledge Boosts Student Better
Xuanyang Zhang, Xiangyu Zhang, Jian Sun
Knowledge distillation field delicately designs various types of knowledge to shrink the performance gap between compact student and large-scale teacher. These existing distillatio…
LGD: Label-guided Self-distillation for Object Detection
Peizhen Zhang, Zijian Kang, Tong Yang +3
In this paper, we propose the first self-distillation framework for general object detection, termed LGD (Label-Guided self-Distillation). Previous studies rely on a strong pretrai…
Anchor DETR: Query Design for Transformer-Based Object Detection
Yingming Wang, Xiangyu Zhang, Tong Yang +1
In this paper, we propose a novel query design for the transformer-based object detection. In previous transformer-based detectors, the object queries are a set of learned embeddin…
YOLOX: Exceeding YOLO Series in 2021
Zheng Ge, Songtao Liu, Feng Wang +2
In this report, we present some experienced improvements to YOLO series, forming a new high-performance detector -- YOLOX. We switch the YOLO detector to an anchor-free manner and…
Fully Convolutional Networks for Panoptic Segmentation with Point-based Supervision
Yanwei Li, Hengshuang Zhao, Xiaojuan Qi +6
In this paper, we present a conceptually simple, strong, and efficient framework for fully- and weakly-supervised panoptic segmentation, called Panoptic FCN. Our approach aims to r…
Workshop on Autonomous Driving at CVPR 2021: Technical Report for Streaming Perception Challenge
Songyang Zhang, Lin Song, Songtao Liu +4
In this report, we introduce our real-time 2D object detection system for the realistic autonomous driving scenario. Our detector is built on a newly designed YOLO model, called YO…
NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results
Goutam Bhat, Martin Danelljan, Radu Timofte +25
This paper reviews the NTIRE2021 challenge on burst super-resolution. Given a RAW noisy burst as input, the task in the challenge was to generate a clean RGB image with 4 times hig…
ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging
Zhen Liu, Wenjie Lin, Xinpeng Li +6
In this paper, we present an attention-guided deformable convolutional network for hand-held multi-frame high dynamic range (HDR) imaging, namely ADNet. This problem comprises two…
Generalized Few-Shot Object Detection without Forgetting
Zhibo Fan, Yuchen Ma, Zeming Li +1
Recently few-shot object detection is widely adopted to deal with data-limited situations. While most previous works merely focus on the performance on few-shot categories, we clai…
Fast Camera Image Denoising on Mobile GPUs with Deep Learning, Mobile AI 2021 Challenge: Report
Andrey Ignatov, Kim Byeoung-su, Radu Timofte +29
Image denoising is one of the most critical problems in mobile photo processing. While many solutions have been proposed for this task, they are usually working with synthetic data…
Points as Queries: Weakly Semi-supervised Object Detection by Points
Liangyu Chen, Tong Yang, Xiangyu Zhang +2
We propose a novel point annotated setting for the weakly semi-supervised object detection task, in which the dataset comprises small fully annotated images and large weakly annota…
IQDet: Instance-wise Quality Distribution Sampling for Object Detection
Yuchen Ma, Songtao Liu, Zeming Li +1
We propose a dense object detector with an instance-wise sampling strategy, named IQDet. Instead of using human prior sampling strategies, we first extract the regional feature of…
Distribution Alignment: A Unified Framework for Long-tail Visual Recognition
Songyang Zhang, Zeming Li, Shipeng Yan +2
Despite the recent success of deep neural networks, it remains challenging to effectively model the long-tail class distribution in visual recognition tasks. To address this proble…
OTA: Optimal Transport Assignment for Object Detection
Zheng Ge, Songtao Liu, Zeming Li +2
Recent advances in label assignment in object detection mainly seek to independently define positive/negative training samples for each ground-truth (gt) object. In this paper, we…
You Only Look One-level Feature
Qiang Chen, Yingming Wang, Tong Yang +3
This paper revisits feature pyramids networks (FPN) for one-stage detectors and points out that the success of FPN is due to its divide-and-conquer solution to the optimization pro…
FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation
Yisheng He, Haibin Huang, Haoqiang Fan +2
In this work, we present FFB6D, a Full Flow Bidirectional fusion network designed for 6D pose estimation from a single RGBD image. Our key insight is that appearance information in…
Momentum^2 Teacher: Momentum Teacher with Momentum Statistics for Self-Supervised Learning
Zeming Li, Songtao Liu, Jian Sun
In this paper, we present a novel approach, Momentum Teacher, for student-teacher based self-supervised learning. The approach performs momentum update on both network weights…
Neural Architecture Search with Random Labels
Xuanyang Zhang, Pengfei Hou, Xiangyu Zhang +1
In this paper, we investigate a new variant of neural architecture search (NAS) paradigm -- searching with random labels (RLNAS). The task sounds counter-intuitive for most existin…
RepVGG: Making VGG-style ConvNets Great Again
Xiaohan Ding, Xiangyu Zhang, Ningning Ma +3
We present a simple but powerful architecture of convolutional neural network, which has a VGG-like inference-time body composed of nothing but a stack of 3x3 convolution and ReLU,…