18.1k citations · 21.9k across the 47 of their papers we have counts for
47 papers
Dynamic Grained Encoder for Vision Transformers
Lin Song, Songyang Zhang, Songtao Liu +5
Transformers, the de-facto standard for language modeling, have been recently applied for vision tasks. This paper introduces sparse queries for vision transformers to exploit the…
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…
PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images
Yingfei Liu, Junjie Yan, Fan Jia +5
In this paper, we propose PETRv2, a unified framework for 3D perception from multi-view images. Based on PETR, PETRv2 explores the effectiveness of temporal modeling, which utilize…
Unifying Voxel-based Representation with Transformer for 3D Object Detection
Yanwei Li, Yilun Chen, Xiaojuan Qi +3
In this work, we present a unified framework for multi-modality 3D object detection, named UVTR. The proposed method aims to unify multi-modality representations in the voxel space…
Progressive End-to-End Object Detection in Crowded Scenes
Anlin Zheng, Yuang Zhang, Xiangyu Zhang +2
In this paper, we propose a new query-based detection framework for crowd detection. Previous query-based detectors suffer from two drawbacks: first, multiple predictions will be i…
Focal Sparse Convolutional Networks for 3D Object Detection
Yukang Chen, Yanwei Li, Xiangyu Zhang +2
Non-uniformed 3D sparse data, e.g., point clouds or voxels in different spatial positions, make contribution to the task of 3D object detection in different ways. Existing basic co…
BSRT: Improving Burst Super-Resolution with Swin Transformer and Flow-Guided Deformable Alignment
Ziwei Luo, Youwei Li, Shen Cheng +6
This work addresses the Burst Super-Resolution (BurstSR) task using a new architecture, which requires restoring a high-quality image from a sequence of noisy, misaligned, and low-…
When NAS Meets Trees: An Efficient Algorithm for Neural Architecture Search
Guocheng Qian, Xuanyang Zhang, Guohao Li +5
The key challenge in neural architecture search (NAS) is designing how to explore wisely in the huge search space. We propose a new NAS method called TNAS (NAS with trees), which i…
Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs
Xiaohan Ding, Xiangyu Zhang, Yizhuang Zhou +3
We revisit large kernel design in modern convolutional neural networks (CNNs). Inspired by recent advances in vision transformers (ViTs), in this paper, we demonstrate that using a…
Simple Baselines for Image Restoration
Liangyu Chen, Xiaojie Chu, Xiangyu Zhang +1
Although there have been significant advances in the field of image restoration recently, the system complexity of the state-of-the-art (SOTA) methods is increasing as well, which…
Towards Self-Supervised Category-Level Object Pose and Size Estimation
Yisheng He, Haoqiang Fan, Haibin Huang +2
In this work, we tackle the challenging problem of category-level object pose and size estimation from a single depth image. Although previous fully-supervised works have demonstra…
Real-time Object Detection for Streaming Perception
Jinrong Yang, Songtao Liu, Zeming Li +2
Autonomous driving requires the model to perceive the environment and (re)act within a low latency for safety. While past works ignore the inevitable changes in the environment aft…
FS6D: Few-Shot 6D Pose Estimation of Novel Objects
Yisheng He, Yao Wang, Haoqiang Fan +2
6D object pose estimation networks are limited in their capability to scale to large numbers of object instances due to the close-set assumption and their reliance on high-fidelity…
Tree Energy Loss: Towards Sparsely Annotated Semantic Segmentation
Zhiyuan Liang, Tiancai Wang, Xiangyu Zhang +2
Sparsely annotated semantic segmentation (SASS) aims to train a segmentation network with coarse-grained (i.e., point-, scribble-, and block-wise) supervisions, where only a small…
Rebalanced Siamese Contrastive Mining for Long-Tailed Recognition
Zhisheng Zhong, Jiequan Cui, Zeming Li +3
Deep neural networks perform poorly on heavily class-imbalanced datasets. Given the promising performance of contrastive learning, we propose Rebalanced Siamese Contrastive Mining…
PETR: Position Embedding Transformation for Multi-View 3D Object Detection
Yingfei Liu, Tiancai Wang, Xiangyu Zhang +1
In this paper, we develop position embedding transformation (PETR) for multi-view 3D object detection. PETR encodes the position information of 3D coordinates into image features,…
Relieving Long-tailed Instance Segmentation via Pairwise Class Balance
Yin-Yin He, Peizhen Zhang, Xiu-Shen Wei +2
Long-tailed instance segmentation is a challenging task due to the extreme imbalance of training samples among classes. It causes severe biases of the head classes (with majority s…
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…