2.4k citations · 2.9k across the 18 of their papers we have counts for
18 papers
FB-BEV: BEV Representation from Forward-Backward View Transformations
Zhiqi Li, Zhiding Yu, Wenhai Wang +3
View Transformation Module (VTM), where transformations happen between multi-view image features and Bird-Eye-View (BEV) representation, is a crucial step in camera-based BEV perce…
The All-Seeing Project: Towards Panoptic Visual Recognition and Understanding of the Open World
Weiyun Wang, Min Shi, Qingyun Li +11
We present the All-Seeing (AS) project: a large-scale data and model for recognizing and understanding everything in the open world. Using a scalable data engine that incorporates…
AVSegFormer: Audio-Visual Segmentation with Transformer
Shengyi Gao, Zhe Chen, Guo Chen +2
The combination of audio and vision has long been a topic of interest in the multi-modal community. Recently, a new audio-visual segmentation (AVS) task has been introduced, aiming…
VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks
Wenhai Wang, Zhe Chen, Xiaokang Chen +8
Large language models (LLMs) have notably accelerated progress towards artificial general intelligence (AGI), with their impressive zero-shot capacity for user-tailored tasks, endo…
GridFormer: Residual Dense Transformer with Grid Structure for Image Restoration in Adverse Weather Conditions
Tao Wang, Kaihao Zhang, Ziqian Shao +6
Image restoration in adverse weather conditions is a difficult task in computer vision. In this paper, we propose a novel transformer-based framework called GridFormer which serves…
Graph Propagation Transformer for Graph Representation Learning
Zhe Chen, Hao Tan, Tao Wang +5
This paper presents a novel transformer architecture for graph representation learning. The core insight of our method is to fully consider the information propagation among nodes…
DDP: Diffusion Model for Dense Visual Prediction
Yuanfeng Ji, Zhe Chen, Enze Xie +6
We propose a simple, efficient, yet powerful framework for dense visual predictions based on the conditional diffusion pipeline. Our approach follows a "noise-to-map" generative pa…
Champion Solution for the WSDM2023 Toloka VQA Challenge
Shengyi Gao, Zhe Chen, Guo Chen +2
In this report, we present our champion solution to the WSDM2023 Toloka Visual Question Answering (VQA) Challenge. Different from the common VQA and visual grounding (VG) tasks, th…
Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method
Tao Wang, Kaihao Zhang, Tianrun Shen +3
As the quality of optical sensors improves, there is a need for processing large-scale images. In particular, the ability of devices to capture ultra-high definition (UHD) images a…
Restoring Vision in Hazy Weather with Hierarchical Contrastive Learning
Tao Wang, Guangpin Tao, Wanglong Lu +4
Image restoration under hazy weather condition, which is called single image dehazing, has been of significant interest for various computer vision applications. In recent years, d…
InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions
Wenhai Wang, Jifeng Dai, Zhe Chen +9
Compared to the great progress of large-scale vision transformers (ViTs) in recent years, large-scale models based on convolutional neural networks (CNNs) are still in an early sta…
Deep Face Restoration: A Survey
Tao Wang, Kaihao Zhang, Jiankang Deng +3
Face Restoration (FR) aims to restore High-Quality (HQ) faces from Low-Quality (LQ) input images, which is a domain-specific image restoration problem in the low-level computer vis…
Incremental Few-Shot Semantic Segmentation via Embedding Adaptive-Update and Hyper-class Representation
Guangchen Shi, Yirui Wu, Jun Liu +3
Incremental few-shot semantic segmentation (IFSS) targets at incrementally expanding model's capacity to segment new class of images supervised by only a few samples. However, feat…
Vision Transformer Adapter for Dense Predictions
Zhe Chen, Yuchen Duan, Wenhai Wang +4
This work investigates a simple yet powerful dense prediction task adapter for Vision Transformer (ViT). Unlike recently advanced variants that incorporate vision-specific inductiv…
BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers
Zhiqi Li, Wenhai Wang, Hongyang Li +5
3D visual perception tasks, including 3D detection and map segmentation based on multi-camera images, are essential for autonomous driving systems. In this work, we present a new f…
FAST: Faster Arbitrarily-Shaped Text Detector with Minimalist Kernel Representation
Zhe Chen, Jiahao Wang, Wenhai Wang +4
We propose an accurate and efficient scene text detection framework, termed FAST (i.e., faster arbitrarily-shaped text detector). Different from recent advanced text detectors that…
Panoptic SegFormer: Delving Deeper into Panoptic Segmentation with Transformers
Zhiqi Li, Wenhai Wang, Enze Xie +5
Panoptic segmentation involves a combination of joint semantic segmentation and instance segmentation, where image contents are divided into two types: things and stuff. We present…
PVT v2: Improved Baselines with Pyramid Vision Transformer
Wenhai Wang, Enze Xie, Xiang Li +6
Transformer recently has presented encouraging progress in computer vision. In this work, we present new baselines by improving the original Pyramid Vision Transformer (PVT v1) by…