2.4k citations · 5k across the 43 of their papers we have counts for
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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…
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…
InternGPT: Solving Vision-Centric Tasks by Interacting with ChatGPT Beyond Language
Zhaoyang Liu, Yinan He, Wenhai Wang +17
We present an interactive visual framework named InternGPT, or iGPT for short. The framework integrates chatbots that have planning and reasoning capabilities, such as ChatGPT, wit…
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…
VLG: General Video Recognition with Web Textual Knowledge
Jintao Lin, Zhaoyang Liu, Wenhai Wang +2
Video recognition in an open and dynamic world is quite challenging, as we need to handle different settings such as close-set, long-tail, few-shot and open-set. By leveraging sema…
Uni-Perceiver v2: A Generalist Model for Large-Scale Vision and Vision-Language Tasks
Hao Li, Jinguo Zhu, Xiaohu Jiang +8
Despite the remarkable success of foundation models, their task-specific fine-tuning paradigm makes them inconsistent with the goal of general perception modeling. The key to elimi…
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…
Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe
Hongyang Li, Chonghao Sima, Jifeng Dai +19
Learning powerful representations in bird's-eye-view (BEV) for perception tasks is trending and drawing extensive attention both from industry and academia. Conventional approaches…
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…
Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEs
Jinguo Zhu, Xizhou Zhu, Wenhai Wang +4
To build an artificial neural network like the biological intelligence system, recent works have unified numerous tasks into a generalist model, which can process various tasks wit…
Uniform Masking: Enabling MAE Pre-training for Pyramid-based Vision Transformers with Locality
Xiang Li, Wenhai Wang, Lingfeng Yang +1
Masked AutoEncoder (MAE) has recently led the trends of visual self-supervision area by an elegant asymmetric encoder-decoder design, which significantly optimizes both the pre-tra…
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…
WegFormer: Transformers for Weakly Supervised Semantic Segmentation
Chunmeng Liu, Enze Xie, Wenjia Wang +3
Although convolutional neural networks (CNNs) have achieved remarkable progress in weakly supervised semantic segmentation (WSSS), the effective receptive field of CNN is insuffici…
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…
VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual Recognition
Changyao Tian, Wenhai Wang, Xizhou Zhu +2
Deep learning-based models encounter challenges when processing long-tailed data in the real world. Existing solutions usually employ some balancing strategies or transfer learning…
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…
ARTS: Eliminating Inconsistency between Text Detection and Recognition with Auto-Rectification Text Spotter
Humen Zhong, Jun Tang, Wenhai Wang +3
Recent approaches for end-to-end text spotting have achieved promising results. However, most of the current spotters were plagued by the inconsistency problem between text detecti…
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…
Learning Class-level Prototypes for Few-shot Learning
Minglei Yuan, Wenhai Wang, Tao Wang +3
Few-shot learning aims to recognize new categories using very few labeled samples. Although few-shot learning has witnessed promising development in recent years, most existing met…
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…
PolarMask++: Enhanced Polar Representation for Single-Shot Instance Segmentation and Beyond
Enze Xie, Wenhai Wang, Mingyu Ding +2
Reducing the complexity of the pipeline of instance segmentation is crucial for real-world applications. This work addresses this issue by introducing an anchor-box free and single…
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
Enze Xie, Wenhai Wang, Zhiding Yu +3
We present SegFormer, a simple, efficient yet powerful semantic segmentation framework which unifies Transformers with lightweight multilayer perception (MLP) decoders. SegFormer h…
PAN++: Towards Efficient and Accurate End-to-End Spotting of Arbitrarily-Shaped Text
Wenhai Wang, Enze Xie, Xiang Li +5
Scene text detection and recognition have been well explored in the past few years. Despite the progress, efficient and accurate end-to-end spotting of arbitrarily-shaped text rema…
Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization
Zhe Chen, Wenhai Wang, Enze Xie +2
We present an extremely simple Ultra-Resolution Style Transfer framework, termed URST, to flexibly process arbitrary high-resolution images (e.g., 10000x10000 pixels) style transfe…