2.4k citations · 3.4k across the 57 of their papers we have counts for
17 papers · 2 filters
MetaCloth: Learning Unseen Tasks of Dense Fashion Landmark Detection from a Few Samples
Yuying Ge, Ruimao Zhang, Ping Luo
Recent advanced methods for fashion landmark detection are mainly driven by training convolutional neural networks on large-scale fashion datasets, which has a large number of anno…
Dynamic Token Normalization Improves Vision Transformers
Wenqi Shao, Yixiao Ge, Zhaoyang Zhang +4
Vision Transformer (ViT) and its variants (e.g., Swin, PVT) have achieved great success in various computer vision tasks, owing to their capability to learn long-range contextual i…
DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion
Peize Sun, Jinkun Cao, Yi Jiang +4
A typical pipeline for multi-object tracking (MOT) is to use a detector for object localization, and following re-identification (re-ID) for object association. This pipeline is pa…
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…
HR-NAS: Searching Efficient High-Resolution Neural Architectures with Lightweight Transformers
Mingyu Ding, Xiaochen Lian, Linjie Yang +4
High-resolution representations (HR) are essential for dense prediction tasks such as segmentation, detection, and pose estimation. Learning HR representations is typically ignored…
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…
Going Deeper Into Face Detection: A Survey
Shervin Minaee, Ping Luo, Zhe Lin +1
Face detection is a crucial first step in many facial recognition and face analysis systems. Early approaches for face detection were mainly based on classifiers built on top of ha…
Disentangled Cycle Consistency for Highly-realistic Virtual Try-On
Chongjian Ge, Yibing Song, Yuying Ge +3
Image virtual try-on replaces the clothes on a person image with a desired in-shop clothes image. It is challenging because the person and the in-shop clothes are unpaired. Existin…
Parser-Free Virtual Try-on via Distilling Appearance Flows
Yuying Ge, Yibing Song, Ruimao Zhang +3
Image virtual try-on aims to fit a garment image (target clothes) to a person image. Prior methods are heavily based on human parsing. However, slightly-wrong segmentation results…
Learning Versatile Neural Architectures by Propagating Network Codes
Mingyu Ding, Yuqi Huo, Haoyu Lu +5
This work explores how to design a single neural network capable of adapting to multiple heterogeneous vision tasks, such as image segmentation, 3D detection, and video recognition…
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…
Deeply Unsupervised Patch Re-Identification for Pre-training Object Detectors
Jian Ding, Enze Xie, Hang Xu +4
Unsupervised pre-training aims at learning transferable features that are beneficial for downstream tasks. However, most state-of-the-art unsupervised methods concentrate on learni…
Segmenting Transparent Object in the Wild with Transformer
Enze Xie, Wenjia Wang, Wenhai Wang +4
This work presents a new fine-grained transparent object segmentation dataset, termed Trans10K-v2, extending Trans10K-v1, the first large-scale transparent object segmentation data…
FAT: Learning Low-Bitwidth Parametric Representation via Frequency-Aware Transformation
Chaofan Tao, Rui Lin, Quan Chen +3
Learning convolutional neural networks (CNNs) with low bitwidth is challenging because performance may drop significantly after quantization. Prior arts often discretize the networ…
Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
Wenhai Wang, Enze Xie, Xiang Li +6
Although using convolutional neural networks (CNNs) as backbones achieves great successes in computer vision, this work investigates a simple backbone network useful for many dense…
DetCo: Unsupervised Contrastive Learning for Object Detection
Enze Xie, Jian Ding, Wenhai Wang +5
Unsupervised contrastive learning achieves great success in learning image representations with CNN. Unlike most recent methods that focused on improving accuracy of image classifi…