420 citations · 2.4k across the 126 of their papers we have counts for
15 papers · 1 filter
Exploring Complicated Search Spaces with Interleaving-Free Sampling
Yunjie Tian, Lingxi Xie, Jiemin Fang +3
The existing neural architecture search algorithms are mostly working on search spaces with short-distance connections. We argue that such designs, though safe and stable, obstacle…
NeuSample: Neural Sample Field for Efficient View Synthesis
Jiemin Fang, Lingxi Xie, Xinggang Wang +3
Neural radiance fields (NeRF) have shown great potentials in representing 3D scenes and synthesizing novel views, but the computational overhead of NeRF at the inference stage is s…
Semantic-Aware Generation for Self-Supervised Visual Representation Learning
Yunjie Tian, Lingxi Xie, Xiaopeng Zhang +6
In this paper, we propose a self-supervised visual representation learning approach which involves both generative and discriminative proxies, where we focus on the former part by…
Consensus Synergizes with Memory: A Simple Approach for Anomaly Segmentation in Urban Scenes
Jiazhong Cen, Zenkun Jiang, Lingxi Xie +3
Anomaly segmentation is a crucial task for safety-critical applications, such as autonomous driving in urban scenes, where the goal is to detect out-of-distribution (OOD) objects w…
CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesis
Peng Zhou, Lingxi Xie, Bingbing Ni +1
The style-based GAN (StyleGAN) architecture achieved state-of-the-art results for generating high-quality images, but it lacks explicit and precise control over camera poses. The r…
Rectifying the Shortcut Learning of Background for Few-Shot Learning
Xu Luo, Longhui Wei, Liangjian Wen +4
The category gap between training and evaluation has been characterised as one of the main obstacles to the success of Few-Shot Learning (FSL). In this paper, we for the first time…
Bag of Instances Aggregation Boosts Self-supervised Distillation
Haohang Xu, Jiemin Fang, Xiaopeng Zhang +5
Recent advances in self-supervised learning have experienced remarkable progress, especially for contrastive learning based methods, which regard each image as well as its augmenta…
Exploring the Diversity and Invariance in Yourself for Visual Pre-Training Task
Longhui Wei, Lingxi Xie, Wengang Zhou +2
Recently, self-supervised learning methods have achieved remarkable success in visual pre-training task. By simply pulling the different augmented views of each image together or o…
What Is Considered Complete for Visual Recognition?
Lingxi Xie, Xiaopeng Zhang, Longhui Wei +2
This is an opinion paper. We hope to deliver a key message that current visual recognition systems are far from complete, i.e., recognizing everything that human can recognize, yet…
Conformer: Local Features Coupling Global Representations for Visual Recognition
Zhiliang Peng, Wei Huang, Shanzhi Gu +4
Within Convolutional Neural Network (CNN), the convolution operations are good at extracting local features but experience difficulty to capture global representations. Within visu…
MSG-Transformer: Exchanging Local Spatial Information by Manipulating Messenger Tokens
Jiemin Fang, Lingxi Xie, Xinggang Wang +3
Transformers have offered a new methodology of designing neural networks for visual recognition. Compared to convolutional networks, Transformers enjoy the ability of referring to…
Location-Sensitive Visual Recognition with Cross-IOU Loss
Kaiwen Duan, Lingxi Xie, Honggang Qi +3
Object detection, instance segmentation, and pose estimation are popular visual recognition tasks which require localizing the object by internal or boundary landmarks. This paper…
Visformer: The Vision-friendly Transformer
Zhengsu Chen, Lingxi Xie, Jianwei Niu +3
The past year has witnessed the rapid development of applying the Transformer module to vision problems. While some researchers have demonstrated that Transformer-based models enjo…
Spatiotemporal Transformer for Video-based Person Re-identification
Tianyu Zhang, Longhui Wei, Lingxi Xie +4
Recently, the Transformer module has been transplanted from natural language processing to computer vision. This paper applies the Transformer to video-based person re-identificati…
MagDR: Mask-guided Detection and Reconstruction for Defending Deepfakes
Zhikai Chen, Lingxi Xie, Shanmin Pang +2
Deepfakes raised serious concerns on the authenticity of visual contents. Prior works revealed the possibility to disrupt deepfakes by adding adversarial perturbations to the sourc…