activity
20172023
most citedSegment Anything in 3D with Radiance Fields

45 citations · 262 across the 27 of their papers we have counts for

collaborators
Showing 2021Show all

9 papers · 1 filter

cs.CV2021

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…

cs.CV2021

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…

cs.CV2021

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…

cs.CV2021★ 1 cited

Multi-dataset Pretraining: A Unified Model for Semantic Segmentation

Bowen Shi, Xiaopeng Zhang, Haohang Xu +4

Collecting annotated data for semantic segmentation is time-consuming and hard to scale up. In this paper, we for the first time propose a unified framework, termed as Multi-Datase…

cs.CV2021★ 6 cited

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…

cs.CV2021★ 4 cited

Semi-supervised Contrastive Learning with Similarity Co-calibration

Yuhang Zhang, Xiaopeng Zhang, Robert. C. Qiu +3

Semi-supervised learning acts as an effective way to leverage massive unlabeled data. In this paper, we propose a novel training strategy, termed as Semi-supervised Contrastive Lea…