activity
20182023
most citedA Survey on Methods and Theories of Quantized Neural Networks

199 citations · 330 across the 13 of their papers we have counts for

collaborators

13 papers

cs.CV2023★ 2 cited

Audio-Visual Class-Incremental Learning

Weiguo Pian, Shentong Mo, Yunhui Guo +1

In this paper, we introduce audio-visual class-incremental learning, a class-incremental learning scenario for audio-visual video recognition. We demonstrate that joint audio-visua…

cs.CV2023★ 1 cited

Unsupervised Feature Learning with Emergent Data-Driven Prototypicality

Yunhui Guo, Youren Zhang, Yubei Chen +1

Given an image set without any labels, our goal is to train a model that maps each image to a point in a feature space such that, not only proximity indicates visual similarity, bu…

cs.RO2023

Self-Supervised Unseen Object Instance Segmentation via Long-Term Robot Interaction

Yangxiao Lu, Ninad Khargonkar, Zesheng Xu +6

We introduce a novel robotic system for improving unseen object instance segmentation in the real world by leveraging long-term robot interaction with objects. Previous approaches…

cs.LG2022★ 2 cited

SCALE: Online Self-Supervised Lifelong Learning without Prior Knowledge

Xiaofan Yu, Yunhui Guo, Sicun Gao +1

Unsupervised lifelong learning refers to the ability to learn over time while memorizing previous patterns without supervision. Although great progress has been made in this direct…

cs.CV2022★ 2 cited

Unsupervised Hierarchical Semantic Segmentation with Multiview Cosegmentation and Clustering Transformers

Tsung-Wei Ke, Jyh-Jing Hwang, Yunhui Guo +2

Unsupervised semantic segmentation aims to discover groupings within and across images that capture object and view-invariance of a category without external supervision. Grouping…

cs.LG2021

CO-SNE: Dimensionality Reduction and Visualization for Hyperbolic Data

Yunhui Guo, Haoran Guo, Stella Yu

Hyperbolic space can naturally embed hierarchies that often exist in real-world data and semantics. While high-dimensional hyperbolic embeddings lead to better representations, mos…