27 citations · 133 across the 21 of their papers we have counts for
30 papers · 1 filter
Can SAM Count Anything? An Empirical Study on SAM Counting
Zhiheng Ma, Xiaopeng Hong, Qinnan Shangguan
Meta AI recently released the Segment Anything model (SAM), which has garnered attention due to its impressive performance in class-agnostic segmenting. In this study, we explore t…
Remind of the Past: Incremental Learning with Analogical Prompts
Zhiheng Ma, Xiaopeng Hong, Beinan Liu +3
Although data-free incremental learning methods are memory-friendly, accurately estimating and counteracting representation shifts is challenging in the absence of historical data.…
Benchmarking Deepart Detection
Yabin Wang, Zhiwu Huang, Xiaopeng Hong
Deepfake technologies have been blurring the boundaries between the real and unreal, likely resulting in malicious events. By leveraging newly emerged deepfake technologies, deepfa…
Isolation and Impartial Aggregation: A Paradigm of Incremental Learning without Interference
Yabin Wang, Zhiheng Ma, Zhiwu Huang +3
This paper focuses on the prevalent performance imbalance in the stages of incremental learning. To avoid obvious stage learning bottlenecks, we propose a brand-new stage-isolation…
Semi-supervised Crowd Counting via Density Agency
Hui Lin, Zhiheng Ma, Xiaopeng Hong +2
In this paper, we propose a new agency-guided semi-supervised counting approach. First, we build a learnable auxiliary structure, namely the density agency to bring the recognized…
Deep Class Incremental Learning from Decentralized Data
Xiaohan Zhang, Songlin Dong, Jinjie Chen +3
In this paper, we focus on a new and challenging decentralized machine learning paradigm in which there are continuous inflows of data to be addressed and the data are stored in mu…