324 citations · 893 across the 40 of their papers we have counts for
6 papers · 1 filter
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…
S-Prompts Learning with Pre-trained Transformers: An Occam's Razor for Domain Incremental Learning
Yabin Wang, Zhiwu Huang, Xiaopeng Hong
State-of-the-art deep neural networks are still struggling to address the catastrophic forgetting problem in continual learning. In this paper, we propose one simple paradigm (name…
A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials
Chuqiao Li, Zhiwu Huang, Danda Pani Paudel +4
There have been emerging a number of benchmarks and techniques for the detection of deepfakes. However, very few works study the detection of incrementally appearing deepfakes in t…
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…
Boosting Crowd Counting via Multifaceted Attention
Hui Lin, Zhiheng Ma, Rongrong Ji +2
This paper focuses on the challenging crowd counting task. As large-scale variations often exist within crowd images, neither fixed-size convolution kernel of CNN nor fixed-size at…