66 citations · 103 across the 15 of their papers we have counts for
15 papers
Multi-modal Crowd Counting via Modal Emulation
Chenhao Wang, Xiaopeng Hong, Zhiheng Ma +3
Multi-modal crowd counting is a crucial task that uses multi-modal cues to estimate the number of people in crowded scenes. To overcome the gap between different modalities, we pro…
Reshaping the Online Data Buffering and Organizing Mechanism for Continual Test-Time Adaptation
Zhilin Zhu, Xiaopeng Hong, Zhiheng Ma +4
Continual Test-Time Adaptation (CTTA) involves adapting a pre-trained source model to continually changing unsupervised target domains. In this paper, we systematically analyze the…
Prompt Customization for Continual Learning
Yong Dai, Xiaopeng Hong, Yabin Wang +3
Contemporary continual learning approaches typically select prompts from a pool, which function as supplementary inputs to a pre-trained model. However, this strategy is hindered b…
Semi-supervised Counting via Pixel-by-pixel Density Distribution Modelling
Hui Lin, Zhiheng Ma, Rongrong Ji +4
This paper focuses on semi-supervised crowd counting, where only a small portion of the training data are labeled. We formulate the pixel-wise density value to regress as a probabi…
Gramformer: Learning Crowd Counting via Graph-Modulated Transformer
Hui Lin, Zhiheng Ma, Xiaopeng Hong +2
Transformer has been popular in recent crowd counting work since it breaks the limited receptive field of traditional CNNs. However, since crowd images always contain a large numbe…
Linguistic Profiling of Deepfakes: An Open Database for Next-Generation Deepfake Detection
Yabin Wang, Zhiwu Huang, Zhiheng Ma +1
The emergence of text-to-image generative models has revolutionized the field of deepfakes, enabling the creation of realistic and convincing visual content directly from textual d…
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…
Towards Practical Multi-Robot Hybrid Tasks Allocation for Autonomous Cleaning
Yabin Wang, Xiaopeng Hong, Zhiheng Ma +3
Task allocation plays a vital role in multi-robot autonomous cleaning systems, where multiple robots work together to clean a large area. However, most current studies mainly focus…
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.…
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…
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…
Anomaly Detection via Self-organizing Map
Ning Li, Kaitao Jiang, Zhiheng Ma +3
Anomaly detection plays a key role in industrial manufacturing for product quality control. Traditional methods for anomaly detection are rule-based with limited generalization abi…
Direct Measure Matching for Crowd Counting
Hui Lin, Xiaopeng Hong, Zhiheng Ma +4
Traditional crowd counting approaches usually use Gaussian assumption to generate pseudo density ground truth, which suffers from problems like inaccurate estimation of the Gaussia…
Bayesian Loss for Crowd Count Estimation with Point Supervision
Zhiheng Ma, Xing Wei, Xiaopeng Hong +1
In crowd counting datasets, each person is annotated by a point, which is usually the center of the head. And the task is to estimate the total count in a crowd scene. Most of the…