324 citations · 893 across the 40 of their papers we have counts for
40 papers
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.…
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…
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…
Object Counting: You Only Need to Look at One
Hui Lin, Xiaopeng Hong, Yabin Wang
This paper aims to tackle the challenging task of one-shot object counting. Given an image containing novel, previously unseen category objects, the goal of the task is to count al…
Short and Long Range Relation Based Spatio-Temporal Transformer for Micro-Expression Recognition
Liangfei Zhang, Xiaopeng Hong, Ognjen Arandjelovic +1
Being spontaneous, micro-expressions are useful in the inference of a person's true emotions even if an attempt is made to conceal them. Due to their short duration and low intensi…
3D Visual Tracking Framework with Deep Learning for Asteroid Exploration
Dong Zhou, Gunaghui Sun, Xiaopeng Hong
3D visual tracking is significant to deep space exploration programs, which can guarantee spacecraft to flexibly approach the target. In this paper, we focus on the studied accurat…
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…
Know Your Surroundings: Panoramic Multi-Object Tracking by Multimodality Collaboration
Yuhang He, Wentao Yu, Jie Han +3
In this paper, we focus on the multi-object tracking (MOT) problem of automatic driving and robot navigation. Most existing MOT methods track multiple objects using a singular RGB…
Image-to-image Translation via Hierarchical Style Disentanglement
Xinyang Li, Shengchuan Zhang, Jie Hu +6
Recently, image-to-image translation has made significant progress in achieving both multi-label (\ie, translation conditioned on different labels) and multi-style (\ie, generation…
Learning Task-oriented Disentangled Representations for Unsupervised Domain Adaptation
Pingyang Dai, Peixian Chen, Qiong Wu +4
Unsupervised domain adaptation (UDA) aims to address the domain-shift problem between a labeled source domain and an unlabeled target domain. Many efforts have been made to address…
Micro-expression spotting: A new benchmark
Thuong-Khanh Tran, Quang-Nhat Vo, Xiaopeng Hong +2
Micro-expressions (MEs) are brief and involuntary facial expressions that occur when people are trying to hide their true feelings or conceal their emotions. Based on psychology re…
Few-Shot Class-Incremental Learning
Xiaoyu Tao, Xiaopeng Hong, Xinyuan Chang +3
The ability to incrementally learn new classes is crucial to the development of real-world artificial intelligence systems. In this paper, we focus on a challenging but practical f…
Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searching
Wei Peng, Xiaopeng Hong, Haoyu Chen +1
Human action recognition from skeleton data, fueled by the Graph Convolutional Network (GCN), has attracted lots of attention, due to its powerful capability of modeling non-Euclid…
Beyond Universal Person Re-ID Attack
Wenjie Ding, Xing Wei, Rongrong Ji +3
Deep learning-based person re-identification (Re-ID) has made great progress and achieved high performance recently. In this paper, we make the first attempt to examine the vulnera…
Structured Modeling of Joint Deep Feature and Prediction Refinement for Salient Object Detection
Yingyue Xu, Dan Xu, Xiaopeng Hong +4
Recent saliency models extensively explore to incorporate multi-scale contextual information from Convolutional Neural Networks (CNNs). Besides direct fusion strategies, many appro…
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…