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Article ; Online: Active Compliance Smart Control Strategy of Hybrid Mechanism for Bonnet Polishing.

Li, Ze / Cheung, Chi Fai / Lam, Kin Man / Lun, Daniel Pak Kong

2024 Volume 24, Issue 2

Abstract: Compliance control strategies have been utilised for the ultraprecision polishing process for many years. Most researchers execute active compliance control strategies by employing impedance control law on a robot development platform. However, these ... ...

Abstract	Compliance control strategies have been utilised for the ultraprecision polishing process for many years. Most researchers execute active compliance control strategies by employing impedance control law on a robot development platform. However, these methods are limited by the load capacity, positioning accuracy, and repeatability of polishing mechanisms. Moreover, a sophisticated actuator mounted at the end of the end-effector of robots is difficult to maintain in the polishing scenario. In contrast, a hybrid mechanism for polishing that possesses the advantages of serial and parallel mechanisms can mitigate the above problems, especially when an active compliance control strategy is employed. In this research, a high-frequency-impedance robust force control strategy is proposed. It outputs a position adjustment value directly according to a contact pressure adjustment value. An open architecture control system with customised software is developed to respond to external interrupts during the polishing procedure, implementing the active compliance control strategy on a hybrid mechanism. Through this method, the hybrid mechanism can adapt to the external environment with a given contact pressure automatically instead of relying on estimating the environment stiffness. Experimental results show that the proposed strategy adapts the unknown freeform surface without overshooting and improves the surface quality. The average surface roughness value decreases from 0.057 um to 0.027 um.
Language	English
Publishing date	2024-01-10
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s24020421
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Distilling Privileged Knowledge for Anomalous Event Detection From Weakly Labeled Videos.

Liu, Tianshan / Lam, Kin-Man / Kong, Jun

IEEE transactions on neural networks and learning systems

2023 Volume PP

Abstract: Weakly supervised video anomaly detection (WS-VAD) aims to identify the snippets involving anomalous events in long untrimmed videos, with solely video-level binary labels. A typical paradigm among the existing WS-VAD methods is to employ multiple ... ...

Abstract	Weakly supervised video anomaly detection (WS-VAD) aims to identify the snippets involving anomalous events in long untrimmed videos, with solely video-level binary labels. A typical paradigm among the existing WS-VAD methods is to employ multiple modalities as inputs, e.g., RGB, optical flow, and audio, as they can provide sufficient discriminative clues that are robust to the diverse, complicated real-world scenes. However, such a pipeline has high reliance on the availability of multiple modalities and is computationally expensive and storage demanding in processing long sequences, which limits its use in some applications. To address this dilemma, we propose a privileged knowledge distillation (KD) framework dedicated to the WS-VAD task, which can maintain the benefits of exploiting additional modalities, while avoiding the need for using multimodal data in the inference phase. We argue that the performance of the privileged KD framework mainly depends on two factors: 1) the effectiveness of the multimodal teacher network and 2) the completeness of the useful information transfer. To obtain a reliable teacher network, we propose a cross-modal interactive learning strategy and an anomaly normal discrimination loss, which target learning task-specific cross-modal features and encourage the separability of anomalous and normal representations, respectively. Furthermore, we design both representation-and logits-level distillation loss functions, which force the unimodal student network to distill abundant privileged knowledge from the well-trained multimodal teacher network, in a snippet-to-video fashion. Extensive experimental results on three public benchmarks demonstrate that the proposed privileged KD framework can train a lightweight yet effective detector, for localizing anomaly events under the supervision of video-level annotations.
Language	English
Publishing date	2023-04-10
Publishing country	United States
Document type	Journal Article
ISSN	2162-2388
ISSN (online)	2162-2388
DOI	10.1109/TNNLS.2023.3263966
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Deep Learning Methods for Calibrated Photometric Stereo and Beyond.

Ju, Yakun / Lam, Kin-Man / Xie, Wuyuan / Zhou, Huiyu / Dong, Junyu / Shi, Boxin

IEEE transactions on pattern analysis and machine intelligence

2024 Volume PP

Abstract: Photometric stereo recovers the surface normals of an object from multiple images with varying shading cues, i.e., modeling the relationship between surface orientation and intensity at each pixel. Photometric stereo prevails in superior per-pixel ... ...

Abstract	Photometric stereo recovers the surface normals of an object from multiple images with varying shading cues, i.e., modeling the relationship between surface orientation and intensity at each pixel. Photometric stereo prevails in superior per-pixel resolution and fine reconstruction details. However, it is a complicated problem because of the non-linear relationship caused by non-Lambertian surface reflectance. Recently, various deep learning methods have shown a powerful ability in the context of photometric stereo against non-Lambertian surfaces. This paper provides a comprehensive review of existing deep learning-based calibrated photometric stereo methods utilizing orthographic cameras and directional light sources. We first analyze these methods from different perspectives, including input processing, supervision, and network architecture. We summarize the performance of deep learning photometric stereo models on the most widely-used benchmark data set. This demonstrates the advanced performance of deep learning-based photometric stereo methods. Finally, we give suggestions and propose future research trends based on the limitations of existing models.
Language	English
Publishing date	2024-04-12
Publishing country	United States
Document type	Journal Article
ISSN	1939-3539
ISSN (online)	1939-3539
DOI	10.1109/TPAMI.2024.3388150
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Holistic-Guided Disentangled Learning With Cross-Video Semantics Mining for Concurrent First-Person and Third-Person Activity Recognition.

Liu, Tianshan / Zhao, Rui / Jia, Wenqi / Lam, Kin-Man / Kong, Jun

IEEE transactions on neural networks and learning systems

2024 Volume 35, Issue 4, Page(s) 5211–5225

Abstract: The popularity of wearable devices has increased the demands for the research on first-person activity recognition. However, most of the current first-person activity datasets are built based on the assumption that only the human-object interaction (HOI) ...

Abstract	The popularity of wearable devices has increased the demands for the research on first-person activity recognition. However, most of the current first-person activity datasets are built based on the assumption that only the human-object interaction (HOI) activities, performed by the camera-wearer, are captured in the field of view. Since humans live in complicated scenarios, in addition to the first-person activities, it is likely that third-person activities performed by other people also appear. Analyzing and recognizing these two types of activities simultaneously occurring in a scene is important for the camera-wearer to understand the surrounding environments. To facilitate the research on concurrent first- and third-person activity recognition (CFT-AR), we first created a new activity dataset, namely PolyU concurrent first- and third-person (CFT) Daily, which exhibits distinct properties and challenges, compared with previous activity datasets. Since temporal asynchronism and appearance gap usually exist between the first- and third-person activities, it is crucial to learn robust representations from all the activity-related spatio-temporal positions. Thus, we explore both holistic scene-level and local instance-level (person-level) features to provide comprehensive and discriminative patterns for recognizing both first- and third-person activities. On the one hand, the holistic scene-level features are extracted by a 3-D convolutional neural network, which is trained to mine shared and sample-unique semantics between video pairs, via two well-designed attention-based modules and a self-knowledge distillation (SKD) strategy. On the other hand, we further leverage the extracted holistic features to guide the learning of instance-level features in a disentangled fashion, which aims to discover both spatially conspicuous patterns and temporally varied, yet critical, cues. Experimental results on the PolyU CFT Daily dataset validate that our method achieves the state-of-the-art performance.
MeSH term(s)	Humans ; Neural Networks, Computer ; Semantics ; Human Activities ; Cues
Language	English
Publishing date	2024-04-04
Publishing country	United States
Document type	Journal Article
ISSN	2162-2388
ISSN (online)	2162-2388
DOI	10.1109/TNNLS.2022.3202835
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Unifying Multimodal Source and Propagation Graph for Rumour Detection on Social Media with Missing Features

Cheung, Tsun-Hin / Lam, Kin-Man

2022

Abstract: With the rapid development of online social media platforms, the spread of rumours has become a critical societal concern. Current methods for rumour detection can be categorized into image-text pair classification and source-reply graph classification. ... ...

Abstract	With the rapid development of online social media platforms, the spread of rumours has become a critical societal concern. Current methods for rumour detection can be categorized into image-text pair classification and source-reply graph classification. In this paper, we propose a novel approach that combines multimodal source and propagation graph features for rumour classification. We introduce the Unified Multimodal Graph Transformer Network (UMGTN) which integrates Transformer encoders to fuse these features. Given that not every message in social media is associated with an image and community responses in propagation graphs do not immediately follow source messages, our aim is to build a network architecture that handles missing features such as images or replies. To enhance the model's robustness to data with missing features, we adopt a multitask learning framework that simultaneously learns representations between samples with complete and missing features. We evaluate our proposed method on four real-world datasets, augmenting them by recovering images and replies from Twitter and Weibo. Experimental results demonstrate that our UMGTN with multitask learning achieves state-of-the-art performance, improving F1-score by 1.0% to 4.0%, while maintaining detection robustness to missing features within 2% accuracy and F1-score compared to models trained without the multitask learning framework. We have made our models and datasets publicly available at: https://thcheung.github.io/umgtn/.
Keywords	Computer Science - Multimedia ; Computer Science - Social and Information Networks
Subject code	006
Publishing date	2022-06-09
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: MGF6mARice: prediction of DNA N6-methyladenine sites in rice by exploiting molecular graph feature and residual block.

Liu, Mengya / Sun, Zhan-Li / Zeng, Zhigang / Lam, Kin-Man

Briefings in bioinformatics

2022 Volume 23, Issue 3

Abstract: DNA N6-methyladenine (6mA) is produced by the N6 position of the adenine being methylated, which occurs at the molecular level, and is involved in numerous vital biological processes in the rice genome. Given the shortcomings of biological experiments, ... ...

Abstract	DNA N6-methyladenine (6mA) is produced by the N6 position of the adenine being methylated, which occurs at the molecular level, and is involved in numerous vital biological processes in the rice genome. Given the shortcomings of biological experiments, researchers have developed many computational methods to predict 6mA sites and achieved good performance. However, the existing methods do not consider the occurrence mechanism of 6mA to extract features from the molecular structure. In this paper, a novel deep learning method is proposed by devising DNA molecular graph feature and residual block structure for 6mA sites prediction in rice, named MGF6mARice. Firstly, the DNA sequence is changed into a simplified molecular input line entry system (SMILES) format, which reflects chemical molecular structure. Secondly, for the molecular structure data, we construct the DNA molecular graph feature based on the principle of graph convolutional network. Then, the residual block is designed to extract higher level, distinguishable features from molecular graph features. Finally, the prediction module is used to obtain the result of whether it is a 6mA site. By means of 10-fold cross-validation, MGF6mARice outperforms the state-of-the-art approaches. Multiple experiments have shown that the molecular graph feature and residual block can promote the performance of MGF6mARice in 6mA prediction. To the best of our knowledge, it is the first time to derive a feature of DNA sequence by considering the chemical molecular structure. We hope that MGF6mARice will be helpful for researchers to analyze 6mA sites in rice.
MeSH term(s)	Adenine ; DNA/genetics ; DNA Methylation ; Delayed Emergence from Anesthesia/genetics ; Oryza/genetics
Chemical Substances	DNA (9007-49-2) ; Adenine (JAC85A2161)
Language	English
Publishing date	2022-06-01
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbac082
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Landmark Localization from Medical Images with Generative Distribution Prior.

Huang, Zixun / Zhao, Rui / Leung, Frank H F / Banerjee, Sunetra / Lam, Kin-Man / Zheng, Yong-Ping / Ling, Sai Ho

IEEE transactions on medical imaging

2024 Volume PP

Abstract: In medical image analysis, anatomical landmarks usually contain strong prior knowledge of their structural information. In this paper, we propose to promote medical landmark localization by modeling the underlying landmark distribution via normalizing ... ...

Abstract	In medical image analysis, anatomical landmarks usually contain strong prior knowledge of their structural information. In this paper, we propose to promote medical landmark localization by modeling the underlying landmark distribution via normalizing flows. Specifically, we introduce the flow-based landmark distribution prior as a learnable objective function into a regression-based landmark localization framework. Moreover, we employ an integral operation to make the mapping from heatmaps to coordinates differentiable to further enhance heatmap-based localization with the learned distribution prior. Our proposed Normalizing Flow-based Distribution Prior (NFDP) employs a straightforward backbone and non-problem-tailored architecture (i.e., ResNet18), which delivers high-fidelity outputs across three X-ray-based landmark localization datasets. Remarkably, the proposed NFDP can do the job with minimal additional computational burden as the normalizing flows module is detached from the framework on inferencing. As compared to existing techniques, our proposed NFDP provides a superior balance between prediction accuracy and inference speed, making it a highly efficient and effective approach. The source code of this paper is available at https://github.com/jacksonhzx95/NFDP.
Language	English
Publishing date	2024-02-29
Publishing country	United States
Document type	Journal Article
ZDB-ID	622531-7
ISSN	1558-254X ; 0278-0062
ISSN (online)	1558-254X
ISSN	0278-0062
DOI	10.1109/TMI.2024.3371948
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: GR-PSN: Learning to Estimate Surface Normal and Reconstruct Photometric Stereo Images.

Ju, Yakun / Shi, Boxin / Chen, Yang / Zhou, Huiyu / Dong, Junyu / Lam, Kin-Man

IEEE transactions on visualization and computer graphics

2023 Volume PP

Abstract: In this paper, we propose a novel method, namely GR-PSN, which learns surface normals from photometric stereo images and generates the photometric images under distant illumination from different lighting directions and surface materials. The framework ... ...

Abstract	In this paper, we propose a novel method, namely GR-PSN, which learns surface normals from photometric stereo images and generates the photometric images under distant illumination from different lighting directions and surface materials. The framework is composed of two subnetworks, named GeometryNet and ReconstructNet, which are cascaded to perform shape reconstruction and image rendering in an end-to-end manner. ReconstructNet introduces additional supervision for surface-normal recovery, forming a closed-loop structure with GeometryNet. We also encode lighting and surface reflectance in ReconstructNet, to achieve arbitrary rendering. In training, we set up a parallel framework to simultaneously learn two arbitrary materials for an object, providing an additional transform loss. Therefore, our method is trained based on the supervision by three different loss functions, namely the surface-normal loss, reconstruction loss, and transform loss. We alternately input the predicted surface-normal map and the ground-truth into ReconstructNet, to achieve stable training for ReconstructNet. Experiments show that our method can accurately recover the surface normals of an object with an arbitrary number of inputs, and can re-render images of the object with arbitrary surface materials. Extensive experimental results show that our proposed method outperforms those methods based on a single surface recovery network and shows realistic rendering results on 100 different materials. Our code can be found in https://github.com/Kelvin-Ju/GR-PSN.
Language	English
Publishing date	2023-11-03
Publishing country	United States
Document type	Journal Article
ISSN	1941-0506
ISSN (online)	1941-0506
DOI	10.1109/TVCG.2023.3329817
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: An Efficient Algorithm for Ocean-Front Evolution Trend Recognition

Yang, Yuting / Lam, Kin-Man / Sun, Xin / Dong, Junyu / Lguensat, Redouane

Remote Sensing. 2022 Jan. 06, v. 14, no. 2

2022

Abstract: Marine hydrological elements are of vital importance in marine surveys. The evolution of these elements can have a profound effect on the relationship between human activities and marine hydrology. Therefore, the detection and explanation of the ... ...

Abstract	Marine hydrological elements are of vital importance in marine surveys. The evolution of these elements can have a profound effect on the relationship between human activities and marine hydrology. Therefore, the detection and explanation of the evolution laws of marine hydrological elements are urgently needed. In this paper, a novel method, named Evolution Trend Recognition (ETR), is proposed to recognize the trend of ocean fronts, being the most important information in the ocean dynamic process. Therefore, in this paper, we focus on the task of ocean-front trend classification. A novel classification algorithm is first proposed for recognizing the ocean-front trend, in terms of the ocean-front scale and strength. Then, the GoogLeNet Inception network is trained to classify the ocean-front trend, i.e., enhancing or attenuating. The ocean-front trend is classified using the deep neural network, as well as a physics-informed classification algorithm. The two classification results are combined to make the final decision on the trend classification. Furthermore, two novel databases were created for this research, and their generation method is described, to foster research in this direction. These two databases are called the Ocean-Front Tracking Dataset (OFTraD) and the Ocean-Front Trend Dataset (OFTreD). Moreover, experiment results show that our proposed method on OFTreD achieves a higher classification accuracy, which is 97.5%, than state-of-the-art networks. This demonstrates that the proposed ETR algorithm is highly promising for trend classification.
Keywords	algorithms ; data collection ; evolution ; humans ; hydrology
Language	English
Dates of publication	2022-0106
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2513863-7
ISSN	2072-4292
ISSN	2072-4292
DOI	10.3390/rs14020259
Database	NAL-Catalogue (AGRICOLA)

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Book ; Online: Restoration of User Videos Shared on Social Media

Luo, Hongming / Zhou, Fei / Lam, Kin-man / Qiu, Guoping

2022

Abstract: User videos shared on social media platforms usually suffer from degradations caused by unknown proprietary processing procedures, which means that their visual quality is poorer than that of the originals. This paper presents a new general video ... ...

Abstract	User videos shared on social media platforms usually suffer from degradations caused by unknown proprietary processing procedures, which means that their visual quality is poorer than that of the originals. This paper presents a new general video restoration framework for the restoration of user videos shared on social media platforms. In contrast to most deep learning-based video restoration methods that perform end-to-end mapping, where feature extraction is mostly treated as a black box, in the sense that what role a feature plays is often unknown, our new method, termed Video restOration through adapTive dEgradation Sensing (VOTES), introduces the concept of a degradation feature map (DFM) to explicitly guide the video restoration process. Specifically, for each video frame, we first adaptively estimate its DFM to extract features representing the difficulty of restoring its different regions. We then feed the DFM to a convolutional neural network (CNN) to compute hierarchical degradation features to modulate an end-to-end video restoration backbone network, such that more attention is paid explicitly to potentially more difficult to restore areas, which in turn leads to enhanced restoration performance. We will explain the design rationale of the VOTES framework and present extensive experimental results to show that the new VOTES method outperforms various state-of-the-art techniques both quantitatively and qualitatively. In addition, we contribute a large scale real-world database of user videos shared on different social media platforms. Codes and datasets are available at https://github.com/luohongming/VOTES.git Comment: This paper has been accepted by ACMMM2022
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Multimedia
Subject code	004 ; 006
Publishing date	2022-08-17
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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