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Article ; Online: SMART: Syntax-Calibrated Multi-Aspect Relation Transformer for Change Captioning.

Tu, Yunbin / Li, Liang / Su, Li / Zha, Zheng-Jun / Huang, Qingming

IEEE transactions on pattern analysis and machine intelligence

2024 Volume PP

Abstract: Change captioning aims to describe the semantic change between two similar images. In this process, as the most typical distractor, viewpoint change leads to the pseudo changes about appearance and position of objects, thereby overwhelming the real ... ...

Abstract	Change captioning aims to describe the semantic change between two similar images. In this process, as the most typical distractor, viewpoint change leads to the pseudo changes about appearance and position of objects, thereby overwhelming the real change. Besides, since the visual signal of change appears in a local region with weak feature, it is difficult for the model to directly translate the learned change features into the sentence. In this paper, we propose a syntax-calibrated multi-aspect relation transformer to learn effective change features under different scenes, and build reliable cross-modal alignment between the change features and linguistic words during caption generation. Specifically, a multi-aspect relation learning network is designed to 1) explore the fine-grained changes under irrelevant distractors (e.g., viewpoint change) by embedding the relations of semantics and relative position into the features of each image; 2) learn two view-invariant image representations by strengthening their global contrastive alignment relation, so as to help capture a stable difference representation; 3) provide the model with the prior knowledge about whether and where the semantic change happened by measuring the relation between the representations of captured difference and the image pair. Through the above manner, the model can learn effective change features for caption generation. Further, we introduce the syntax knowledge of Part-of-Speech (POS) and devise a POS-based visual switch to calibrate the transformer decoder. The POS-based visual switch dynamically utilizes visual information during different word generation based on the POS of words. This enables the decoder to build reliable cross-modal alignment, so as to generate a high-level linguistic sentence about change. Extensive experiments show that the proposed method achieves the state-of-the-art performance on the three public datasets.
Language	English
Publishing date	2024-02-13
Publishing country	United States
Document type	Journal Article
ISSN	1939-3539
ISSN (online)	1939-3539
DOI	10.1109/TPAMI.2024.3365104
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Prototype-Augmented Self-Supervised Generative Network for Generalized Zero-Shot Learning.

Wu, Jiamin / Zhang, Tianzhu / Zha, Zheng-Jun / Luo, Jiebo / Zhang, Yongdong / Wu, Feng

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

2024 Volume 33, Page(s) 1938–1951

Abstract: Generalized Zero-Shot Learning (GZSL) aims at recognizing images from both seen and unseen classes by constructing correspondences between visual images and semantic embedding. However, existing methods suffer from a strong bias problem, where unseen ... ...

Abstract	Generalized Zero-Shot Learning (GZSL) aims at recognizing images from both seen and unseen classes by constructing correspondences between visual images and semantic embedding. However, existing methods suffer from a strong bias problem, where unseen images in the target domain tend to be recognized as seen classes in the source domain. To address this issue, we propose a Prototype-augmented Self-supervised Generative Network by integrating self-supervised learning and prototype learning into a feature generating model for GZSL. The proposed model enjoys several advantages. First, we propose a Self-supervised Learning Module to exploit inter-domain relationships, where we introduce anchors as a bridge between seen and unseen categories. In the shared space, we pull the distribution of the target domain away from the source domain and obtain domain-aware features. To our best knowledge, this is the first work to introduce self-supervised learning into GZSL as learning guidance. Second, a Prototype Enhancing Module is proposed to utilize class prototypes to model reliable target domain distribution in finer granularity. In this module, a Prototype Alignment mechanism and a Prototype Dispersion mechanism are combined to guide the generation of better target class features with intra-class compactness and inter-class separability. Extensive experimental results on five standard benchmarks demonstrate that our model performs favorably against state-of-the-art GZSL methods.
Language	English
Publishing date	2024-03-14
Publishing country	United States
Document type	Journal Article
ISSN	1941-0042
ISSN (online)	1941-0042
DOI	10.1109/TIP.2024.3351439
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Graph Representation Learning for Large-Scale Neuronal Morphological Analysis.

Zhao, Jie / Chen, Xuejin / Xiong, Zhiwei / Zha, Zheng-Jun / Wu, Feng

IEEE transactions on neural networks and learning systems

2024 Volume 35, Issue 4, Page(s) 5461–5472

Abstract: The analysis of neuronal morphological data is essential to investigate the neuronal properties and brain mechanisms. The complex morphologies, absence of annotations, and sheer volume of these data pose significant challenges in neuronal morphological ... ...

Abstract	The analysis of neuronal morphological data is essential to investigate the neuronal properties and brain mechanisms. The complex morphologies, absence of annotations, and sheer volume of these data pose significant challenges in neuronal morphological analysis, such as identifying neuron types and large-scale neuron retrieval, all of which require accurate measuring and efficient matching algorithms. Recently, many studies have been conducted to describe neuronal morphologies quantitatively using predefined measurements. However, hand-crafted features are usually inadequate for distinguishing fine-grained differences among massive neurons. In this article, we propose a novel morphology-aware contrastive graph neural network (MACGNN) for unsupervised neuronal morphological representation learning. To improve the retrieval efficiency in large-scale neuronal morphological datasets, we further propose Hash-MACGNN by introducing an improved deep hash algorithm to train the network end-to-end to learn binary hash representations of neurons. We conduct extensive experiments on the largest dataset, NeuroMorpho, which contains more than 100 000 neurons. The experimental results demonstrate the effectiveness and superiority of our MACGNN and Hash-MACGNN for large-scale neuronal morphological analysis.
MeSH term(s)	Neural Networks, Computer ; Learning ; Brain ; Algorithms ; Neurons
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.2022.3204686
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: UHPLC-ESI-QTOF-MS

Shao, Hui / Xiao, Minmin / Zha, Zheng / Olatunji, Opeyemi Joshua

Food science & nutrition

2022 Volume 10, Issue 4, Page(s) 1058–1069

Abstract: Diabetes mellitus (DM) is a chronic disorder associated with severe metabolic derangement and comorbidities. The constant increase in the global population of diabetic patients coupled with some prevailing side effects associated with synthetic ... ...

Abstract	Diabetes mellitus (DM) is a chronic disorder associated with severe metabolic derangement and comorbidities. The constant increase in the global population of diabetic patients coupled with some prevailing side effects associated with synthetic antidiabetic drugs has necessitated the urgent need for the search for alternative antidiabetic regimens. This study investigated the antidiabetic, antioxidant, and pancreatic protective effects of the
Language	English
Publishing date	2022-01-19
Publishing country	United States
Document type	Journal Article
ZDB-ID	2703010-6
ISSN	2048-7177
ISSN	2048-7177
DOI	10.1002/fsn3.2732
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Potential Utility of Natural Products against Oxidative Stress in Animal Models of Multiple Sclerosis.

Zha, Zheng / Liu, Sisi / Liu, Yijiang / Li, Chen / Wang, Lei

Antioxidants (Basel, Switzerland)

2022 Volume 11, Issue 8

Abstract: Multiple sclerosis (MS) is an autoimmune-mediated degenerative disease of the central nervous system (CNS) characterized by immune cell infiltration, demyelination and axonal injury. Oxidative stress-induced inflammatory response, especially the ... ...

Abstract	Multiple sclerosis (MS) is an autoimmune-mediated degenerative disease of the central nervous system (CNS) characterized by immune cell infiltration, demyelination and axonal injury. Oxidative stress-induced inflammatory response, especially the destructive effect of immune cell-derived free radicals on neurons and oligodendrocytes, is crucial in the onset and progression of MS. Therefore, targeting oxidative stress-related processes may be a promising preventive and therapeutic strategy for MS. Animal models, especially rodent models, can be used to explore the in vivo molecular mechanisms of MS considering their similarity to the pathological processes and clinical signs of MS in humans and the significant oxidative damage observed within their CNS. Consequently, these models have been used widely in pre-clinical studies of oxidative stress in MS. To date, many natural products have been shown to exert antioxidant effects to attenuate the CNS damage in animal models of MS. This review summarized several common rodent models of MS and their association with oxidative stress. In addition, this review provides a comprehensive and concise overview of previously reported natural antioxidant products in inhibiting the progression of MS.
Language	English
Publishing date	2022-07-29
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2704216-9
ISSN	2076-3921
ISSN	2076-3921
DOI	10.3390/antiox11081495
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A Closer Look at the Reflection Formulation in Single Image Reflection Removal.

Chen, Zhikai / Long, Fuchen / Qiu, Zhaofan / Zhang, Juyong / Zha, Zheng-Jun / Yao, Ting / Luo, Jiebo

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

2024 Volume 33, Page(s) 625–638

Abstract: How to model the effect of reflection is crucial for single image reflection removal (SIRR) task. Modern SIRR methods usually simplify the reflection formulation with the assumption of linear combination of a transmission layer and a reflection layer. ... ...

Abstract	How to model the effect of reflection is crucial for single image reflection removal (SIRR) task. Modern SIRR methods usually simplify the reflection formulation with the assumption of linear combination of a transmission layer and a reflection layer. However, the large variations in image content and the real-world picture-taking conditions often result in far more complex reflection. In this paper, we introduce a new screen-blur combination based on two important factors, namely the intensity and the blurriness of reflection, to better characterize the reflection formulation in SIRR. Specifically, we present Screen-blur Reflection Networks (SRNet), which executes the screen-blur formulation in its network design and adapts to the complex reflection on real scenes. Technically, SRNet consists of three components: a blended image generator, a reflection estimator and a reflection removal module. The image generator exploits the screen-blur combination to synthesize the training blended images. The reflection estimator learns the reflection layer and a blur degree that measures the level of blurriness for reflection. The reflection removal module further uses the blended image, blur degree and reflection layer to filter out the transmission layer in a cascaded manner. Superior results on three different SIRR methods are reported when generating the training data on the principle of the screen-blur combination. Moreover, extensive experiments on six datasets quantitatively and qualitatively demonstrate the efficacy of SRNet over the state-of-the-art methods.
Language	English
Publishing date	2024-01-10
Publishing country	United States
Document type	Journal Article
ISSN	1941-0042
ISSN (online)	1941-0042
DOI	10.1109/TIP.2023.3347915
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Image De-Raining Transformer.

Xiao, Jie / Fu, Xueyang / Liu, Aiping / Wu, Feng / Zha, Zheng-Jun

IEEE transactions on pattern analysis and machine intelligence

2023 Volume 45, Issue 11, Page(s) 12978–12995

Abstract: Existing deep learning based de-raining approaches have resorted to the convolutional architectures. However, the intrinsic limitations of convolution, including local receptive fields and independence of input content, hinder the model's ability to ... ...

Abstract	Existing deep learning based de-raining approaches have resorted to the convolutional architectures. However, the intrinsic limitations of convolution, including local receptive fields and independence of input content, hinder the model's ability to capture long-range and complicated rainy artifacts. To overcome these limitations, we propose an effective and efficient transformer-based architecture for the image de-raining. First, we introduce general priors of vision tasks, i.e., locality and hierarchy, into the network architecture so that our model can achieve excellent de-raining performance without costly pre-training. Second, since the geometric appearance of rainy artifacts is complicated and of significant variance in space, it is essential for de-raining models to extract both local and non-local features. Therefore, we design the complementary window-based transformer and spatial transformer to enhance locality while capturing long-range dependencies. Besides, to compensate for the positional blindness of self-attention, we establish a separate representative space for modeling positional relationship, and design a new relative position enhanced multi-head self-attention. In this way, our model enjoys powerful abilities to capture dependencies from both content and position, so as to achieve better image content recovery while removing rainy artifacts. Experiments substantiate that our approach attains more appealing results than state-of-the-art methods quantitatively and qualitatively.
Language	English
Publishing date	2023-10-03
Publishing country	United States
Document type	Journal Article
ISSN	1939-3539
ISSN (online)	1939-3539
DOI	10.1109/TPAMI.2022.3183612
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Event-Driven Video Restoration With Spiking-Convolutional Architecture.

Cao, Chengzhi / Fu, Xueyang / Zhu, Yurui / Sun, Zhijing / Zha, Zheng-Jun

IEEE transactions on neural networks and learning systems

2023 Volume PP

Abstract: With high temporal resolution, high dynamic range, and low latency, event cameras have made great progress in numerous low-level vision tasks. To help restore low-quality (LQ) video sequences, most existing event-based methods usually employ ... ...

Abstract	With high temporal resolution, high dynamic range, and low latency, event cameras have made great progress in numerous low-level vision tasks. To help restore low-quality (LQ) video sequences, most existing event-based methods usually employ convolutional neural networks (CNNs) to extract sparse event features without considering the spatial sparse distribution or the temporal relation in neighboring events. It brings about insufficient use of spatial and temporal information from events. To address this problem, we propose a new spiking-convolutional network (SC-Net) architecture to facilitate event-driven video restoration. Specifically, to properly extract the rich temporal information contained in the event data, we utilize a spiking neural network (SNN) to suit the sparse characteristics of events and capture temporal correlation in neighboring regions; to make full use of spatial consistency between events and frames, we adopt CNNs to transform sparse events as an extra brightness prior to being aware of detailed textures in video sequences. In this way, both the temporal correlation in neighboring events and the mutual spatial information between the two types of features are fully explored and exploited to accurately restore detailed textures and sharp edges. The effectiveness of the proposed network is validated in three representative video restoration tasks: deblurring, super-resolution, and deraining. Extensive experiments on synthetic and real-world benchmarks have illuminated that our method performs better than existing competing methods.
Language	English
Publishing date	2023-11-09
Publishing country	United States
Document type	Journal Article
ISSN	2162-2388
ISSN (online)	2162-2388
DOI	10.1109/TNNLS.2023.3329741
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Semantic and Relation Modulation for Audio-Visual Event Localization.

Wang, Hao / Zha, Zheng-Jun / Li, Liang / Chen, Xuejin / Luo, Jiebo

IEEE transactions on pattern analysis and machine intelligence

2023 Volume 45, Issue 6, Page(s) 7711–7725

Abstract: We study the problem of localizing audio-visual events that are both audible and visible in a video. Existing works focus on encoding and aligning audio and visual features at the segment level while neglecting informative correlation between segments of ...

Abstract	We study the problem of localizing audio-visual events that are both audible and visible in a video. Existing works focus on encoding and aligning audio and visual features at the segment level while neglecting informative correlation between segments of the two modalities and between multi-scale event proposals. We propose a novel Semantic and Relation Modulation Network (SRMN) to learn the above correlation and leverage it to modulate the related auditory, visual, and fused features. In particular, for semantic modulation, we propose intra-modal normalization and cross-modal normalization. The former modulates features of a single modality with the event-relevant semantic guidance of the same modality. The latter modulates features of two modalities by establishing and exploiting the cross-modal relationship. For relation modulation, we propose a multi-scale proposal modulating module and a multi-alignment segment modulating module to introduce multi-scale event proposals and enable dense matching between cross-modal segments, which strengthen correlations between successive segments within one proposal and between all segments. With the features modulated by the correlation information regarding audio-visual events, SRMN performs accurate event localization. Extensive experiments conducted on the public AVE dataset demonstrate that our method outperforms the state-of-the-art methods in both supervised event localization and cross-modality localization tasks.
Language	English
Publishing date	2023-05-05
Publishing country	United States
Document type	Journal Article
ISSN	1939-3539
ISSN (online)	1939-3539
DOI	10.1109/TPAMI.2022.3226328
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Continual Image Deraining With Hypergraph Convolutional Networks.

Fu, Xueyang / Xiao, Jie / Zhu, Yurui / Liu, Aiping / Wu, Feng / Zha, Zheng-Jun

IEEE transactions on pattern analysis and machine intelligence

2023 Volume 45, Issue 8, Page(s) 9534–9551

Abstract: Image deraining is a challenging task since rain streaks have the characteristics of a spatially long structure and have a complex diversity. Existing deep learning-based methods mainly construct the deraining networks by stacking vanilla convolutional ... ...

Abstract	Image deraining is a challenging task since rain streaks have the characteristics of a spatially long structure and have a complex diversity. Existing deep learning-based methods mainly construct the deraining networks by stacking vanilla convolutional layers with local relations, and can only handle a single dataset due to catastrophic forgetting, resulting in a limited performance and insufficient adaptability. To address these issues, we propose a new image deraining framework to effectively explore nonlocal similarity, and to continuously learn on multiple datasets. Specifically, we first design a patchwise hypergraph convolutional module, which aims to better extract the nonlocal properties with higher-order constraints on the data, to construct a new backbone and to improve the deraining performance. Then, to achieve better generalizability and adaptability in real-world scenarios, we propose a biological brain-inspired continual learning algorithm. By imitating the plasticity mechanism of brain synapses during the learning and memory process, our continual learning process allows the network to achieve a subtle stability-plasticity tradeoff. This it can effectively alleviate catastrophic forgetting and enables a single network to handle multiple datasets. Compared with the competitors, our new deraining network with unified parameters attains a state-of-the-art performance on seen synthetic datasets and has a significantly improved generalizability on unseen real rainy images.
MeSH term(s)	Algorithms ; Brain ; Memory
Language	English
Publishing date	2023-06-30
Publishing country	United States
Document type	Journal Article
ISSN	1939-3539
ISSN (online)	1939-3539
DOI	10.1109/TPAMI.2023.3241756
Database	MEDical Literature Analysis and Retrieval System OnLINE

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