Pub Date : 2024-06-26DOI: 10.1109/TMM.2024.3405724
Peiguang Jing;Xuan Zhao;Fugui Fan;Fan Yang;Yun Li;Yuting Su
Micro-videos, as an increasingly popular form of user-generated content (UGC), naturally include diverse multimodal cues. However, in pursuit of consistent representations, existing methods neglect the simultaneous consideration of exploring modality discrepancy and preserving modality diversity. In this paper, we propose a multimodal progressive modulation network (MPMNet) for micro-video multi-label classification, which enhances the indicative ability of each modality through gradually regulating various modality biases. In MPMNet, we first leverage a unimodal-centered parallel aggregation strategy to obtain preliminary comprehensive representations. We then integrate feature-domain disentangled modulation process and category-domain adaptive modulation process into a unified framework to jointly refine modality-oriented representations. In the former modulation process, we constrain inter-modal dependencies in a latent space to obtain modality-oriented sample representations, and introduce a disentangled paradigm to further maintain modality diversity. In the latter modulation process, we construct global-context-aware graph convolutional networks to acquire modality-oriented category representations, and develop two instance-level parameter generators to further regulate unimodal semantic biases. Extensive experiments on two micro-video multi-label datasets show that our proposed approach outperforms the state-of-the-art methods.
{"title":"Multimodal Progressive Modulation Network for Micro-Video Multi-Label Classification","authors":"Peiguang Jing;Xuan Zhao;Fugui Fan;Fan Yang;Yun Li;Yuting Su","doi":"10.1109/TMM.2024.3405724","DOIUrl":"10.1109/TMM.2024.3405724","url":null,"abstract":"Micro-videos, as an increasingly popular form of user-generated content (UGC), naturally include diverse multimodal cues. However, in pursuit of consistent representations, existing methods neglect the simultaneous consideration of exploring modality discrepancy and preserving modality diversity. In this paper, we propose a multimodal progressive modulation network (MPMNet) for micro-video multi-label classification, which enhances the indicative ability of each modality through gradually regulating various modality biases. In MPMNet, we first leverage a unimodal-centered parallel aggregation strategy to obtain preliminary comprehensive representations. We then integrate feature-domain disentangled modulation process and category-domain adaptive modulation process into a unified framework to jointly refine modality-oriented representations. In the former modulation process, we constrain inter-modal dependencies in a latent space to obtain modality-oriented sample representations, and introduce a disentangled paradigm to further maintain modality diversity. In the latter modulation process, we construct global-context-aware graph convolutional networks to acquire modality-oriented category representations, and develop two instance-level parameter generators to further regulate unimodal semantic biases. Extensive experiments on two micro-video multi-label datasets show that our proposed approach outperforms the state-of-the-art methods.","PeriodicalId":13273,"journal":{"name":"IEEE Transactions on Multimedia","volume":null,"pages":null},"PeriodicalIF":8.4,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141528982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Owing to the capacity of performing full-time target searches, cross-modality vehicle re-identification based on unmanned aerial vehicles (UAV) is gaining more attention in both video surveillance and public security. However, this promising and innovative research has not been studied sufficiently due to the issue of data inadequacy. Meanwhile, the cross-modality discrepancy and orientation discrepancy challenges further aggravate the difficulty of this task. To this end, we pioneer a cross-modality vehicle Re-ID benchmark named UAV Cross-Modality Vehicle Re-ID (UCM-VeID), containing 753 identities with 16015 RGB and 13913 infrared images. Moreover, to meet cross-modality discrepancy and orientation discrepancy challenges, we present a hybrid weights decoupling network (HWDNet) to learn the shared discriminative orientation-invariant features. For the first challenge, we proposed a hybrid weights siamese network with a well-designed weight restrainer and its corresponding objective function to learn both modality-specific and modality shared information. In terms of the second challenge, three effective decoupling structures with two pretext tasks are investigated to flexibly conduct orientation-invariant feature separation task. Comprehensive experiments are carried out to validate the effectiveness of the proposed method.
{"title":"Relation-Aware Weight Sharing in Decoupling Feature Learning Network for UAV RGB-Infrared Vehicle Re-Identification","authors":"Xingyue Liu;Jiahao Qi;Chen Chen;Kangcheng Bin;Ping Zhong","doi":"10.1109/TMM.2024.3400675","DOIUrl":"10.1109/TMM.2024.3400675","url":null,"abstract":"Owing to the capacity of performing full-time target searches, cross-modality vehicle re-identification based on unmanned aerial vehicles (UAV) is gaining more attention in both video surveillance and public security. However, this promising and innovative research has not been studied sufficiently due to the issue of data inadequacy. Meanwhile, the cross-modality discrepancy and orientation discrepancy challenges further aggravate the difficulty of this task. To this end, we pioneer a cross-modality vehicle Re-ID benchmark named UAV Cross-Modality Vehicle Re-ID (UCM-VeID), containing 753 identities with \u0000<bold>16015</b>\u0000 RGB and \u0000<bold>13913</b>\u0000 infrared images. Moreover, to meet cross-modality discrepancy and orientation discrepancy challenges, we present a hybrid weights decoupling network (HWDNet) to learn the shared discriminative orientation-invariant features. For the first challenge, we proposed a hybrid weights siamese network with a well-designed weight restrainer and its corresponding objective function to learn both modality-specific and modality shared information. In terms of the second challenge, three effective decoupling structures with two pretext tasks are investigated to flexibly conduct orientation-invariant feature separation task. Comprehensive experiments are carried out to validate the effectiveness of the proposed method.","PeriodicalId":13273,"journal":{"name":"IEEE Transactions on Multimedia","volume":null,"pages":null},"PeriodicalIF":8.4,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141517081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-21DOI: 10.1109/TMM.2024.3417694
Yahui Xu;Yi Bin;Jiwei Wei;Yang Yang;Guoqing Wang;Heng Tao Shen
We study the task of image retrieval with text feedback, where a reference image and modification text are composed to retrieve the desired target image. To accomplish this goal, existing methods always get the multimodal representations through different feature encoders and then adopt different strategies to model the correlation between the composed inputs and the target image. However, the multimodal query brings more challenges as it requires not only the synergistic understanding of the semantics from the heterogeneous multimodal inputs but also the ability to accurately build the underlying semantic correlation existing in each inputs-target triplet, i.e., reference image, modification text, and target image. In this paper, we tackle these issues with a novel Align and Retrieve (AlRet) framework. First, our proposed methods employ the contrastive loss in the feature encoders to learn meaningful multimodal representation while making the subsequent correlation modeling process in a more harmonious space. Then we propose to learn the accurate correlation between the composed inputs and target image in a novel composition-and-decomposition paradigm. Specifically, the composition network couples the reference image and modification text into a joint representation to learn the correlation between the joint representation and target image. The decomposition network conversely decouples the target image into visual and text subspaces to exploit the underlying correlation between the target image with each query element. The composition-and-decomposition paradigm forms a closed loop, which can be optimized simultaneously to promote each other in the performance. Massive comparison experiments on three real-world datasets confirm the effectiveness of the proposed method.
{"title":"Align and Retrieve: Composition and Decomposition Learning in Image Retrieval With Text Feedback","authors":"Yahui Xu;Yi Bin;Jiwei Wei;Yang Yang;Guoqing Wang;Heng Tao Shen","doi":"10.1109/TMM.2024.3417694","DOIUrl":"10.1109/TMM.2024.3417694","url":null,"abstract":"We study the task of image retrieval with text feedback, where a reference image and modification text are composed to retrieve the desired target image. To accomplish this goal, existing methods always get the multimodal representations through different feature encoders and then adopt different strategies to model the correlation between the composed inputs and the target image. However, the multimodal query brings more challenges as it requires not only the synergistic understanding of the semantics from the heterogeneous multimodal inputs but also the ability to accurately build the underlying semantic correlation existing in each inputs-target triplet, i.e., reference image, modification text, and target image. In this paper, we tackle these issues with a novel Align and Retrieve (AlRet) framework. First, our proposed methods employ the contrastive loss in the feature encoders to learn meaningful multimodal representation while making the subsequent correlation modeling process in a more harmonious space. Then we propose to learn the accurate correlation between the composed inputs and target image in a novel composition-and-decomposition paradigm. Specifically, the composition network couples the reference image and modification text into a joint representation to learn the correlation between the joint representation and target image. The decomposition network conversely decouples the target image into visual and text subspaces to exploit the underlying correlation between the target image with each query element. The composition-and-decomposition paradigm forms a closed loop, which can be optimized simultaneously to promote each other in the performance. Massive comparison experiments on three real-world datasets confirm the effectiveness of the proposed method.","PeriodicalId":13273,"journal":{"name":"IEEE Transactions on Multimedia","volume":null,"pages":null},"PeriodicalIF":8.4,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141504211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to the limited number of stable image feature descriptors and the simplistic concatenation approach to hash generation, existing hashing methods have not achieved a satisfactory balance between robustness and discrimination. To this end, a novel perceptual hashing method is proposed in this paper using feature fusion of fractional-order continuous orthogonal moments (FrCOMs). Specifically, two robust image descriptors, i.e., fractional-order Chebyshev Fourier moments (FrCHFMs) and fractional-order radial harmonic Fourier moments (FrRHFMs), are used to extract global structural features of a color image. Then, the canonical correlation analysis (CCA) strategy is employed to fuse these features during the final hash generation process. Compared to direct concatenation, CCA excels in eliminating redundancies between feature vectors, resulting in a shorter hash sequence and higher authentication performance. A series of experiments demonstrate that the proposed method achieves satisfactory robustness, discrimination and security. Particularly, the proposed method exhibits better tampering detection ability and robustness against combined content-preserving manipulations in practical applications.
{"title":"Perceptual Image Hashing Using Feature Fusion of Orthogonal Moments","authors":"Xinran Li;Zichi Wang;Guorui Feng;Xinpeng Zhang;Chuan Qin","doi":"10.1109/TMM.2024.3405660","DOIUrl":"10.1109/TMM.2024.3405660","url":null,"abstract":"Due to the limited number of stable image feature descriptors and the simplistic concatenation approach to hash generation, existing hashing methods have not achieved a satisfactory balance between robustness and discrimination. To this end, a novel perceptual hashing method is proposed in this paper using feature fusion of fractional-order continuous orthogonal moments (FrCOMs). Specifically, two robust image descriptors, i.e., fractional-order Chebyshev Fourier moments (FrCHFMs) and fractional-order radial harmonic Fourier moments (FrRHFMs), are used to extract global structural features of a color image. Then, the canonical correlation analysis (CCA) strategy is employed to fuse these features during the final hash generation process. Compared to direct concatenation, CCA excels in eliminating redundancies between feature vectors, resulting in a shorter hash sequence and higher authentication performance. A series of experiments demonstrate that the proposed method achieves satisfactory robustness, discrimination and security. Particularly, the proposed method exhibits better tampering detection ability and robustness against combined content-preserving manipulations in practical applications.","PeriodicalId":13273,"journal":{"name":"IEEE Transactions on Multimedia","volume":null,"pages":null},"PeriodicalIF":8.4,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141517083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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