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Article ; Online: Latent Semantic Consensus for Deterministic Geometric Model Fitting.

Xiao, Guobao / Yu, Jun / Ma, Jiayi / Fan, Deng-Ping / Shao, Ling

IEEE transactions on pattern analysis and machine intelligence

2024 Volume PP

Abstract: Estimating reliable geometric model parameters from the data with severe outliers is a fundamental and important task in computer vision. This paper attempts to sample high-quality subsets and select model instances to estimate parameters in the multi- ... ...

Abstract	Estimating reliable geometric model parameters from the data with severe outliers is a fundamental and important task in computer vision. This paper attempts to sample high-quality subsets and select model instances to estimate parameters in the multi-structural data. To address this, we propose an effective method called Latent Semantic Consensus (LSC). The principle of LSC is to preserve the latent semantic consensus in both data points and model hypotheses. Specifically, LSC formulates the model fitting problem into two latent semantic spaces based on data points and model hypotheses, respectively. Then, LSC explores the distributions of points in the two latent semantic spaces, to remove outliers, generate high-quality model hypotheses, and effectively estimate model instances. Finally, LSC is able to provide consistent and reliable solutions within only a few milliseconds for general multi-structural model fitting, due to its deterministic fitting nature and efficiency. Compared with several state-of-the-art model fitting methods, our LSC achieves significant superiority for the performance of both accuracy and speed on synthetic data and real images. The code will be available at https://github.com/guobaoxiao/LSC.
Language	English
Publishing date	2024-03-18
Publishing country	United States
Document type	Journal Article
ISSN	1939-3539
ISSN (online)	1939-3539
DOI	10.1109/TPAMI.2024.3376731
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Salient Objects in Clutter.

Fan, Deng-Ping / Zhang, Jing / Xu, Gang / Cheng, Ming-Ming / Shao, Ling

IEEE transactions on pattern analysis and machine intelligence

2023 Volume 45, Issue 2, Page(s) 2344–2366

Abstract: In this paper, we identify and address a serious design bias of existing salient object detection (SOD) datasets, which unrealistically assume that each image should contain at least one clear and uncluttered salient object. This design bias has led to a ...

Abstract	In this paper, we identify and address a serious design bias of existing salient object detection (SOD) datasets, which unrealistically assume that each image should contain at least one clear and uncluttered salient object. This design bias has led to a saturation in performance for state-of-the-art SOD models when evaluated on existing datasets. However, these models are still far from satisfactory when applied to real-world scenes. Based on our analyses, we propose a new high-quality dataset and update the previous saliency benchmark. Specifically, our dataset, called Salient Objects in Clutter (SOC), includes images with both salient and non-salient objects from several common object categories. In addition to object category annotations, each salient image is accompanied by attributes that reflect common challenges in common scenes, which can help provide deeper insight into the SOD problem. Further, with a given saliency encoder, e.g., the backbone network, existing saliency models are designed to achieve mapping from the training image set to the training ground-truth set. We therefore argue that improving the dataset can yield higher performance gains than focusing only on the decoder design. With this in mind, we investigate several dataset-enhancement strategies, including label smoothing to implicitly emphasize salient boundaries, random image augmentation to adapt saliency models to various scenarios, and self-supervised learning as a regularization strategy to learn from small datasets. Our extensive results demonstrate the effectiveness of these tricks. We also provide a comprehensive benchmark for SOD, which can be found in our repository: https://github.com/DengPingFan/SODBenchmark.
Language	English
Publishing date	2023-01-06
Publishing country	United States
Document type	Journal Article
ISSN	1939-3539
ISSN (online)	1939-3539
DOI	10.1109/TPAMI.2022.3166451
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Salient Object Detection via Integrity Learning.

Zhuge, Mingchen / Fan, Deng-Ping / Liu, Nian / Zhang, Dingwen / Xu, Dong / Shao, Ling

IEEE transactions on pattern analysis and machine intelligence

2023 Volume 45, Issue 3, Page(s) 3738–3752

Abstract: Although current salient object detection (SOD) works have achieved significant progress, they are limited when it comes to the integrity of the predicted salient regions. We define the concept of integrity at both a micro and macro level. Specifically, ... ...

Abstract	Although current salient object detection (SOD) works have achieved significant progress, they are limited when it comes to the integrity of the predicted salient regions. We define the concept of integrity at both a micro and macro level. Specifically, at the micro level, the model should highlight all parts that belong to a certain salient object. Meanwhile, at the macro level, the model needs to discover all salient objects in a given image. To facilitate integrity learning for SOD, we design a novel Integrity Cognition Network (ICON), which explores three important components for learning strong integrity features. 1) Unlike existing models, which focus more on feature discriminability, we introduce a diverse feature aggregation (DFA) component to aggregate features with various receptive fields (i.e., kernel shape and context) and increase feature diversity. Such diversity is the foundation for mining the integral salient objects. 2) Based on the DFA features, we introduce an integrity channel enhancement (ICE) component with the goal of enhancing feature channels that highlight the integral salient objects, while suppressing the other distracting ones. 3) After extracting the enhanced features, the part-whole verification (PWV) method is employed to determine whether the part and whole object features have strong agreement. Such part-whole agreements can further improve the micro-level integrity for each salient object. To demonstrate the effectiveness of our ICON, comprehensive experiments are conducted on seven challenging benchmarks. Our ICON outperforms the baseline methods in terms of a wide range of metrics. Notably, our ICON achieves ∼ 10% relative improvement over the previous best model in terms of average false negative ratio (FNR), on six datasets. Codes and results are available at: https://github.com/mczhuge/ICON.
Language	English
Publishing date	2023-02-03
Publishing country	United States
Document type	Journal Article
ISSN	1939-3539
ISSN (online)	1939-3539
DOI	10.1109/TPAMI.2022.3179526
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Concealed Object Detection.

Fan, Deng-Ping / Ji, Ge-Peng / Cheng, Ming-Ming / Shao, Ling

IEEE transactions on pattern analysis and machine intelligence

2022 Volume 44, Issue 10, Page(s) 6024–6042

Abstract: We present the first systematic study on concealed object detection (COD), which aims to identify objects that are visually embedded in their background. The high intrinsic similarities between the concealed objects and their background make COD far more ...

Abstract	We present the first systematic study on concealed object detection (COD), which aims to identify objects that are visually embedded in their background. The high intrinsic similarities between the concealed objects and their background make COD far more challenging than traditional object detection/segmentation. To better understand this task, we collect a large-scale dataset, called COD10K, which consists of 10,000 images covering concealed objects in diverse real-world scenarios from 78 object categories. Further, we provide rich annotations including object categories, object boundaries, challenging attributes, object-level labels, and instance-level annotations. Our COD10K is the largest COD dataset to date, with the richest annotations, which enables comprehensive concealed object understanding and can even be used to help progress several other vision tasks, such as detection, segmentation, classification etc. Motivated by how animals hunt in the wild, we also design a simple but strong baseline for COD, termed the Search Identification Network (SINet). Without any bells and whistles, SINet outperforms twelve cutting-edge baselines on all datasets tested, making them robust, general architectures that could serve as catalysts for future research in COD. Finally, we provide some interesting findings, and highlight several potential applications and future directions. To spark research in this new field, our code, dataset, and online demo are available at our project page: http://mmcheng.net/cod.
MeSH term(s)	Algorithms ; Animals ; Image Interpretation, Computer-Assisted/methods
Language	English
Publishing date	2022-09-14
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1939-3539
ISSN (online)	1939-3539
DOI	10.1109/TPAMI.2021.3085766
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: BA-SAM

Song, Yiran / Zhou, Qianyu / Li, Xiangtai / Fan, Deng-Ping / Lu, Xuequan / Ma, Lizhuang

Scalable Bias-Mode Attention Mask for Segment Anything Model

2024

Abstract: In this paper, we address the challenge of image resolution variation for the Segment Anything Model (SAM). SAM, known for its zero-shot generalizability, exhibits a performance degradation when faced with datasets with varying image sizes. Previous ... ...

Abstract	In this paper, we address the challenge of image resolution variation for the Segment Anything Model (SAM). SAM, known for its zero-shot generalizability, exhibits a performance degradation when faced with datasets with varying image sizes. Previous approaches tend to resize the image to a fixed size or adopt structure modifications, hindering the preservation of SAM's rich prior knowledge. Besides, such task-specific tuning necessitates a complete retraining of the model, which is cost-expensive and unacceptable for deployment in the downstream tasks. In this paper, we reformulate this issue as a length extrapolation problem, where token sequence length varies while maintaining a consistent patch size for images of different sizes. To this end, we propose Scalable Bias-Mode Attention Mask (BA-SAM) to enhance SAM's adaptability to varying image resolutions while eliminating the need for structure modifications. Firstly, we introduce a new scaling factor to ensure consistent magnitude in the attention layer's dot product values when the token sequence length changes. Secondly, we present a bias-mode attention mask that allows each token to prioritize neighboring information, mitigating the impact of untrained distant information. Our BA-SAM demonstrates efficacy in two scenarios: zero-shot and fine-tuning. Extensive evaluation on diverse datasets, including DIS5K, DUTS, ISIC, COD10K, and COCO, reveals its ability to significantly mitigate performance degradation in the zero-shot setting and achieve state-of-the-art performance with minimal fine-tuning. Furthermore, we propose a generalized model and benchmark, showcasing BA-SAM's generalizability across all four datasets simultaneously. Comment: Code:https://github.com/zongzi13545329/BA-SAM
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	004
Publishing date	2024-01-04
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Vanishing-Point-Guided Video Semantic Segmentation of Driving Scenes

Guo, Diandian / Fan, Deng-Ping / Lu, Tongyu / Sakaridis, Christos / Van Gool, Luc

2024

Abstract: The estimation of implicit cross-frame correspondences and the high computational cost have long been major challenges in video semantic segmentation (VSS) for driving scenes. Prior works utilize keyframes, feature propagation, or cross-frame attention ... ...

Abstract	The estimation of implicit cross-frame correspondences and the high computational cost have long been major challenges in video semantic segmentation (VSS) for driving scenes. Prior works utilize keyframes, feature propagation, or cross-frame attention to address these issues. By contrast, we are the first to harness vanishing point (VP) priors for more effective segmentation. Intuitively, objects near VPs (i.e., away from the vehicle) are less discernible. Moreover, they tend to move radially away from the VP over time in the usual case of a forward-facing camera, a straight road, and linear forward motion of the vehicle. Our novel, efficient network for VSS, named VPSeg, incorporates two modules that utilize exactly this pair of static and dynamic VP priors: sparse-to-dense feature mining (DenseVP) and VP-guided motion fusion (MotionVP). MotionVP employs VP-guided motion estimation to establish explicit correspondences across frames and help attend to the most relevant features from neighboring frames, while DenseVP enhances weak dynamic features in distant regions around VPs. These modules operate within a context-detail framework, which separates contextual features from high-resolution local features at different input resolutions to reduce computational costs. Contextual and local features are integrated through contextualized motion attention (CMA) for the final prediction. Extensive experiments on two popular driving segmentation benchmarks, Cityscapes and ACDC, demonstrate that VPSeg outperforms previous SOTA methods, with only modest computational overhead.
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	629
Publishing date	2024-01-26
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: GCoNet+: A Stronger Group Collaborative Co-Salient Object Detector.

Zheng, Peng / Fu, Huazhu / Fan, Deng-Ping / Fan, Qi / Qin, Jie / Tai, Yu-Wing / Tang, Chi-Keung / Van Gool, Luc

IEEE transactions on pattern analysis and machine intelligence

2023 Volume 45, Issue 9, Page(s) 10929–10946

Abstract: In this paper, we present a novel end-to-end group collaborative learning network, termed GCoNet+, which can effectively and efficiently (250 fps) identify co-salient objects in natural scenes. The proposed GCoNet+ achieves the new state-of-the-art ... ...

Abstract	In this paper, we present a novel end-to-end group collaborative learning network, termed GCoNet+, which can effectively and efficiently (250 fps) identify co-salient objects in natural scenes. The proposed GCoNet+ achieves the new state-of-the-art performance for co-salient object detection (CoSOD) through mining consensus representations based on the following two essential criteria: 1) intra-group compactness to better formulate the consistency among co-salient objects by capturing their inherent shared attributes using our novel group affinity module (GAM); 2) inter-group separability to effectively suppress the influence of noisy objects on the output by introducing our new group collaborating module (GCM) conditioning on the inconsistent consensus. To further improve the accuracy, we design a series of simple yet effective components as follows: i) a recurrent auxiliary classification module (RACM) promoting model learning at the semantic level; ii) a confidence enhancement module (CEM) assisting the model in improving the quality of the final predictions; and iii) a group-based symmetric triplet (GST) loss guiding the model to learn more discriminative features. Extensive experiments on three challenging benchmarks, i.e., CoCA, CoSOD3k, and CoSal2015, demonstrate that our GCoNet+ outperforms the existing 12 cutting-edge models. Code has been released at https://github.com/ZhengPeng7/GCoNet_plus.
Language	English
Publishing date	2023-08-07
Publishing country	United States
Document type	Journal Article
ISSN	1939-3539
ISSN (online)	1939-3539
DOI	10.1109/TPAMI.2023.3264571
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: IC9600: A Benchmark Dataset for Automatic Image Complexity Assessment.

Feng, Tinglei / Zhai, Yingjie / Yang, Jufeng / Liang, Jie / Fan, Deng-Ping / Zhang, Jing / Shao, Ling / Tao, Dacheng

IEEE transactions on pattern analysis and machine intelligence

2023 Volume 45, Issue 7, Page(s) 8577–8593

Abstract: Image complexity (IC) is an essential visual perception for human beings to understand an image. However, explicitly evaluating the IC is challenging, and has long been overlooked since, on the one hand, the evaluation of IC is relatively subjective due ... ...

Abstract	Image complexity (IC) is an essential visual perception for human beings to understand an image. However, explicitly evaluating the IC is challenging, and has long been overlooked since, on the one hand, the evaluation of IC is relatively subjective due to its dependence on human perception, and on the other hand, the IC is semantic-dependent while real-world images are diverse. To facilitate the research of IC assessment in this deep learning era, we built the first, to our best knowledge, large-scale IC dataset with 9,600 well-annotated images. The images are of diverse areas such as abstract, paintings and real-world scenes, each of which is elaborately annotated by 17 human contributors. Powered by this high-quality dataset, we further provide a base model to predict the IC scores and estimate the complexity density maps in a weakly supervised way. The model is verified to be effective, and correlates well with human perception (with the Pearson correlation coefficient being 0.949). Last but not the least, we have empirically validated that the exploration of IC can provide auxiliary information and boost the performance of a wide range of computer vision tasks. The dataset and source code can be found at https://github.com/tinglyfeng/IC9600.
Language	English
Publishing date	2023-06-05
Publishing country	United States
Document type	Journal Article
ISSN	1939-3539
ISSN (online)	1939-3539
DOI	10.1109/TPAMI.2022.3232328
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Bilateral Reference for High-Resolution Dichotomous Image Segmentation

Zheng, Peng / Gao, Dehong / Fan, Deng-Ping / Liu, Li / Laaksonen, Jorma / Ouyang, Wanli / Sebe, Nicu

2024

Abstract: We introduce a novel bilateral reference framework (***BiRefNet***) for high-resolution dichotomous image segmentation (DIS). It comprises two essential components: the localization module (LM) and the reconstruction module (RM) with our proposed ... ...

Abstract	We introduce a novel bilateral reference framework (*BiRefNet) for high-resolution dichotomous image segmentation (DIS). It comprises two essential components: the localization module (LM) and the reconstruction module (RM) with our proposed bilateral reference (BiRef). The LM aids in object localization using global semantic information. Within the RM, we utilize BiRef for the reconstruction process, where hierarchical patches of images provide the source reference and gradient maps serve as the target reference. These components collaborate to generate the final predicted maps. We also introduce auxiliary gradient supervision to enhance focus on regions with finer details. Furthermore, we outline practical training strategies tailored for DIS to improve map quality and training process. To validate the general applicability of our approach, we conduct extensive experiments on four tasks to evince that BiRefNet* exhibits remarkable performance, outperforming task-specific cutting-edge methods across all benchmarks.
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	004
Publishing date	2024-01-07
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Masked Vision-Language Transformer in Fashion

Ji, Ge-Peng / Zhuge, Mingchen / Gao, Dehong / Fan, Deng-Ping / Sakaridis, Christos / Van Gool, Luc

Machine Intelligence Research, 20 (3)

2023

Abstract: We present a masked vision-language transformer (MVLT) for fashion-specific multi-modal representation. Technically, we simply utilize the vision transformer architecture for replacing the bidirectional encoder representations from Transformers (BERT) in ...

Abstract	We present a masked vision-language transformer (MVLT) for fashion-specific multi-modal representation. Technically, we simply utilize the vision transformer architecture for replacing the bidirectional encoder representations from Transformers (BERT) in the pre-training model, making MVLT the first end-to-end framework for the fashion domain. Besides, we designed masked image reconstruction (MIR) for a fine-grained understanding of fashion. MVLT is an extensible and convenient architecture that admits raw multi-modal inputs without extra pre-processing models (e.g., ResNet), implicitly modeling the vision-language alignments. More importantly, MVLT can easily generalize to various matching and generative tasks. Experimental results show obvious improvements in retrieval (rank@5: 17%) and recognition (accuracy: 3%) tasks over the Fashion-Gen 2018 winner, Kaleido-BERT. The code is available at https://github.com/GewelsJI/MVLT. ISSN:2731-538X ISSN:2731-5398
Keywords	Computer Vision ; Transformers ; Vision and language ; Masked image reconstruction ; Fashion ; info:eu-repo/classification/ddc/004 ; Data processing ; computer science
Subject code	004
Language	English
Publisher	Springer
Publishing country	ch
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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