Reviewerly/exHarmony huggingface dataset:
Coarse-to-fine online latent representations matching for one-stage domain adaptive semantic segmentation

Authors:
raw_author_name: Zihao Dong
raw_affiliation_string: School of Information Science and Engineering, University of Jinan, Jinan 250022, China
raw_author_name: Sijie Niu
raw_affiliation_string: School of Information Science and Engineering, University of Jinan, Jinan 250022, China
raw_author_name: Xizhan Gao
raw_affiliation_string: School of Information Science and Engineering, University of Jinan, Jinan 250022, China
raw_author_name: Xiuli Shao
raw_affiliation_string: College of Computer Science, Nankai University, Tianjin 300350, China

Abstract: Domain adaptive semantic segmentation is meaningful since collecting numerous labeled samples in different domains is expensive and time-consuming. Recent domain adaptation methods yield not so efficient performance compared with supervised learning. With the hypothesis that semantic feature can be shared across domains, this paper proposes a coarse-to-fine online matching architecture (COM) for one-stage domain adaptation. We consider subsequent learning stages progressively refining the task in the latent feature space, i.e. the finer set at each component is hierarchically derived from the coarser set of the previous components, including cross-domain global prototypes, categories and instances matching and anchor-points contrastive learning, which further achieve self-supervised learning with region-level pseudo label generated only in a single training step. Beforehand, feature refinement are performed to realize edge perception and inter-feature augmentation. Then, coarse-to-fine network fuses global and local consistency matching via specific distribution alignment between the source and target domain. Finally, the adversarial structure controls the uncertainty of generator prediction through the maximization of classification results and minimization of two classifiers discrepancy. This proposed method is evaluated in two unsupervised domain adaptation tasks, i.e. GTA5 → Cityscapes and SYNTHIA → Cityscapes. Extensive experiments verify the effectiveness of our proposed COM model and demonstrate its superiority over several state-of-the-art approaches.
Parallel disentangling network for human–object interaction detection

Authors:
raw_author_name: Yamin Cheng
raw_affiliation_string: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
raw_author_name: Hancong Duan
raw_affiliation_string: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
raw_author_name: Chen Wang
raw_affiliation_string: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
raw_author_name: Zhijun Chen
raw_affiliation_string: SKLSDE Lab, School of Computer Science and Engineering, Beihang University, China

Abstract: Human–object interaction (HOI) detection aims to localize and classify triplets of human, object and interaction from a given image. Earlier two-stage methods suffer both from mutually independent training processes and the interference of redundant negative human–object pairs. Prevailing one-stage transformer-based methods are free from the above problems by tackling HOI in an end-to-end manner. However, one-stage transformer-based methods carry the unnecessary entanglements of the query for different tasks, i.e., human–object detection and interaction classification, and thus bring in poor performance. In this paper, we propose a new transformer-based approach that parallelly disentangles human–object detection and interaction classification in a triplet-wise manner. To make each query focus on one specific task clearly, we exhaustively disentangle HOI by parallelly expanding the naive query in vanilla transformer as triple explicit queries. Then, we introduce a semantic communication layer to preserve the consistent semantic association of each HOI through mixing the feature representations of each query triplet of the correspondence constraint. Extensive experiments demonstrate that our proposed framework outperforms the existing methods and achieves the state-of-the-art performance, with significant reduction in parameters and FLOPs.
Multi-view graph imputation network

Authors:
raw_author_name: Xin Peng
raw_affiliation_string: College of Computer Science and Technology, Hainan University, Haikou, 570228, China
raw_author_name: Jieren Cheng
raw_affiliation_string: College of Computer Science and Technology, Hainan University, Haikou, 570228, China; Hainan Blockchain Technology Engineering Research Center, Haikou 570228, China
raw_author_name: Xiangyan Tang
raw_affiliation_string: College of Computer Science and Technology, Hainan University, Haikou, 570228, China; College of Intelligence and Computing, Tianjin University, Tianjin 300072, China
raw_author_name: Bin Zhang
raw_affiliation_string: College of Computer Science and Technology, Hainan University, Haikou, 570228, China
raw_author_name: Wenxuan Tu
raw_affiliation_string: College of Computing, National University of Defense Technology, Changsha 410073, China

Abstract: Graph data in the real world is often accompanied by the problem of missing attributes. Recently, self-supervised graph representation learning, implementing data imputation according to observable nodes, has become a paradigm for studying attribute-missing graphs. However, existing methods directly encode attribute-missing graphs and then impute attribute-missing nodes in the latent space, which often increases the uncertainty of node embedding information, thereby limiting the performance of attribute imputation. To address this issue, we propose a novel method named Multi-view GrAph impuTation nEtwork (MATE), which performs attribute imputation in the input space for attribute-missing graphs. Specifically, we first employ parameter initialization and graph diffusion in the input space to generate relatively complete multi-view from both the attribute and structure levels. To provide reliable guidance in each epoch for parameter initialization, we propose a Dual Constraint Strategy (DCS) that maximizes the consistency of node embeddings between two views. In this way, the learning of parameter initialization and node embedding promotes each other, thus effectively improving the quality of attribute imputation. Extensive experiments on four benchmark datasets demonstrate that our proposed MATE achieves state-of-the-art performance. The corresponding code is available at https://github.com/XinPeng97/MATE.
Adaptive Trust Model for Multi-Agent Teaming Based on Reinforcement-Learning-Based Fusion

Authors:
raw_author_name: Chin-Teng Lin
raw_affiliation_string: GrapheneX-UTS Human-centric Artificial Intelligence Centre, University of Technology Sydney, Ultimo, NSW, Australia
raw_author_name: Liang Ou
raw_affiliation_string: GrapheneX-UTS Human-centric Artificial Intelligence Centre, University of Technology Sydney, Ultimo, NSW, Australia
raw_author_name: Yu-Cheng Chang
raw_affiliation_string: GrapheneX-UTS Human-centric Artificial Intelligence Centre, University of Technology Sydney, Ultimo, NSW, Australia
raw_author_name: Yu-Kai Wang
raw_affiliation_string: GrapheneX-UTS Human-centric Artificial Intelligence Centre, University of Technology Sydney, Ultimo, NSW, Australia

Abstract: The performance of agents is highly influenced by multiple factors, including ability, decision, and states. Trust modeling is widely used to boost the performance of multiagent teaming (MAT). However, most existing trust models rely on statistical methods or preset parameters to assess the trust value in the MAT scenario. In this article, an adaptive trust model is proposed to evaluate comprehensive trust values based on multiple pieces of evidence from variant sources. The proposed trust model leverages information fusion and RL to properly fuse multiple pieces of evidence to generate trust value for every agent in MAT. The trust value is then used in an interaction protocol with MAT to increase the efficiency of cooperation. To verify the performance of the proposed trust model, a ball-collection experiment is designed for MAT to work cooperatively in simulation environments. Two different scenario settings are used to demonstrate the adaptability and robustness of the proposed trust model. The results are further compared with human-designed fusion methods. The comparison shows that the proposed trust model has a better representation of agent performance, namely convergence speed, than human-designed methods in different scenario settings.
CS-GAC: Compressively sensed geodesic active contours

Authors:
raw_author_name: Hao Shan
raw_affiliation_string: Xinjiang Key Laboratory of Radio Astrophysics, Chinese Academy of Sciences, no. 150, Science Street 1st, Urumqi, Xinjiang, 830011, China

Abstract: This paper proposes an edge based compressively sensed (CS) geodesic active contour (GAC) model, termed CS-GAC, to ensure faithful edge detection and accurate object segmentation. The motivation behind this paper is that edge information driving the contour evolution can be iteratively obtained by incomplete CS measurements. In each iteration, the CS-GAC is a three-step process including edge detection, active contouring and sparse reconstruction. Instead of working on the final reconstructed images themselves, the evolution of the CS-GAC is driven by a few CS measurements and guided by updatable edge information. The edge information is generated by a complex shearlet transform (CST) based edge map. In the framework, reconstruction and edge detection work alternately. The iterative update property that takes advantages of both edge sparsity and edge detection can largely improve the evolution precision. Numerical experiments show that the CS-GAC can obtain challenging segmentation results in comparisons with the state of the art methods, and has competitive prospects.
Convolutional neural networks rarely learn shape for semantic segmentation

Authors:
raw_author_name: Maciej A. Mazurowski
raw_affiliation_string: Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA

Abstract: Shape learning, or the ability to leverage shape information, could be a desirable property of convolutional neural networks (CNNs) when target objects have specific shapes. While some research on the topic is emerging, there is no systematic study to conclusively determine whether and under what circumstances CNNs learn shape. Here, we present such a study in the context of segmentation networks where shapes are particularly important. We define shape and propose a new behavioral metric to measure the extent to which a CNN utilizes shape information. We then execute a set of experiments with synthetic and real-world data to progressively uncover under which circumstances CNNs learn shape and what can be done to encourage such behavior. We conclude that (i) CNNs do not learn shape in typical settings but rather rely on other features available to identify the objects of interest, (ii) CNNs can learn shape, but only if the shape is the only feature available to identify the object, (iii) sufficiently large receptive field size relative to the size of target objects is necessary for shape learning; (iv) a limited set of augmentations can encourage shape learning; (v) learning shape is indeed useful in the presence of out-of-distribution data.
Weighted side-window based gradient guided image filtering

Authors:
raw_author_name: Weimin Yuan
raw_affiliation_string: School of Astronautics, Beihang University, Beijing 100191, China
raw_author_name: Cai Meng
raw_affiliation_string: Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100191, China; School of Astronautics, Beihang University, Beijing 100191, China
raw_author_name: Xiangzhi Bai
raw_affiliation_string: Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100191, China; School of Astronautics, Beihang University, Beijing 100191, China

Abstract: Image filtering under guidance image, known as guided filtering (GF), has been successfully applied to a variety of applications. Existing GF methods utilize either conventional full window-based framework (FWF) or simple uniformly weighted aggregation strategy (UWA); thereby they suffer from edge-blurring. In this paper, based upon gradient guided filtering (GGF), a weighted side-window based gradient guided filtering (WSGGF) is proposed to address the aforementioned problem. First, both regression and adaptive regularization terms in GGF are improved upon eight side windows by introducing side window-based framework (SWF). L1 norm is adopted to choose the results calculated in side windows. Second, UWA strategy in GGF is replaced by a refined variance-based weighted average (VWA) aggregation. In VWA, the value of each weight is chosen inversely proportional to the corresponding estimator. We show that with these improvements our method can well retain the edge sharpness and is robust to visual artifacts. To cut down the time consumption, a fast version of WSGGF (FWSGGF) is further proposed by incorporating a simple but effective down-sampling strategy, which is about four times faster while maintaining the superior performance. By comparing with the state-of-the-art (SOTA) methods on edge-aware smoothing, detail enhancement, high dynamic range image (HDR) compression, image luminance adjustment, depth map upsampling and single image haze removal, the effectiveness and flexibility of our proposed methods are verified. The source code is available at: https://github.com/weimin581/WSGGF
Matrix randomized autoencoder

Authors:
raw_author_name: Shichen Zhang
raw_affiliation_string: Artificial Intelligent Institute, Hangzhou Dianzi University, Zhejiang, 310018, China; Machine Learning and I-health International Cooperation Base of Zhejiang Province, Hangzhou Dianzi University, 310018, China
raw_author_name: Tianlei Wang
raw_affiliation_string: Artificial Intelligent Institute, Hangzhou Dianzi University, Zhejiang, 310018, China; Machine Learning and I-health International Cooperation Base of Zhejiang Province, Hangzhou Dianzi University, 310018, China
raw_author_name: Jiuwen Cao
raw_affiliation_string: Artificial Intelligent Institute, Hangzhou Dianzi University, Zhejiang, 310018, China; Machine Learning and I-health International Cooperation Base of Zhejiang Province, Hangzhou Dianzi University, 310018, China
raw_author_name: Wandong Zhang
raw_affiliation_string: Department of Electrical and Computer Engineering, Western University, London, ON N6A 3K7, Canada
raw_author_name: Badong Chen
raw_affiliation_string: School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China

Abstract: Randomized autoencoder (RAE) has attracted much attention due to its strong capability of representation with fast learning speed. However, the mainstream RAEs are still designed for scalar/vector data, which inevitably destroys the structure information of tensor data. To alleviate this deficiency, a novel convolutions based matrix randomized autoencoder (MRAE) is developed for two-dimensional (2D) data in this paper, including a one-side MRAE (OMRAE) exploiting the row or column information and a double-side MRAE (DMRAE) that simultaneously extracts the row and column information by 2 parallel OMRAEs. To reduce meaningless encoded features, the within-class scatter matrix (WSI) and within-class interaction distance (WID) constraints are added into OMRAE resulting WSI-OMRAE and WID-OMRAE, respectively. To demonstrate the superiority, stacked MRAEs are embedded into hierarchical regularized least squares for one-class classification and comparisons with several state-of-the-art methods are provided. The source code would be available at https://github.com/ML-HDU/MRAE.
Semi-supervised clustering with two types of background knowledge: Fusing pairwise constraints and monotonicity constraints

Authors:
raw_author_name: Germán González-Almagro
raw_affiliation_string: Andalusian Institute of Data Science and Computational Intelligence (DaSCI), Spain; Department of Computer Science and Artificial Intelligence (DECSAI), University of Granada, Granada, Andalusia, Spain
raw_author_name: Juan Luis Suarez
raw_affiliation_string: Andalusian Institute of Data Science and Computational Intelligence (DaSCI), Spain; Department of Computer Science and Artificial Intelligence (DECSAI), University of Granada, Granada, Andalusia, Spain
raw_author_name: José-Ramón Cano
raw_affiliation_string: Department of Computer Science and Artificial Intelligence (DECSAI), University of Granada, Granada, Andalusia, Spain; Department of Computer Science, EPS of Linares, University of Jaen, Linares, Jaen, Andalusia, Spain
raw_author_name: Salvador García
raw_affiliation_string: Andalusian Institute of Data Science and Computational Intelligence (DaSCI), Spain; Department of Computer Science and Artificial Intelligence (DECSAI), University of Granada, Granada, Andalusia, Spain

Abstract: This study addresses the problem of performing clustering in the presence of two types of background knowledge: pairwise constraints and monotonicity constraints. Pairwise constraint relate pair of instances, indicating if they must or must not belong to the same cluster, while monotonicity constraints impose and order relationship between the cluster labels, and are applied to the dataset globally. To fuse these to types of background knowledge, the formal framework to perform clustering under monotonicity constraints is, firstly, defined, resulting in a specific distance measure. Pairwise constraints are integrated afterwards by designing an objective function which combines the proposed distance measure and a pairwise constraint-based penalty term, in order to fuse both types of information. This objective function can be optimized with an EM optimization scheme. The proposed method serves as the first approach to the problem it addresses, as it is the first method designed to work with the two types of background knowledge mentioned above. Our proposal is tested with previous approaches from different clustering paradigms in monotonic datasets, including a real-world case of study. Bayesian statistical tests are used to provide empirical evidence in favor of our proposal, and fund subsequent conclusions: the proposed method can be used obtain better results than previous approaches within the monotonic constrained clustering paradigm.
Domain generalization via Inter-domain Alignment and Intra-domain Expansion

Authors:
raw_author_name: Jiajun Hu
raw_affiliation_string: National Institute of Healthcare Data Science, Nanjing University, Nanjing, China
raw_author_name: Lei Qi
raw_affiliation_string: Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, Nanjing, China
raw_author_name: Jian Zhang
raw_affiliation_string: National Institute of Healthcare Data Science, Nanjing University, Nanjing, China
raw_author_name: Yinghuan Shi
raw_affiliation_string: National Institute of Healthcare Data Science, Nanjing University, Nanjing, China

Abstract: The performance of traditional deep learning models tends to drop dramatically during being deployed in real-world scenarios when the distribution shift between the seen training and unseen test data occurs. Domain Generalization methods are designed to achieve generalizability to deal with the above issue. Since the features extracted by softmax cross-entropy loss are not adequately domain-invariant, previous works in Domain Generalization have attempted to overcome this problem by employing contrastive-based losses which pull positive pairs (i.e., samples with the same class label) from different domains closer. Unfortunately, these approaches tend to produce an extremely small feature space, which is not robust facing unseen domain and easily overfits to source domains. To address the aforementioned issue, we propose a novel loss named IAIE Loss to simultaneously perform Inter-domain Alignment and Intra-domain Expansion for positive pairs, which facilitates the model to extract domain-invariant features and mitigates overfitting. Specifically, we design two sets of positive samples named "easy positive samples" and "hard positive samples". IAIE Loss pulls the hard positive pairs closer (alignment) while pushing the easy positive pairs apart (expansion). The state-of-the-art results on multiple DG benchmark datasets verify the effectiveness of our method.
A segmentation method based on the deep fuzzy segmentation model in combined with SCANDLE clustering

Authors:
raw_author_name: Zenan Yang
raw_affiliation_string: Research Centre of Arable Land Protection and Urban-rural High-quality Development of Yellow River Basin, Jiaozuo, 454003, China; School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, 454003, China
raw_author_name: Xiaoxuan Wang
raw_affiliation_string: Key Laboratory of Spatio-temporal Information and Ecological Restoration of Mines of Natural Resources of the People's Republic of China, Henan Polytechnic University, Jiaozuo, 454003, China
raw_author_name: Liangxin Fan
raw_affiliation_string: Research Centre of Arable Land Protection and Urban-rural High-quality Development of Yellow River Basin, Jiaozuo, 454003, China; School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, 454003, China

Abstract: To enhance the low clustering accuracy of the fuzzy clustering segmentation algorithm for analyzing high spatial resolution remote sensing images (HSRRSIs), a deep fuzzy segmentation model (DFSM)combined with Spectral Clustering with Adaptive Neighbors for Deep Learning (SCANDLE) clustering is proposed. The DFSM is used to over-segment the image, and the automatic coding structure is used to adaptively fuse the image features, minimizing the internal compactness and maximizing the external separability of the clustering, yielding better results. Meanwhile, the SCANDLE clustering model is used to cluster the over-segmentation results, and the matrix construction algorithm for adaptive neighborhood allocation is used to map the frame of the connected layer and optimally combine the over-segmentation images to realize the final segmentation results. The new method can accurately segment HSRRSIs with good segmentation performance.
Medoid Silhouette clustering with automatic cluster number selection

Authors:
raw_author_name: Lars Lenssen
raw_affiliation_string: TU Dortmund University, Informatik VIII, Dortmund 44221, Germany
raw_author_name: Erich Schubert
raw_affiliation_string: TU Dortmund University, Informatik VIII, Dortmund 44221, Germany

Abstract: The evaluation of clustering results is difficult, highly dependent on the evaluated data set and the perspective of the beholder. There are many different clustering quality measures, which try to provide a general measure to validate clustering results. A very popular measure is the Silhouette. We discuss the efficient medoid-based variant of the Silhouette, perform a theoretical analysis of its properties, provide two fast versions for the direct optimization, and discuss the use to choose the optimal number of clusters. We combine ideas from the original Silhouette with the well-known PAM algorithm and its latest improvements FasterPAM. One of the versions guarantees equal results to the original variant and provides a run speedup of O(k2). In experiments on real data with 30000 samples and k= 100, we observed a 10464× speedup compared to the original PAMMEDSIL algorithm. Additionally, we provide a variant to choose the optimal number of clusters directly.