A Visual Representation-guided Framework with Global Affinity for Weakly Supervised Salient Object Detection

Fully supervised salient object detection (SOD) methods have made considerable progress in performance, yet these models rely heavily on expensive pixel-wise labels. Recently, to achieve a trade-off between labeling burden and performance, scribble-based SOD methods have attracted increasing attention. Previous scribble-based models directly implement the SOD task only based on SOD training data with limited information, it is extremely difficult for them to understand the image and further achieve a superior SOD task. In this paper, we propose a simple yet effective framework guided by general visual representations with rich contextual semantic knowledge for scribble-based SOD. These general visual representations are generated by self-supervised learning based on large-scale unlabeled datasets. Our framework consists of a task-related encoder, a general visual module, and an information integration module to efficiently combine the general visual representations with task-related features to perform the SOD task based on understanding the contextual connections of images. Meanwhile, we propose a novel global semantic affinity loss to guide the model to perceive the global structure of the salient objects. Experimental results on five public benchmark datasets demonstrate that our method, which only utilizes scribble annotations without introducing any extra label, outperforms the state-of-the-art weakly supervised SOD methods. Specifically, it outperforms the previous best scribble-based method on all datasets with an average gain of 5.5% for max f-measure, 5.8% for mean f-measure, 24% for MAE, and 3.1% for E-measure. Moreover, our method achieves comparable or even superior performance to the state-of-the-art fully supervised models

A Normalized Fuzzy Neural Network and its Application

A normal fuzzy neural network(NFNN) with five layers is proposed. Focusing on the structure optimization of network, a new node selection method and corresponding back propagation learning algorithm rules are presented In the case with fewer input nodes, the training is more fast in this kind of neural network. Water-flooded zone identification in measure-well explanation is an important problem in the oil field development; especially in its later period. Complex geology conditions lead to many fuzzy characters in measure-well curves. In the combination of all kinds of fuzzy conditions, oil water-flooded behaves as strong water-flooded, middle water-flooded, weak water-flooded and no water-flooded, etc. NFNN is applied to water-flooded identification in oil well measure-well to find its mapping relation between well measure-well and water-flooded level,accordingly realize the water-flooded zone identification in measure-well explanation of fuzzy oil. Test results illustrate its practicabilit

Motion-aware Memory Network for Fast Video Salient Object Detection

Previous methods based on 3DCNN, convLSTM, or optical flow have achieved great success in video salient object detection (VSOD). However, they still suffer from high computational costs or poor quality of the generated saliency maps. To solve these problems, we design a space-time memory (STM)-based network, which extracts useful temporal information of the current frame from adjacent frames as the temporal branch of VSOD. Furthermore, previous methods only considered single-frame prediction without temporal association. As a result, the model may not focus on the temporal information sufficiently. Thus, we initially introduce object motion prediction between inter-frame into VSOD. Our model follows standard encoder--decoder architecture. In the encoding stage, we generate high-level temporal features by using high-level features from the current and its adjacent frames. This approach is more efficient than the optical flow-based methods. In the decoding stage, we propose an effective fusion strategy for spatial and temporal branches. The semantic information of the high-level features is used to fuse the object details in the low-level features, and then the spatiotemporal features are obtained step by step to reconstruct the saliency maps. Moreover, inspired by the boundary supervision commonly used in image salient object detection (ISOD), we design a motion-aware loss for predicting object boundary motion and simultaneously perform multitask learning for VSOD and object motion prediction, which can further facilitate the model to extract spatiotemporal features accurately and maintain the object integrity. Extensive experiments on several datasets demonstrated the effectiveness of our method and can achieve state-of-the-art metrics on some datasets. The proposed model does not require optical flow or other preprocessing, and can reach a speed of nearly 100 FPS during inference.Comment: 12 pages, 10 figure

General Rotation Invariance Learning for Point Clouds via Weight-Feature Alignment

Compared to 2D images, 3D point clouds are much more sensitive to rotations. We expect the point features describing certain patterns to keep invariant to the rotation transformation. There are many recent SOTA works dedicated to rotation-invariant learning for 3D point clouds. However, current rotation-invariant methods lack generalizability on the point clouds in the open scenes due to the reliance on the global distribution, \ie the global scene and backgrounds. Considering that the output activation is a function of the pattern and its orientation, we need to eliminate the effect of the orientation.In this paper, inspired by the idea that the network weights can be considered a set of points distributed in the same 3D space as the input points, we propose Weight-Feature Alignment (WFA) to construct a local Invariant Reference Frame (IRF) via aligning the features with the principal axes of the network weights. Our WFA algorithm provides a general solution for the point clouds of all scenes. WFA ensures the model achieves the target that the response activity is a necessary and sufficient condition of the pattern matching degree. Practically, we perform experiments on the point clouds of both single objects and open large-range scenes. The results suggest that our method almost bridges the gap between rotation invariance learning and normal methods.Comment: 4 figure

AFPN: Asymptotic Feature Pyramid Network for Object Detection

Multi-scale features are of great importance in encoding objects with scale variance in object detection tasks. A common strategy for multi-scale feature extraction is adopting the classic top-down and bottom-up feature pyramid networks. However, these approaches suffer from the loss or degradation of feature information, impairing the fusion effect of non-adjacent levels. This paper proposes an asymptotic feature pyramid network (AFPN) to support direct interaction at non-adjacent levels. AFPN is initiated by fusing two adjacent low-level features and asymptotically incorporates higher-level features into the fusion process. In this way, the larger semantic gap between non-adjacent levels can be avoided. Given the potential for multi-object information conflicts to arise during feature fusion at each spatial location, adaptive spatial fusion operation is further utilized to mitigate these inconsistencies. We incorporate the proposed AFPN into both two-stage and one-stage object detection frameworks and evaluate with the MS-COCO 2017 validation and test datasets. Experimental evaluation shows that our method achieves more competitive results than other state-of-the-art feature pyramid networks. The code is available at \href{https://github.com/gyyang23/AFPN}{https://github.com/gyyang23/AFPN}

Identification and characterization of four immune-related signatures in keloid

A keloid is a fibroproliferative disorder of unknown etiopathogenesis that requires ill-defined treatment. Existing evidence indicates that the immune system plays an important role in the occurrence and development of keloid. However, there is still a lack of research on the immune-related signatures of keloid. Here we identified immune-related signatures in keloid and explored their pathological mechanisms. Transcriptomic datasets (GSE7890, GSE92566, and GSE44270) of keloid and normal skin tissues were obtained from the Gene Expression Omnibus database. The overlap of differentially expressed genes and immune-related genes was considered as differentially expressed immune-related genes (DEIGs). Functional analysis, expression, and distribution were applied to explore the function and characteristics of DEIGs, and the expression of these DEIGs in keloid and normal skin tissues was verified by immunohistochemistry. Finally, we conducted interactive network analysis and immune infiltration analysis to determine the therapeutic potential and immune correlation. We identified four DEIGs (LGR5, PTN, JAG1, and DKK1). In these datasets, only GSE7890 met the screening criteria. In the GSE7890 dataset, DKK1 and PTN were downregulated in keloid, whereas JAG1 and LGR5 were upregulated in keloid. In addition, we obtained the same conclusion through immunohistochemistry. Functional analysis indicated that these four DEIGs were mainly involved in stem cell, cell cycle, UV response, and therapy resistance. Through interactive network analysis, we found that these DEIGs were associated with drugs currently used to treat keloid, such as hydrocortisone, androstanolone, irinotecan, oxaliplatin, BHQ-880, and lecoleucovorin. Finally, many immune cells, including CD8+ T cells, resting memory CD4+ T cells, and M1 macrophages, were obtained by immune infiltration analysis. In conclusion, we identified four immune signaling molecules associated with keloid (LGR5, PTN, JAG1, and DKK1). These immune-related signaling molecules may be important modules in the pathogenesis of keloid. Additionally, we developed novel therapeutic targets for the treatment of this challenging disease

Thickness-dependent magnetic properties in Pt[CoNi]n multilayers with perpendicular magnetic anisotropy

We systematically investigated the Ni and Co thickness-dependent perpendicular magnetic anisotropy (PMA) coefficient, magnetic domain structures, and magnetization dynamics of Pt(5 nm)/[Co(t_Co nm)/Ni(t_Ni nm)]5/Pt(1 nm) multilayers by combining the four standard magnetic characterization techniques. The magnetic-related hysteresis loops obtained from the field-dependent magnetization M and anomalous Hall resistivity (AHR) \r{ho}_xy found that the two serial multilayers with t_Co = 0.2 and 0.3 nm have the optimum PMA coefficient K_U well as the highest coercivity H_C at the Ni thickness t_Ni = 0.6 nm. Additionally, the magnetic domain structures obtained by Magneto-optic Kerr effect (MOKE) microscopy also significantly depend on the thickness and K_U of the films. Furthermore, the thickness-dependent linewidth of ferromagnetic resonance is inversely proportional to K_U and H_C, indicating that inhomogeneous magnetic properties dominate the linewidth. However, the intrinsic Gilbert damping constant determined by a linear fitting of frequency-dependent linewidth does not depend on Ni thickness and K_U. Our results could help promote the PMA [Co/Ni] multilayer applications in various spintronic and spin-orbitronic devices.Comment: 17 pages, 4 figure