Collaborative camouflaged object segmentation (CoCOS) is a challenging task, focusing on identifying objects that blend closely with their backgrounds by jointly processing intraclass images. Existing methods fail to fully leverage the shared features (e.g., shape, texture, and contour) from these intraclass images, which leads to poor segmentation performance in relatively complex scenarios. To address this issue, we propose a novel mutually guided fusion refinement network (MFRNet), which improves the model performance by more effectively collaborating and optimizing the shared information. Specifically, it includes feature encoding, single-image branch feature enhancement, multiimage branch feature enhancement, and mutual guidance. After the feature encoding step, we design the graph convolution self-attention (GCS) and spatial context exploration (SCE) modules to enhance multilevel features of the single-image and multiimage branches, respectively. Moreover, we propose a mutual guidance fusion (MGF) module to utilize cross-scene image information for mutual guidance and progressive refinement, enhancing intraclass collaboration for improving target feature distinction. Extensive experimental results demonstrate that our MFRNet significantly outperforms existing CoCOS methods, achieving a mean E-measure score of 0.846 on the CoCOD8K dataset. Our code will be published at https://github.com/another-u/MFRNet.

Most current medical image segmentation (MIS) methods focus on the performance of models while overlooking the interpretability of models. For existing segmentation models, the number of channels of the output feature maps is equal to the number of segmentation categories. The category of each pixel is determined by the channel with the highest probability, yet the reasons for this decision are unclear. Inspired by the principle that samples from the same category are spatially closer, we propose a novel subspace projection method to enhance both the performance and interpretability of MIS models. Our approach replaces the output layer of baseline models with a new convolutional layer that enhances channel-wise representational power, and concatenates the channel features of each pixel into feature vectors to integrate channel-level information. Considering the sparse structure of feature vectors, an overcomplete dictionary learning is applied to extract the most discriminative features. These feature vectors are projected into category subspaces, with pixel classification determined based on the smallest projection distance. To guarantee that feature vectors within the same category subspace are grouped closely and to promote nonoverlapping feature learning across subspaces, we introduce the distance loss and the subspace orthogonality loss. Our method is evaluated on ten popular MIS models using two publicly available datasets. Experimental results show that our approach outperforms baseline models in terms of both segmentation accuracy and the interpretability of model decision.

Accurate brain tumor segmentation is vital for clinical diagnosis and treatment. Due to motion artifacts and image damage, it is challenging to obtain accurate segmentation results of brain tumors in the presence of incomplete MRI modalities. We propose a multimodal reversible feature learning method to tackle this problem. This method can fully explore the potential feature similarities and complementarities between MRI modalities. To compensate the information of missing modalities, we propose a reversible feature interaction module. It explores information similarity among existing modalities as priors, which are used to reconstruct features of missing modalities at the feature level. With enhanced discriminative information, the model suppresses the noise in the missing modalities. Furthermore, we propose a dual-scale attention module to enhance the detail restoration and reconstruction accuracy of images. Comparison results on the BRATS challenge datasets show the superiority of our method over current popular methods. The code used in this study is available athttps://github.com/fybgogogo/reverse.

Face recognition has achieved remarkable success owing to the development of deep learning. However, most of existing face recognition models perform poorly against pose variations. We argue that, it is primarily caused by pose-based long-tailed data - imbalanced distribution of training samples between profile faces and near-frontal faces. Additionally, self-occlusion and nonlinear warping of facial textures caused by large pose variations also increase the difficulty in learning discriminative features of profile faces. In this study, we propose a novel framework called Symmetrical Siamese Network (SSN), which can simultaneously overcome the limitation of pose-based long-tailed data and pose-invariant features learning. Specifically, two sub-modules are proposed in the SSN, i.e., Feature-Consistence Learning sub-Net (FCLN) and Identity-Consistence Learning sub-Net (ICLN). For FCLN, the inputs are all face images on training dataset. Inspired by the contrastive learning, we simulate pose variations of faces and constrain the model to focus on the consistent areas between the original face image and its corresponding virtual pose face images. For ICLN, only profile images are used as inputs, and we propose to adopt Identity Consistence Loss to minimize the intra-class feature variation across different poses. The collaborative learning of two sub-modules guarantees that the parameters of network are updated in a relatively equal probability between near-frontal face images and profile images, so that the pose-based long-tailed problem can be effectively addressed. The proposed SSN shows comparable results over the state-of-the-art methods on several public datasets. In this study, LightCNN is selected as the backbone of SSN, and existing popular networks also can be used into our framework for pose-robust face recognition.

The accurate segmentation of brain tissue in Magnetic Resonance Image (MRI) slices is essential for assessing neurological conditions and brain diseases. However, it is challenging to segment MRI slices because of the low contrast between different brain tissues and the partial volume effect. 2-Dimensional (2-D) convolutional networks cannot handle such volumetric image data well because they overlook spatial information between MRI slices. Although 3-Dimensional (3-D) convolutions capture volumetric spatial information, they have not been fully exploited to enhance representative ability of deep networks; moreover, they may lead to overfitting with insufficient training data. In this paper, we propose a novel convolutional mechanism, termed Rubik convolution, to capture multi-dimensional information between MRI slices. Rubik convolution rotates the axis of a set of consecutive slices, enabling 2-D convolution kernels to extract features of each axial plane simultaneously. Next, feature maps are rotated back to fuse multidimensional information by the Max-View-Maps. Furthermore, we propose an efficient 2-D convolutional network, namely Rubik-Net, where the residual connections and the bottleneck structure are used to enhance information transmission and reduce the number of network parameters. The Rubik-Net shows promising results on iSeg2017, iSeg2019, IBSR and BrainWeb datasets in terms of segmentation accuracy. In particular, we achieved the best results in 95th percentile Hausdorff distance and average surface distance in cerebrospinal fluid segmentation on the most challenging iSeg2019 dataset. The experiments indicate that Rubik-Net improves the accuracy and efficiency of medical image segmentation. Moreover, Rubik convolution can be easily embedded into existing 2-D convolutional networks.

Feature representation is highly important for many computer vision tasks. A broad range of prior studies have been proposed to strengthen representation ability of architectures via built-in blocks. However, during the forward propagation, the reduction in feature map scales still leads to the lack of representation ability. In this paper, we focus on boosting the representational power of a convolutional network by the multi-branch framework that we term the BranchNet. Each branch is directly supervised by label information to enrich the hierarchy features in BranchNet. Based on this framework, we further propose a collaborative learning loss and a soft target loss to transfer knowledge from deeper layers to shallow layers. BranchNet is an efficient training framework without extra parameters introduced in inference and can be integrated in existing networks, e.g., VGG, ResNet, and DenseNet. We evaluate BranchNet on all of these models and find that our method outperforms the baseline models on the widely-used CIFAR and ImageNet datasets. In particular, on the CIFAR-100 dataset, the classification error of ResNet-164 with BranchNet decreases by 4.51 percent. We also conduct experiments on the representative computer vision tasks of instance segmentation and class activation mapping, further verifying the superiority of BranchNet over the baseline models. Models and code are available athttps://github.com/zyyupup/BranchNet/.

As an important building block in automatic medical applications, image segmentation has made a great progress due to the data-driving mechanism of deep architecture. Recently, numerous methods have been proposed to boost the segmentation performance based on U-shape network. However, they often built feature encoders with only one data routine, which have limited the representation ability of the networks. Although some methods applied multiple learning paths to fix this problem, the deep supervision techniques are required to monitor the training status at individual path, which may bring extra burden to practical usage of the algorithm. Additionally, under these frameworks, the semantic gap between different paths may interfere with model's learning performance, and the potential transduction ability of skip connections still needs further investigation. To address these issues, we introduce a novel medical image segmentation framework, namely AFT-Net, in which an attention-based data fusion model is proposed to effectively cooperate with multi-stream encoder. By progressively accumulating the features from different paths, our method can establish meaningful connections between structural and semantic features, while keeping an integral and flexible layout without deeply customized supervisions. Extensive experiments on two medical image data sets demonstrate that our method is able to acquire image features with both diversity and quality, thereby outperforms current state-of-the-art segmentation methods.

In this article, we study solder paste inspection (SPI), an important stage that is used in the semiconductor manufacturing industry, where abnormal boards should be detected. A highly accurate SPI can substantially reduce human expert involvement, as well as reduce the waste in disposing of the boards in good condition. A key difference today is that because of increasing demand in board customization, the number of board types increases substantially and quantity of the boards produced in each type decreases. Thus, the previous approaches where a fine-tuned model is developed for each board type are no longer viable. Intrinsically, our problem is an anomaly detection problem. A major specialty in today’s SPI is that the target tasks for prediction cannot be fully pre-determined due to context changes during the solder paste printing stage. Our experiences show that a conventional approach to first define a set of tasks and train these tasks offline will lead to low accuracy. Here, we propose a novel multi-task approach, where the performance of all target tasks is ensured simultaneously. We note that the SPI process is streamlined and automatic, allowing the SPI time for only a few seconds. We propose a fast clustering algorithm that reuses existing models to avoid retraining and fine tune in the inference phase. We evaluate our approach using 3-month data collected from production lines. We show that we can reduce 81.28% of false alarms. This can translate to annual savings of $11.3 million.

Large-scale datacenters have been widely used to host cloud services, which are typically allocated to different virtual machines (VMs) through resource multiplexing across shared physical servers. Although recent studies have primarily focused on harnessing live migration of VMs to achieve load balancing and power saving among different servers, there has been little attention on the incurred performance interference and cost on both source and destination servers during and after such VM migration. To avoid potential violations of service-level-agreement (SLA) demanded by cloud applications, this paper proposes iAware, a lightweight interference-aware VM live migration strategy. It empirically captures the essential relationships between VM performance interference and key factors that are practically accessible through realistic experiments of benchmark workloads on a Xen virtualized cluster platform. iAware jointly estimates and minimizes both migration and co-location interference among VMs, by designing a simple multi-resource demand-supply model. Extensive experiments and complementary large-scale simulations are conducted to validate the performance gain and runtime overhead of iAware in terms of I/O and network throughput, CPU consumption, and scalability, compared to the traditional interference-unaware VM migration approaches. Moreover, we demonstrate that iAware is flexible enough to cooperate with existing VM scheduling or consolidation policies in a complementary manner, such that the load balancing or power saving can still be achieved without sacrificing performance.

Accurate segmentation of cracked body from three-dimensional (3D) industrial Computed Tomography (CT) images is an important step in the process of crack measurement and automatic recognition. In this paper we present a fast method for the segmentation of cracked body. The improved algorithm incorporates wavelet transform and Chan and Vese (C-V) model as key components. The 3D wavelet transform is applied for detecting rough edges. Then region growing is used to find a suitable region which contains cracked body. Based on the resulting volume data, 3D C-V model is used to capture the edges of cracked body. The improved method can locate rough regions by using wavelet modulus maxima, which not only reduces the amount of data C-V model processed, but also provides initial contour surface that can accelerate the convergence speed of C-V model. Experimental results illustrate our method can accurately detect the cracked surface, as well as give computational savings of segmentation which satisfy the demand of defects detection of industrial CT.