ISTR: Mask-Embedding-Based Instance Segmentation Transformer.

Transformer-based instance-level recognition has attracted increasing research attention recently due to the superior performance. However, although attempts have been made to encode masks as embeddings into Transformer-based frameworks, how to combine mask embeddings and spatial information for a transformer-based approach is still not fully explored. In this paper, we revisit the design of mask-embedding-based pipelines and propose an Instance Segmentation TRansformer (ISTR) with Mask Meta-Embeddings (MME), leveraging the strengths of transformer models in encoding embedding information and incorporating spatial information from mask embeddings. ISTR incorporates a recurrent refining head that consists of a Dynamic Box Predictor (DBP), a Mask Information Generator (MIG), and a Mask Meta-Decoder (MMD). To improve the quality of mask embeddings, MME interprets the mask encoding-decoding processes as a mutual information maximization problem, which unifies the objective functions of different decoding schemes such as Principal Component Analysis (PCA) and Discrete Cosine Transform (DCT) with a meta-formulation. Under the meta-formulation, a learnable Spatial Mask Tuner (SMT) is further proposed, which fuses the spatial and embedding information produced from MIG and can significantly boost the segmentation performance. The resulting varieties, i.e., ISTR-PCA, ISTR-DCT, and ISTR-SMT, demonstrate the effectiveness and efficiency of incorporating mask embeddings with the query-based instance segmentation pipelines. On the COCO dataset, ISTR surpasses all predominant mask-embedding-based models by a large margin, and achieves competitive performance compared to concurrent state-of-the-art models. On the Cityscapes dataset, ISTR also outperforms several strong baselines. Our code has been made available at: https://github.com/hujiecpp/ISTR.

Backdoor Attack against Face Sketch Synthesis.

Deep neural networks (DNNs) are easily exposed to backdoor threats when training with poisoned training samples. Models using backdoor attack have normal performance for benign samples, and possess poor performance for poisoned samples manipulated with pre-defined trigger patterns. Currently, research on backdoor attacks focuses on image classification and object detection. In this article, we investigated backdoor attacks in facial sketch synthesis, which can be beneficial for many applications, such as animation production and assisting police in searching for suspects. Specifically, we propose a simple yet effective poison-only backdoor attack suitable for generation tasks. We demonstrate that when the backdoor is integrated into the target model via our attack, it can mislead the model to synthesize unacceptable sketches of any photos stamped with the trigger patterns. Extensive experiments are executed on the benchmark datasets. Specifically, the light strokes devised by our backdoor attack strategy can significantly decrease the perceptual quality. However, the FSIM score of light strokes is 68.21% on the CUFS dataset and the FSIM scores of pseudo-sketches generated by FCN, cGAN, and MDAL are 69.35%, 71.53%, and 72.75%, respectively. There is no big difference, which proves the effectiveness of the proposed backdoor attack method.

Joint segmentation and detection of COVID-19 via a sequential region generation network.

The fast pandemics of coronavirus disease (COVID-19) has led to a devastating influence on global public health. In order to treat the disease, medical imaging emerges as a useful tool for diagnosis. However, the computed tomography (CT) diagnosis of COVID-19 requires experts' extensive clinical experience. Therefore, it is essential to achieve rapid and accurate segmentation and detection of COVID-19. This paper proposes a simple yet efficient and general-purpose network, called Sequential Region Generation Network (SRGNet), to jointly detect and segment the lesion areas of COVID-19. SRGNet can make full use of the supervised segmentation information and then outputs multi-scale segmentation predictions. Through this, high-quality lesion-areas suggestions can be generated on the predicted segmentation maps, reducing the diagnosis cost. Simultaneously, the detection results conversely refine the segmentation map by a post-processing procedure, which significantly improves the segmentation accuracy. The superiorities of our SRGNet over the state-of-the-art methods are validated through extensive experiments on the built COVID-19 database.

Shale Brittleness Index Based on the Energy Evolution Theory and Evaluation with Logging Data: A Case Study of the Guandong Block.

Shale brittleness is a key index that indicates the shale fracability, provides a basis for selecting wells and intervals to be fractured, and guarantees the good fracturing effect. The available models are not accurate in evaluating the shale brittleness when considering the confining pressure, and it is necessary to establish a new shale brittleness model under the geo-stress. In this study, the variation of elastic energy, fracture energy, and residual elastic energy in the whole process of rock compression and failure is analyzed based on the stress-strain curve in the experiments, and a shale brittleness index reflecting the energy evolution characteristics during rock failure under different confining pressures is established; a method of directly evaluating the shale brittleness with logging data by combining the rock mechanic experiment results with logging interpretation results is proposed. The calculation results show that the brittleness decreases as the confining pressure increases. When the confining pressure of the Kong-2 member shale of the Guandong block is less than 25 MPa, the brittleness index decreases significantly as the confining pressure increases, and when the confining pressure is greater than 25 MPa, the brittleness index decreases slightly. It is shown that the shale brittleness index is more sensitive to the confining pressure within a certain range and less sensitive to the confining pressure above a certain value.

Face Sketch Synthesis by Multidomain Adversarial Learning.

Given a training set of face photo-sketch pairs, face sketch synthesis targets at learning a mapping from the photo domain to the sketch domain. Despite the exciting progresses made in the literature, it retains as an open problem to synthesize high-quality sketches against blurs and deformations. Recent advances in generative adversarial training provide a new insight into face sketch synthesis, from which perspective the existing synthesis pipelines can be fundamentally revisited. In this paper, we present a novel face sketch synthesis method by multidomain adversarial learning (termed MDAL), which overcomes the defects of blurs and deformations toward high-quality synthesis. The principle of our scheme relies on the concept of "interpretation through synthesis." In particular, we first interpret face photographs in the photodomain and face sketches in the sketch domain by reconstructing themselves respectively via adversarial learning. We define the intermediate products in the reconstruction process as latent variables, which form a latent domain. Second, via adversarial learning, we make the distributions of latent variables being indistinguishable between the reconstruction process of the face photograph and that of the face sketch. Finally, given an input face photograph, the latent variable obtained by reconstructing this face photograph is applied for synthesizing the corresponding sketch. Quantitative comparisons to the state-of-the-art methods demonstrate the superiority of the proposed MDAL method.