BTAN: Lightweight Super-Resolution Network with Target Transform and Attention

In the realm of single-image super-resolution (SISR), generating high-resolution (HR) images from a low-resolution (LR) input remains a challenging task. While deep neural networks have shown promising results, they often require significant computational resources. To address this issue, we introduce a lightweight convolutional neural network, named BTAN, that leverages the connection between LR and HR images to enhance performance without increasing the number of parameters. Our approach includes a target transform module that adjusts output features to match the target distribution and improve reconstruction quality, as well as a spatial and channel-wise attention module that modulates feature maps based on visual attention at multiple layers. We demonstrate the effectiveness of our approach on four benchmark datasets, showcasing superior accuracy, efficiency, and visual quality when compared to state-of-the-art methods.    

Study on a class of Schrödinger elliptic system involving a nonlinear operator

This paper considers a class of Schrödinger elliptic system involving a nonlinear operator. Firstly, under the simple condition on and ', we prove the existence of the entire positive bounded radial solutions. Secondly, by using the iterative technique and the method of contradiction, we prove the existence and nonexistence of the entire positive blow-up radial solutions. Our results extend the previous existence and nonexistence results for both the single equation and systems. In the end, we give two examples to illustrate our results

Partition Function Expansion on Region-Graphs and Message-Passing Equations

Disordered and frustrated graphical systems are ubiquitous in physics, biology, and information science. For models on complete graphs or random graphs, deep understanding has been achieved through the mean-field replica and cavity methods. But finite-dimensional `real' systems persist to be very challenging because of the abundance of short loops and strong local correlations. A statistical mechanics theory is constructed in this paper for finite-dimensional models based on the mathematical framework of partition function expansion and the concept of region-graphs. Rigorous expressions for the free energy and grand free energy are derived. Message-passing equations on the region-graph, such as belief-propagation and survey-propagation, are also derived rigorously.Comment: 10 pages including two figures. New theoretical and numerical results added. Will be published by JSTAT as a lette

Curative effect and technical key points of laparoscopic surgery for choledochal cysts in children

ObjectiveThe purpose of this study was to investigate the curative effect of and experience with laparoscopic surgery for congenital choledochal cysts in children.MethodsThis is a retrospective analysis of 33 children diagnosed with congenital choledochal cyst in the pediatric surgery department of the Affiliated Hospital of Southwest Medical University between January 2019 and December 2021. The cohort included 8 males and 25 females aged 0.25–13.7 years (median age, 3.2 years), including 21 cases of type I and 12 cases of type IV choledochal cyst (Todani classification). Laparoscopic choledochal cyst resection and hepaticojejunostomy with Roux-en-Y anastomosis were performed in all the patients.ResultsLaparoscopy without transit opening was successfully performed in the 33 cases. The duration of the procedure was 235–460 min (mean ± SD, 316 ± 61 min), and intraoperative blood loss volume was 15–40 ml (23 ± 7.6 ml). Postoperative hospital stay was 7–14 days (9 ± 1.8 days). Postoperative biliary fistula and pancreatitis occurred in two cases each, and all four patients were successfully treated with conservative treatment. No anastomotic stenosis, delayed bleeding, cholangitis, intestinal obstruction, or other complications occurred. All the children were followed up for 2–36 months (median period, 17.2 months). The clinical symptoms disappeared, and no obvious hepatic dysfunction was found on abdominal color ultrasound and liver function examination.ConclusionLaparoscopic surgery for congenital choledochal cyst in children is safe and effective, as it is a minimally invasive surgery that is associated with a low degree of trauma and bleeding, rapid postoperative recovery, and satisfactory aesthetic appearance

SemiReward: A General Reward Model for Semi-supervised Learning

Semi-supervised learning (SSL) has witnessed great progress with various improvements in the self-training framework with pseudo labeling. The main challenge is how to distinguish high-quality pseudo labels against the confirmation bias. However, existing pseudo-label selection strategies are limited to pre-defined schemes or complex hand-crafted policies specially designed for classification, failing to achieve high-quality labels, fast convergence, and task versatility simultaneously. To these ends, we propose a Semi-supervised Reward framework (SemiReward) that predicts reward scores to evaluate and filter out high-quality pseudo labels, which is pluggable to mainstream SSL methods in wide task types and scenarios. To mitigate confirmation bias, SemiReward is trained online in two stages with a generator model and subsampling strategy. With classification and regression tasks on 13 standard SSL benchmarks across three modalities, extensive experiments verify that SemiReward achieves significant performance gains and faster convergence speeds upon Pseudo Label, FlexMatch, and Free/SoftMatch. Code and models are available at https://github.com/Westlake-AI/SemiReward.Comment: ICLR 2024 Camera Ready. Preprint V2 (25 pages) with the source code at https://github.com/Westlake-AI/SemiRewar

Research on a lightweight electronic component detection method based on knowledge distillation

As an essential part of electronic component assembly, it is crucial to rapidly and accurately detect electronic components. Therefore, a lightweight electronic component detection method based on knowledge distillation is proposed in this study. First, a lightweight student model was constructed. Then, we consider issues like the teacher and student's differing expressions. A knowledge distillation method based on the combination of feature and channel is proposed to learn the teacher's rich class-related and inter-class difference features. Finally, comparative experiments were analyzed for the dataset. The results show that the student model Params (13.32 M) are reduced by 55%, and FLOPs (28.7 GMac) are reduced by 35% compared to the teacher model. The knowledge distillation method based on the combination of feature and channel improves the student model's mAP by 3.91% and 1.13% on the Pascal VOC and electronic components detection datasets, respectively. As a result of the knowledge distillation, the constructed student model strikes a superior balance between model precision and complexity, allowing for fast and accurate detection of electronic components with a detection precision (mAP) of 97.81% and a speed of 79 FPS

OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning

Spatio-temporal predictive learning is a learning paradigm that enables models to learn spatial and temporal patterns by predicting future frames from given past frames in an unsupervised manner. Despite remarkable progress in recent years, a lack of systematic understanding persists due to the diverse settings, complex implementation, and difficult reproducibility. Without standardization, comparisons can be unfair and insights inconclusive. To address this dilemma, we propose OpenSTL, a comprehensive benchmark for spatio-temporal predictive learning that categorizes prevalent approaches into recurrent-based and recurrent-free models. OpenSTL provides a modular and extensible framework implementing various state-of-the-art methods. We conduct standard evaluations on datasets across various domains, including synthetic moving object trajectory, human motion, driving scenes, traffic flow and weather forecasting. Based on our observations, we provide a detailed analysis of how model architecture and dataset properties affect spatio-temporal predictive learning performance. Surprisingly, we find that recurrent-free models achieve a good balance between efficiency and performance than recurrent models. Thus, we further extend the common MetaFormers to boost recurrent-free spatial-temporal predictive learning. We open-source the code and models at https://github.com/chengtan9907/OpenSTL.Comment: Accepted by NeurIPS 2023. 33 pages, 17 figures, 19 tables. Under review. For more details, please refer to https://github.com/chengtan9907/OpenST

Efficient Multi-order Gated Aggregation Network

Since the recent success of Vision Transformers (ViTs), explorations toward transformer-style architectures have triggered the resurgence of modern ConvNets. In this work, we explore the representation ability of DNNs through the lens of interaction complexities. We empirically show that interaction complexity is an overlooked but essential indicator for visual recognition. Accordingly, a new family of efficient ConvNets, named MogaNet, is presented to pursue informative context mining in pure ConvNet-based models, with preferable complexity-performance trade-offs. In MogaNet, interactions across multiple complexities are facilitated and contextualized by leveraging two specially designed aggregation blocks in both spatial and channel interaction spaces. Extensive studies are conducted on ImageNet classification, COCO object detection, and ADE20K semantic segmentation tasks. The results demonstrate that our MogaNet establishes new state-of-the-art over other popular methods in mainstream scenarios and all model scales. Typically, the lightweight MogaNet-T achieves 80.0\% top-1 accuracy with only 1.44G FLOPs using a refined training setup on ImageNet-1K, surpassing ParC-Net-S by 1.4\% accuracy but saving 59\% (2.04G) FLOPs.Comment: Preprint with 14 pages of main body and 5 pages of appendi