Prototypical Residual Networks for Anomaly Detection and Localization

Anomaly detection and localization are widely used in industrial manufacturing for its efficiency and effectiveness. Anomalies are rare and hard to collect and supervised models easily over-fit to these seen anomalies with a handful of abnormal samples, producing unsatisfactory performance. On the other hand, anomalies are typically subtle, hard to discern, and of various appearance, making it difficult to detect anomalies and let alone locate anomalous regions. To address these issues, we propose a framework called Prototypical Residual Network (PRN), which learns feature residuals of varying scales and sizes between anomalous and normal patterns to accurately reconstruct the segmentation maps of anomalous regions. PRN mainly consists of two parts: multi-scale prototypes that explicitly represent the residual features of anomalies to normal patterns; a multisize self-attention mechanism that enables variable-sized anomalous feature learning. Besides, we present a variety of anomaly generation strategies that consider both seen and unseen appearance variance to enlarge and diversify anomalies. Extensive experiments on the challenging and widely used MVTec AD benchmark show that PRN outperforms current state-of-the-art unsupervised and supervised methods. We further report SOTA results on three additional datasets to demonstrate the effectiveness and generalizability of PRN.Comment: Accepted by CVPR 202

MRN: Multiplexed Routing Network for Incremental Multilingual Text Recognition

Multilingual text recognition (MLTR) systems typically focus on a fixed set of languages, which makes it difficult to handle newly added languages or adapt to ever-changing data distribution. In this paper, we propose the Incremental MLTR (IMLTR) task in the context of incremental learning (IL), where different languages are introduced in batches. IMLTR is particularly challenging due to rehearsal-imbalance, which refers to the uneven distribution of sample characters in the rehearsal set, used to retain a small amount of old data as past memories. To address this issue, we propose a Multiplexed Routing Network (MRN). MRN trains a recognizer for each language that is currently seen. Subsequently, a language domain predictor is learned based on the rehearsal set to weigh the recognizers. Since the recognizers are derived from the original data, MRN effectively reduces the reliance on older data and better fights against catastrophic forgetting, the core issue in IL. We extensively evaluate MRN on MLT17 and MLT19 datasets. It outperforms existing general-purpose IL methods by large margins, with average accuracy improvements ranging from 10.3% to 35.8% under different settings. Code is available at https://github.com/simplify23/MRN.Comment: Accepted by ICCV 202

Self-distillation Augmented Masked Autoencoders for Histopathological Image Classification

Self-supervised learning (SSL) has drawn increasing attention in pathological image analysis in recent years. However, the prevalent contrastive SSL is suboptimal in feature representation under this scenario due to the homogeneous visual appearance. Alternatively, masked autoencoders (MAE) build SSL from a generative paradigm. They are more friendly to pathological image modeling. In this paper, we firstly introduce MAE to pathological image analysis. A novel SD-MAE model is proposed to enable a self-distillation augmented SSL on top of the raw MAE. Besides the reconstruction loss on masked image patches, SD-MAE further imposes the self-distillation loss on visible patches. It guides the encoder to perceive high-level semantics that benefit downstream tasks. We apply SD-MAE to the image classification task on two pathological and one natural image datasets. Experiments demonstrate that SD-MAE performs highly competitive when compared with leading contrastive SSL methods. The results, which are pre-trained using a moderate size of pathological images, are also comparable to the method pre-trained with two orders of magnitude more images. Our code will be released soon

Context Perception Parallel Decoder for Scene Text Recognition

Scene text recognition (STR) methods have struggled to attain high accuracy and fast inference speed. Autoregressive (AR)-based STR model uses the previously recognized characters to decode the next character iteratively. It shows superiority in terms of accuracy. However, the inference speed is slow also due to this iteration. Alternatively, parallel decoding (PD)-based STR model infers all the characters in a single decoding pass. It has advantages in terms of inference speed but worse accuracy, as it is difficult to build a robust recognition context in such a pass. In this paper, we first present an empirical study of AR decoding in STR. In addition to constructing a new AR model with the top accuracy, we find out that the success of AR decoder lies also in providing guidance on visual context perception rather than language modeling as claimed in existing studies. As a consequence, we propose Context Perception Parallel Decoder (CPPD) to decode the character sequence in a single PD pass. CPPD devises a character counting module and a character ordering module. Given a text instance, the former infers the occurrence count of each character, while the latter deduces the character reading order and placeholders. Together with the character prediction task, they construct a context that robustly tells what the character sequence is and where the characters appear, well mimicking the context conveyed by AR decoding. Experiments on both English and Chinese benchmarks demonstrate that CPPD models achieve highly competitive accuracy. Moreover, they run approximately 7x faster than their AR counterparts, and are also among the fastest recognizers. The code will be released soon

Deficiency of Brummer Impaires Lipid Mobilization and JH-Mediated Vitellogenesis in the Brown Planthopper, Nilaparvata lugens

Provisioning of sufficient lipids and vitellogenin to the oocytes is an indispensable process for fecundity of oviparous insects. Acute mobilization of lipid reserves in insects is controlled by the Brummer (Bmm), an orthologous of human adipose triglyceride lipase (ATGL). To investigate the functional roles of brummer-mediated lipolysis in the fecundity of the brown planthopper, Nilaparvata lugens, RNA interference (RNAi) analyses were performed with double-stranded RNA (dsRNA) against NlBmm in adult females. Knockdown of NlBmm expression resulted in obesity and blocked lipid mobilization in the fat body. In addition, NlBmm silencing led to retarded ovarian development with immature eggs and less ovarioles, decreased number of laid eggs, prolonged preoviposition period and egg duration. Furthermore, severe reductions of vitellogenin and its receptor abundance were observed upon NlBmm knockdown. The transcript levels of NlJHE (juvenile hormone esterase) which degrades JH were up-regulated, whereas the expression levels of JH receptors NlMet (Methoprene-tolerant) and NlTai (Taiman) and their downstream transcription factors NlKr-h1 (Krüppel-homolog 1) and NlBr (Broad-Complex) were down-regulated after suppression of NlBmm. JH-deficient females exhibited impaired vitellogenin expression, whereas JH exposure stimulated vitellogenin biosynthesis. Moreover, JH topical application partially rescued the decrease in vitellogenin expression in the NlBmm-deficient females. These results demonstrate that brummer-mediated lipolytic system is essential for lipid mobilization and energy homeostasis during reproduction in N. lugens. In addition to the classical view of brummer as a direct lipase with lipolysis activity, we propose here that brummer-mediated lipolysis works through JH signaling pathway to activate vitellogenesis and oocyte maturation that in turn regulates female fecundity

An Algorithm and Implementation Based on an Agricultural EOQ Model

With the improvement of living quality, the agricultural supermarket gradually take the place of the farmers market as the trend. But the agricultural supermarkets’ inappropriate inventory strategies are wasteful and inefficient. So this paper will put forward an inventory strategy for the agricultural supermarkets to lead the conductor decides when and how much to shelve the product. This strategy has significant meaning that it can reduce the loss and get more profit. The research methods are based on the inventory theory and the EOQ model, but the authors add multiple cycles’ theory to them because of the agricultural products’ decreasing characteristics. The research procedures are shown as follows. First, the authors do research in the agricultural supermarket to find their real conduction, and then put forward the new strategy in this paper. Second, the authors found out the model. At last, the authors search the specialty agriculture document to find the data such as the loss rate and the fresh parameters, and solve it out by MATLAB. The numerical result proves that the strategy is better than the real conduction in agricultural supermarket, and it also proves the feasibility