Deep neural network-based detection of counterfeit products from smartphone images

Counterfeit products such as drugs and vaccines as well as luxury items such as high-fashion handbags, watches, jewelry, garments, and cosmetics, represent significant direct losses of revenue to legitimate manufacturers and vendors, as well as indirect costs to societies at large. We present the world's first purely computer-vision-based system to combat such counterfeiting-one that does not require special security tags or other alterations to the products or modifications to supply chain tracking. Our deep neural network system shows high accuracy on branded garments from our first manufacturer tested (99.71% after 3.06% rejections) using images captured under natural, weakly controlled conditions, such as in retail stores, customs checkpoints, warehouses, and outdoors. Our system, suitably transfer trained on a small number of fake and genuine articles, should find application in additional product categories as well, for example fashion accessories, perfume boxes, medicines, and more.

Preserving Empirical Probabilities in BERT for Small-sample Clinical Entity Recognition

Named Entity Recognition (NER) encounters the challenge of unbalanced labels, where certain entity types are overrepresented while others are underrepresented in real-world datasets. This imbalance can lead to biased models that perform poorly on minority entity classes, impeding accurate and equitable entity recognition. This paper explores the effects of unbalanced entity labels of the BERT-based pre-trained model. We analyze the different mechanisms of loss calculation and loss propagation for the task of token classification on randomized datasets. Then we propose ways to improve the token classification for the highly imbalanced task of clinical entity recognition.

Joint Stream: Malignant Region Learning for Breast Cancer Diagnosis

Early diagnosis of breast cancer (BC) significantly contributes to reducing the mortality rate worldwide. The detection of different factors and biomarkers such as Estrogen receptor (ER), Progesterone receptor (PR), Human epidermal growth factor receptor 2 (HER2) gene, Histological grade (HG), Auxiliary lymph node (ALN) status, and Molecular subtype (MS) can play a significant role in improved BC diagnosis. However, the existing methods predict only a single factor which makes them less suitable to use in diagnosis and designing a strategy for treatment. In this paper, we propose to classify the six essential indicating factors (ER, PR, HER2, ALN, HG, MS) for early BC diagnosis using H\&E stained WSI's. To precisely capture local neighboring relationships, we use spatial and frequency domain information from the large patch size of WSI's malignant regions. Furthermore, to cater the variable number of regions of interest sizes and give due attention to each region, we propose a malignant region learning attention network. Our experimental results demonstrate that combining spatial and frequency information using the malignant region learning module significantly improves multi-factor and single-factor classification performance on publicly available datasets.

A Large-scale Multi Domain Leukemia Dataset for the White Blood Cells Detection with Morphological Attributes for Explainability

Earlier diagnosis of Leukemia can save thousands of lives annually. The prognosis of leukemia is challenging without the morphological information of White Blood Cells (WBC) and relies on the accessibility of expensive microscopes and the availability of hematologists to analyze Peripheral Blood Samples (PBS). Deep Learning based methods can be employed to assist hematologists. However, these algorithms require a large amount of labeled data, which is not readily available. To overcome this limitation, we have acquired a realistic, generalized, and large dataset. To collect this comprehensive dataset for real-world applications, two microscopes from two different cost spectrums (high-cost HCM and low-cost LCM) are used for dataset capturing at three magnifications (100x, 40x, 10x) through different sensors (high-end camera for HCM, middle-level camera for LCM and mobile-phone camera for both). The high-sensor camera is 47 times more expensive than the middle-level camera and HCM is 17 times more expensive than LCM. In this collection, using HCM at high resolution (100x), experienced hematologists annotated 10.3k WBC types (14) and artifacts, having 55k morphological labels (Cell Size, Nuclear Chromatin, Nuclear Shape, etc.) from 2.4k images of several PBS leukemia patients. Later on, these annotations are transferred to other 2 magnifications of HCM, and 3 magnifications of LCM, and on each camera captured images. Along with the LeukemiaAttri dataset, we provide baselines over multiple object detectors and Unsupervised Domain Adaptation (UDA) strategies, along with morphological information-based attribute prediction. The dataset will be publicly available after publication to facilitate the research in this direction.

DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

The creation of large, diverse, high-quality robot manipulation datasets is an important stepping stone on the path toward more capable and robust robotic manipulation policies. However, creating such datasets is challenging: collecting robot manipulation data in diverse environments poses logistical and safety challenges and requires substantial investments in hardware and human labour. As a result, even the most general robot manipulation policies today are mostly trained on data collected in a small number of environments with limited scene and task diversity. In this work, we introduce DROID (Distributed Robot Interaction Dataset), a diverse robot manipulation dataset with 76k demonstration trajectories or 350 hours of interaction data, collected across 564 scenes and 84 tasks by 50 data collectors in North America, Asia, and Europe over the course of 12 months. We demonstrate that training with DROID leads to policies with higher performance and improved generalization ability. We open source the full dataset, policy learning code, and a detailed guide for reproducing our robot hardware setup.

A survey of the Vision Transformers and its CNN-Transformer based Variants

Vision transformers have recently become popular as a possible alternative to convolutional neural networks (CNNs) for a variety of computer vision applications. These vision transformers due to their ability to focus on global relationships in images have large capacity, but may result in poor generalization as compared to CNNs. Very recently, the hybridization of convolution and self-attention mechanisms in vision transformers is gaining popularity due to their ability of exploiting both local and global image representations. These CNN-Transformer architectures also known as hybrid vision transformers have shown remarkable results for vision applications. Recently, due to the rapidly growing number of these hybrid vision transformers, there is a need for a taxonomy and explanation of these architectures. This survey presents a taxonomy of the recent vision transformer architectures, and more specifically that of the hybrid vision transformers. Additionally, the key features of each architecture such as the attention mechanisms, positional embeddings, multi-scale processing, and convolution are also discussed. This survey highlights the potential of hybrid vision transformers to achieve outstanding performance on a variety of computer vision tasks. Moreover, it also points towards the future directions of this rapidly evolving field.

Classifying Text-Based Conspiracy Tweets related to COVID-19 using Contextualized Word Embeddings

The FakeNews task in MediaEval 2022 investigates the challenge of finding accurate and high-performance models for the classification of conspiracy tweets related to COVID-19. In this paper, we used BERT, ELMO, and their combination for feature extraction and RandomForest as classifier. The results show that ELMO performs slightly better than BERT, however their combination at feature level reduces the performance.

Real-time Speech Emotion Recognition Based on Syllable-Level Feature Extraction

Speech emotion recognition systems have high prediction latency because of the high computational requirements for deep learning models and low generalizability mainly because of the poor reliability of emotional measurements across multiple corpora. To solve these problems, we present a speech emotion recognition system based on a reductionist approach of decomposing and analyzing syllable-level features. Mel-spectrogram of an audio stream is decomposed into syllable-level components, which are then analyzed to extract statistical features. The proposed method uses formant attention, noise-gate filtering, and rolling normalization contexts to increase feature processing speed and tolerance to adversity. A set of syllable-level formant features is extracted and fed into a single hidden layer neural network that makes predictions for each syllable as opposed to the conventional approach of using a sophisticated deep learner to make sentence-wide predictions. The syllable level predictions help to achieve the real-time latency and lower the aggregated error in utterance level cross-corpus predictions. The experiments on IEMOCAP (IE), MSP-Improv (MI), and RAVDESS (RA) databases show that the method archives real-time latency while predicting with state-of-the-art cross-corpus unweighted accuracy of 47.6% for IE to MI and 56.2% for MI to IE.

DeepCFD: Efficient Steady-State Laminar Flow Approximation with Deep Convolutional Neural Networks

Computational Fluid Dynamics (CFD) simulation by the numerical solution of the Navier-Stokes equations is an essential tool in a wide range of applications from engineering design to climate modeling. However, the computational cost and memory demand required by CFD codes may become very high for flows of practical interest, such as in aerodynamic shape optimization. This expense is associated with the complexity of the fluid flow governing equations, which include non-linear partial derivative terms that are of difficult solution, leading to long computational times and limiting the number of hypotheses that can be tested during the process of iterative design. Therefore, we propose DeepCFD: a convolutional neural network (CNN) based model that efficiently approximates solutions for the problem of non-uniform steady laminar flows. The proposed model is able to learn complete solutions of the Navier-Stokes equations, for both velocity and pressure fields, directly from ground-truth data generated using a state-of-the-art CFD code. Using DeepCFD, we found a speedup of up to 3 orders of magnitude compared to the standard CFD approach at a cost of low error rates.

Brain MRI Segmentation using Rule-Based Hybrid Approach

Medical image segmentation being a substantial component of image processing plays a significant role to analyze gross anatomy, to locate an infirmity and to plan the surgical procedures. Segmentation of brain Magnetic Resonance Imaging (MRI) is of considerable importance for the accurate diagnosis. However, precise and accurate segmentation of brain MRI is a challenging task. Here, we present an efficient framework for segmentation of brain MR images. For this purpose, Gabor transform method is used to compute features of brain MRI. Then, these features are classified by using four different classifiers i.e., Incremental Supervised Neural Network (ISNN), K-Nearest Neighbor (KNN), Probabilistic Neural Network (PNN), and Support Vector Machine (SVM). Performance of these classifiers is investigated over different images of brain MRI and the variation in the performance of these classifiers is observed for different brain tissues. Thus, we proposed a rule-based hybrid approach to segment brain MRI. Experimental results show that the performance of these classifiers varies over each tissue MRI and the proposed rule-based hybrid approach exhibits better segmentation of brain MRI tissues.