Abstract: Disclosed herein is a system and method for improving the accuracy of an object detector when trained with a dataset having a significant number of missing annotations. The method uses a novel Background Recalibration Loss (BRL) which adjusts the gradient direction according to its own activation to reduce the adverse effect of error signals by replacing the negative branch of the focal loss with a mirror of the positive branch when the activation is below a confusion threshold.

Abstract: A method of training a deep neural network, such as would be used in facial recognition, includes training the deep neural network to normalize feature vectors to a learned value representing a radius of a multi-dimensional hypersphere using a convex augmentation of the primary loss function.

Abstract: An automated inventory monitoring system includes an image capture module able to create an image of an aisle of a retail store. Product images and shelf label and peg label images are identified in the image and products are associated with product labels based on the positioning of the products with respect to the labels. Based on the association between labels and products, out-of-stock products are detected and reported to the retail store.

Abstract: Disclosed herein is a system and method of identifying misplaced products on a retail shelf using a feature extractor trained to extract features from images of products on the shelf and output identifying information regarding the product in the product image. The extracted features are compared to extracted features in a product library and a best fit is obtained. A misplaced product is identified if the identifying information produced by the feature extractor fails to match the identifying information associated with the best fit features from the product library.

Abstract: A method for increasing the confidence of a match between a test image and an image stored in a library database in which features are extracted from the test image and compared to features stored in the image database. If a match is determined, one or more transformations are performed on the test image to generate pose-altered images from which features are extracted and matched with pose-altered images in the database. The scores for the subsequent matchings can be aggregated to determine an overall probability of a match between the test image in an image in the library database.

Abstract: Disclosed herein is an improved few-shot detector which utilizes a dynamic semantic network which takes as input a language feature and generates trainable parameters for a visual network. The visual network takes a visual feature as input and generates a classification and localization of an object.

Abstract: Disclosed herein is a methodology for refining novel-class features in a few-shot learning scenario by fine-tuning the feature extractor by reducing both class-agnostic biases and class-specific biases. A distribution calibration module is used to reduce the class-agnostic bias by normalizing the overall feature distribution for novel classes and further reshaping the feature manifold for fast adaptation during fine-tuning. Selected sampling is used to reduce class-specific bias by augmenting more data for better estimation.

Abstract: Disclosed herein is a system and method for generating complex, concave polygonal bonding boxes which tightly cover the most representative faces of retail products having arbitrary poses. The polygonal bounding boxes do not include unnecessary background information or miss parts of the objects, as would the axis-aligned or rotated bounding boxes produced by prior art detectors. A simple projection transformation can correct the pose of products for downstream tasks.

Abstract: Disclosed herein is a system and method for novel neural architecture search using a random graph network backbone to facilitate the creation of an efficient network structure. The method utilizes reinforcement learning algorithms to build a complex relationship between intra-connections (i.e., links between blocks in a random graph network) and extra-connections (i.e., links among blocks across the random graphs network) for discovering an efficient random neural architecture.

Abstract: A method of training a deep neural network, such as would be used in facial recognition, includes training the deep neural network to normalize feature vectors to a learned value representing the radius of a multi-dimensional hypersphere using a convex augmentation of the primary loss function.

Abstract: Disclosed herein is a method to disentangle linear-encoded facial semantics from facial images without external supervision. The method uses linear regression and sparse representation learning concepts to make the disentangled latent representations easily interpreted and manipulated. Generated facial images are decomposed into multiple semantic features and latent representations are extracted to capture interpretable facial semantics. The semantic features may be manipulated to synthesize photorealistic facial images by sampling along vectors representing the semantic features, thereby changing the associate semantics.

Abstract: Disclosed herein is an improved method for identifying images containing objects-of-interest from a large set of images. The method comprises mixing two or more of the images to create a grouped image and exposing the grouped image to an object detector trained on grouped images to make an initial determination that the grouped image was formed from at least one image containing an object-of-interest. The images which formed the grouped image are then exposed to regular object detectors to determine a classification of the object-of-interest.

Abstract: During training of deep neural networks, a Copernican loss (LC) is designed to augment a primary loss function, for example, a standard Softmax loss, to explicitly minimize intra-class variation and simultaneously maximize inter-class variation. Copernican loss operates using the cosine distance and thereby affects angles leading to a cosine embedding, which removes the disconnect between training and testing.

Abstract: Disclosed herein is a system and method for matching products detected in an image of a shelf. The match or non-match of the products is then used to make a determination that the products are correctly positioned on the shelf of if the positioning of the products represents a plug or spread situation.

Abstract: An automated inventory monitoring system includes an image capture module able to create an image of an aisle of a retail store. Images of products labels are identified in the image and classified as shelf labels or peg labels. For shelf labels, an area of the shelf is defined and associated with the shelf label. Images of products are identified in the image and products on the shelf within an area associated with a shelf label are associated with the shelf label. Products located below a peg label are associated with the peg label. Based on the association between labels and products, out-of-stock products, plugs and spread may be detected and reported to the staff of the retail store.

Abstract: Disclosed herein is a system and method for data augmentation for general object recognition which preserves the class identity of the augmented data. The system comprises an image recognition network an image generation network that take as input ground truth images and classes respectively and which generates a predicted class and an augmented image. A discriminator evaluates the predicted class and augmented image and provides feedback to the image recognition network and the image generation network.

Abstract: Disclosed herein is a system and method for the automatic detection of persons engaged in the open carry of firearms at a venue. The system and method comprise strategically placed cameras at the venue which are connected to edge devices which extract frames from video generated by the cameras. The video frames are sent to a server for analysis by an AI/ML model trained to detect firearms and, specifically, to detect persons carrying firearms. If a person wielding a firearm is detected in any image, an alert is raised and local authorities are automatically contacted. The system is designed to run continuously such as to be able to quickly detect a person in a venue carrying a firearm.

Abstract: Disclosed herein is a system and method for reducing false positives in object detection frameworks. A human form view of objects detected by the object detection framework and indicates the object is a false positive. When an indication of a false positive been received, a feature representation of displayed object stored in the gallery. During an inference or testing phase, the gallery is searched for a feature representation matching the feature representation of the detected objects, and, if a match is found, the detected object is deemed to be a false positive and is not displayed to the user.

Abstract: Disclosed herein is a system and method of identifying misplaced products on a retail shelf using a feature extractor trained to extract features from images of products on the shelf and output identifying information regarding the product in the product image. The extracted features are compared to extracted features in a product library and a best fit is obtained. A misplaced product is identified if the identifying information produced by the feature extractor fails to match the identifying information associated with the best fit features from the product library.

Abstract: Disclosed here are various techniques for improving the testing and training of datasets comprising sequences of skeletal representations performing various actions. The dataset can be denoised by applying various techniques to determine noisy frames within each sequence and eliminating the sequences from the dataset when the number of noisy frames in the sequence is too large. In addition, the dataset may be augmented by various data augmentation techniques to manipulate the skeletal representations, after denoising.