Abstract: Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning. In an example method, an input image is accessed, and the input image is processed using an image encoder to generate an image embedding tensor. The image embedding tensor is processed using a mask decoder machine learning model to generate a set of mask embedding tensors. A textual input is processed using a text encoder to generate a text embedding tensor. A set of augmented masks is generated based on aggregating the text embedding tensor with the set of mask embedding tensors.

Abstract: Certain aspects of the present disclosure provide techniques for generating fine depth maps for images of a scene based on semantic segmentation and segment-based refinement neural networks. An example method generally includes generating, through a segmentation neural network, a segmentation map based on an image of a scene. The segmentation map generally comprises a map segmenting the scene into a plurality of regions, and each region of the plurality of regions is generally associated with one of a plurality of categories. A first depth map of the scene is generated through a first depth neural network based on a depth measurement of the scene. A second depth map of the scene is generated through a depth refinement neural network based on the segmentation map and the first depth map. One or more actions are taken based on the second depth map of the scene.

Abstract: An apparatus includes a memory and processing circuitry in communication with the memory. The processing circuitry is configured to process a joint graph representation using a graph neural network (GNN) to form an enhanced graph representation. The joint graph representation includes first features from a voxelized point cloud, and second features from a plurality of camera images. The enhanced graph representation includes enhanced first features and enhanced second features. The processing circuitry is further configured to perform a diffusion processes on the enhanced first features and the enhanced second features of the enhanced graph representation to form a denoised graph representation having denoised first features and denoised second features, and fuse the denoised first features and the denoised second features of the denoised graph representation using a graph attention network (GAT) to form a fused point cloud having fused features.

Abstract: A system stores first and second images generated by first and second cameras; applies a segmentation model to the first image to generate a first segmentation mask identifying object instances; applies the segmentation model to the second image to generate a second segmentation mask identifying the object instances; projects the first segmentation mask to a viewpoint of the second camera to generate a first projected segmentation mask; converts the first projected segmentation mask and the second segmentation mask to first and second semantic masks, respectively; and computes a first similarity value based on the first and second semantic masks. This may be repeated exchanging the first and second images to compute a second similarity value. The system determines a loss value based on the first similarity value and the second similarity value and trains the segmentation model based on the loss value.

Abstract: A method includes receiving one or more images, wherein at least one of the one or more images depicts a water region and analyzing, by one or more processors, the one or more images using a first machine learning model to determine a depth of the water region. The method also includes analyzing, by the one or more processors, the one or more images using a second machine learning model to determine a surface normal of the water region and performing, by the one or more processors, using a third machine learning model, multi-class segmentation of the one or more images. Additionally, the method includes performing one or more fusion operations on outputs of at least two of the first machine learning model, the second machine learning model and the third machine learning model to generate a classification of the water region.

Abstract: Systems and techniques are provided for processing image data. According to some aspects, a computing device can generate a gradient (e.g., a classifier gradient using a trained classifier) associated with a current sample. The computing device can combine the gradient with an iterative model estimated score function or data associated with the current sample to generate a score function estimate. The computing device can predict, using the diffusion machine learning model and based on the score function estimate, a new sample.

Abstract: Certain aspects of the present disclosure provide techniques for training and using an instance segmentation neural network to detect instances of a target object in an image. An example method generally includes generating, through an instance segmentation neural network, a first mask output from a first mask generation branch of the network. The method further includes generating, through the instance segmentation neural network, a second mask output from a second, parallel, mask generation branch of the network. The second mask output is typically of a lower resolution than the first mask output. The method further includes combining the first mask output and second mask output to generate a combined mask output. Based on the combined mask output, an output of the instance segmentation neural network is generated. One or more actions are taken based on the generated output.

Abstract: This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support image processing. In a first aspect, a method of image processing includes receiving a plurality of image frames by a computing device and using machine learning models to identify corrupted or occluded image frames. A first machine learning model may identify corrupted image frames, while a second machine learning model may identify partially occluded image frames. The method may further include generating updated versions of image frames captured by vehicle cameras, such as based on feature vectors from the first and second machine learning models. The feature vectors may be fused and provided to a third machine learning model to generate updated versions of occluded image frames. The method may further include determining vehicle control instructions based on the updated versions. Other aspects and features are also claimed and described.

Abstract: A processor-implemented method for adapting an artificial neural network (ANN) at test-time includes receiving by a first ANN model and a second ANN model, a test data set. The test data set includes unlabeled data samples. The first ANN model is pretrained using a training data set and the test data set. The first ANN model generates first estimated labels for the test data set. The second ANN model generates second estimated labels for the test data set. Samples of the test data set are selected based on a confidence difference between the first estimated labels and the second estimated labels. The second ANN model is retrained based on the selected samples.

Abstract: Disclosed are systems, apparatuses, processes, and computer-readable media for processing image data. For example, a process can include obtaining segmentation information associated with an image of a scene, the image including a plurality of pixels having a resolution, and obtaining depth information associated with one or more objects in the scene. A plurality of features can be generated corresponding to the plurality of pixels, wherein each feature of the plurality of features corresponds to a particular pixel of the plurality of pixels, and wherein each feature includes respective segmentation information of the particular pixel and respective depth information of the particular pixel. The plurality of features can be processed to generate a dense depth output corresponding to the image.

Abstract: Certain aspects of the present disclosure provide techniques for improved domain adaptation in machine learning. A feature tensor is generated by processing input data using a feature extractor. A first set of logits is generated by processing the feature tensor using a domain-agnostic classifier, and a second set of logits is generated by processing the feature tensor using a domain-specific classifier. A loss is computed based at least in part on the first set of logits and the second set of logits, where the loss includes a divergence loss component. The feature extractor, the domain-agnostic classifier, and the domain-specific classifier are refined using the loss.

Abstract: Techniques and systems are provided for generating one or more segmentations masks. For instance, a process may include generating a delta image based on a difference between a current image and a prior image. The process may further include processing, using a transform operation, the delta image and features representing the prior image to generate a transformed feature representation of the prior image. The process may include combining the transformed feature representation of the prior image with features representing the current image to generate a combined feature representation of the current image. The process may further include generating, based on the combined feature representation of the current image, a segmentation mask for the current image.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for regenerative learning to enhance dense predictions. In one example method, an input image is accessed. A dense prediction output is generated based on the input image using a dense prediction machine learning (ML) model, and a regenerated version of the input image is generated. A first loss is generated based on the input image and a corresponding ground truth dense prediction, and a second loss is generated based on the regenerated version of the input image. One or more parameters of the dense prediction ML model are updated based on the first and second losses.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for training a machine learning model. An example method generally includes partitioning a machine learning model into a plurality of partitions. A request to update a respective partition of the plurality of partitions in the machine learning model is transmitted to each respective participating device of a plurality of participating devices in a federated learning scheme, and the request may specify that the respective partition is to be updated based on unique data at the respective participating device. Updates to one or more partitions in the machine learning model are received from the plurality of participating devices, and the machine learning model is updated based on the received updates.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for feature masking. A feature tensor is accessed in a neural network, and a feature mask is generated by processing the feature tensor using a masking subnetwork, where the masking subnetwork was trained based at least in part on a polarization constraint and an activation constraint to generate feature masks. A masked feature tensor is generated based on the feature tensor and the feature mask, and an output inference is generated using the neural network based at least in part on the masked feature tensor.

Abstract: A method receives first and second data generated from a first and second domains including first and second set of objects, receiving first class labels for each of the first set of objects, and receiving second class labels for each of the second set of objects. The method generates a training dataset by augmenting the first data and corresponding first class labels, and locally updating neural network parameters of a model based on the training dataset. The method generates a validation dataset by augmenting the second data and corresponding second class labels, and globally updating the neural network parameters of the model based on the validation dataset. The method also generates multiple target labels for target data generated from a target domain including a third set of objects after globally updating the neural network parameters of the model based on the validation dataset.

Abstract: Techniques and systems are provided for performing online adaptation of machine learning model(s). For example, a process may include obtaining features extracted from a image by a machine learning model during inference and determining, by the machine learning model based on the features during inference, a plurality of keypoint estimates in the image and/or a bounding region estimate associated with an object in the image. The process may further include generating pseudo-label(s) based on the plurality of keypoint estimates and/or the bounding region estimate. The process may include determining at least one self-supervised loss based on the plurality of keypoint estimates and/or the bounding region estimate. The process may further include adapting, based on the at least one self-supervised loss, parameter(s) of the machine learning model. The process may include generating, using the machine learning model with the adapted parameter(s), a segmentation mask for the image (or another image).

Abstract: Systems and techniques are provided for processing one or more images. For instance, according to some aspects of the disclosure, a method may include obtaining an unlabeled image and generating at least one transformed image based on the unlabeled image. The method may include processing the unlabeled image using a pre-trained semantic segmentation model to generate a first segmentation output. The method may further include processing the at least one transformed image using the pre-trained semantic segmentation model to generate at least a second segmentation output. The method may include fine-tuning, based on the first segmentation output and at least the second segmentation output, one or more parameters of the pre-trained semantic segmentation model.

Abstract: Systems and techniques are provided for processing data (e.g., image data). For instance, according to some aspects of the disclosure, a method may include receiving, at a transformer of a machine learning system, learnable queries, keys, and values obtained from a feature map of a segmentation model of the machine learning system. The method may further include learning, via the transformer, a mapping between an unsupervised output and a supervised output of the segmentation model based on the feature map.

Abstract: Systems and techniques are provided for training a neural network model or machine learning model. For example, a method of augmenting training data can include augmenting, based on a randomly initialized neural network, training data to generate augmented training data and aggregating data with a plurality of styles from the augmented training data to generate aggregated training data. The method can further include applying semantic-aware style fusion to the aggregated training data to generate fused training data and adding the fused training data as fictitious samples to the training data to generate updated training data for training the neural network model or machine learning model.