Nec Corporation

Samuel Schulter

Manmohan Chandraker

Wongun Choi

Yi-Hsuan Tsai

Paul Vernaza

Buyu Liu

Bharat Singh

Bingbing Zhuang

Kihyuk Sohn

Gaurav Sharma

Sparsh Garg

Menghua Zhai

Tuan Hung Vu

Xiang Yu

Quoc-Huy Tran

Ramin Moslemi

Pan Ji

Jinwoo Choi

Vijay Kumar Baikampady Gopalkrishna

Ziyan Wang

Nataniel Ruiz

Xiangyun Zhao

Nanbo Li

Inkyu Shin

Systems and methods are provided for identifying and retrieving semantically similar images from a database. Semantic analysis is performed on an input query utilizing a vision language model to identify semantic concepts associated with the input query. A preliminary set of images is retrieved from the database for semantic concepts identified. Relevant concepts are extracted for images with a tokenizer by comparing images against a predefined label space to identify relevant concepts. A ranked list of relevant concepts is generated based on occurrence frequency within the set. The preliminary set of images is refined based on selecting specific relevant concepts from the ranked list by the user by combining the input query with the specific relevant concepts. Additional semantic analysis is iteratively performed to retrieve additional sets of images semantically similar to the combined input query and selection of the specific relevant concepts until a threshold condition is met.

Multimodal semantic analysis and image retrieval

Systems and methods are provided for multi-modal test-time adaptation. The method includes inputting a digital image into a pre-trained Camera Intra-modal Pseudo-label Generator, and inputting a point cloud set into a pre-trained Lidar Intra-modal Pseudo-label Generator. The method further includes applying a fast 2-dimension (2D) model, and a slow 2D model, to the inputted digital image to apply pseudo-labels, and applying a fast 3-dimension (3D) model, and a slow 3D model, to the inputted point cloud set to apply pseudo-labels. The method further includes fusing pseudo-label predictions from the fast models and the slow models through an Inter-modal Pseudo-label Refinement module to obtain robust pseudo labels, and measuring a prediction consistency for the pseudo-labels. The method further includes selecting confident pseudo-labels from the robust pseudo labels and measured prediction consistencies to form a final cross-modal pseudo-label set as a self-training signal, and updating batch parameters utilizing the self-training signal.

Multi-modal test-time adaptation

Methods and systems for detecting faults include capturing an image of a scene using a camera. The image is embedded using a segmentation model that includes an image branch having an image embedding layer that embeds images into a joint latent space and a text branch having a text embedding layer that embeds text into the joint latent space. Semantic information is generated for a region of the image corresponding to a predetermined static object using the embedded image. A fault of the camera is identified based on a discrepancy between the semantic information and semantic information of the predetermined static image. The fault of the camera is corrected.

Semantic image capture fault detection

A method for achieving high-fidelity novel view synthesis and 3D reconstruction for large-scale scenes is presented. The method includes obtaining images from a video stream received from a plurality of video image capturing devices, grouping the images into different image clusters representing a large-scale 3D scene, training a neural radiance field (NeRF) and an uncertainty multilayer perceptron (MLP) for each of the image clusters to generate a plurality of NeRFs and a plurality of uncertainty MLPs for the large-scale 3D scene, applying a rendering loss and an entropy loss to the plurality of NeRFs, performing uncertainty-based fusion to the plurality of NeRFs to define a fused NeRF, and jointly fine-tuning the plurality of NeRFs and the plurality of uncertainty MLPs, and during inference, applying the fused NeRF for novel view synthesis of the large-scale 3D scene.

Uncertainty-aware fusion towards large-scale NeRF

Systems and methods for domain adaptation for structured output via disentangled representations are provided. The system receives a ground truth of a source domain. The ground truth is used in a task loss function for a first convolutional neural network that predicts at least one output based on inputs from the source domain and a target domain. The system clusters the ground truth of the source domain into a predetermined number of clusters, and predicts, via a second convolutional neural network, a structure of label patches. The structure includes an assignment of each of the at least one output of the first convolutional neural network to the predetermined number of clusters. A cluster loss is computed for the predicted structure of label patches, and an adversarial loss function is applied to the predicted structure of label patches to align the source domain and the target domain on a structural level.

Domain adaptation for structured output via disentangled representations

A method is provided for danger prediction. The method includes generating fully-annotated simulated training data for a machine learning model responsive to receiving a set of computer-selected simulator-adjusting parameters. The method further includes training the machine learning model using reinforcement learning on the fully-annotated simulated training data. The method also includes measuring an accuracy of the trained machine learning model relative to learning a discriminative function for a given task. The discriminative function predicts a given label for a given image from the fully-annotated simulated training data. The method additionally includes adjusting the computer-selected simulator-adjusting parameters and repeating said training and measuring steps responsive to the accuracy being below a threshold accuracy. The method further includes predicting a dangerous condition relative to a motor vehicle and providing a warning to an entity regarding the dangerous condition by applying the trained machine learning model to actual unlabeled data for the vehicle.

Learning to simulate

Systems and methods for road typology scene annotation are provided. A method for road typology scene annotation includes receiving an image having a road scene. The image is received from an imaging device. The method populates, using a machine learning model, a set of attribute settings with values representing the road scene. An annotation interface is implemented and configured to adjust values of the attribute settings to correspond with the road scene. Based on the values of the attribute settings, a simulated overhead view of the respective road scene is generated.

Scene attribute annotation of complex road typographies

A method for performing video domain adaptation for human action recognition is presented. The method includes using annotated source data from a source video and unannotated target data from a target video in an unsupervised domain adaptation setting, identifying and aligning discriminative clips in the source and target videos via an attention mechanism, and learning spatial-background invariant human action representations by employing a self-supervised clip order prediction loss for both the annotated source data and the unannotated target data.

Shuffle, attend, and adapt: video domain adaptation by clip order prediction and clip attention alignment

A method is provided in an Advanced Driver-Assistance System (ADAS). The method extracts, from an input video stream including a plurality of images using a multi-task Convolutional Neural Network (CNN), shared features across different perception tasks. The perception tasks include object detection and other perception tasks. The method concurrently solves, using the multi-task CNN, the different perception tasks in a single pass by concurrently processing corresponding ones of the shared features by respective different branches of the multi-task CNN to provide a plurality of different perception task outputs. Each respective different branch corresponds to a respective one of the different perception tasks. The method forms a parametric representation of a driving scene as at least one top-view map responsive to the plurality of different perception task outputs. The method controls an operation of the vehicle for collision avoidance responsive to the at least one top-view map indicating an impending collision.

Multi-task perception network with applications to scene understanding and advanced driver-assistance system

Systems and methods are provided for producing a road layout model. The method includes capturing digital images having a perspective view, converting each of the digital images into top-down images, and conveying a top-down image of time t to a neural network that performs a feature transform to form a feature map of time t. The method also includes transferring the feature map of the top-down image of time t to a feature transform module to warp the feature map to a time t+1, and conveying a top-down image of time t+1 to form a feature map of time t+1. The method also includes combining the warped feature map of time t with the feature map of time t+1 to form a combined feature map, transferring the combined feature map to a long short-term memory (LSTM) module to generate the road layout model, and displaying the road layout model.

Parametric top-view representation of complex road scenes

A method for implementing parametric models for scene representation to improve autonomous task performance includes generating an initial map of a scene based on at least one image corresponding to a perspective view of the scene, the initial map including a non-parametric top-view representation of the scene, implementing a parametric model to obtain a scene element representation based on the initial map, the scene element representation providing a description of one or more scene elements of the scene and corresponding to an estimated semantic layout of the scene, identifying one or more predicted locations of the one or more scene elements by performing three-dimensional localization based on the at least one image, and obtaining an overlay for performing an autonomous task by placing the one or more scene elements with the one or more respective predicted locations onto the scene element representation.

Parametric top-view representation of scenes

Methods and systems for object detection include training dataset-specific object detectors using respective annotated datasets, each of the annotated datasets including annotations for a respective set of one or more object classes. The annotated datasets are cross-annotated using the dataset-specific object detectors. A unified object detector is trained, using the cross-annotated datasets, to detect all of the object classes of the annotated datasets. Objects are detected in an input image using the unified object detector.

Object detection with training from multiple datasets

A computer-implemented method, system, and computer program product are provided for object detection utilizing an online flow guided memory network. The method includes receiving a plurality of videos, each of the plurality of videos including a plurality of frames. The method also includes generating, with a feature extraction network, a frame feature map for a current frame of the plurality of frames. The method additionally includes aggregating a memory feature map from the frame feature map and previous memory feature maps from previous frames on a plurality of time axes, with the plurality of time axes including a first time axis at a first frame increment and a second time axis at a second frame increment. The method further includes predicting, with a task network, an object from the memory feature map. The method also includes controlling an operation of a processor-based machine to react in accordance with the object.

Temporal multi-scale clockwork memory networks for object detection in videos

A computer-implemented method, system, and computer program product are provided for object detection utilizing an online flow guided memory network. The method includes receiving, by a processor, a plurality of videos, each of the plurality of videos including a plurality of frames. The method also includes generating, by the processor with a feature extraction network, a frame feature map for a current frame of the plurality of frames. The method additionally includes determining, by the processor, a memory feature map from the frame feature map and a previous memory feature map from a previous frame by warping the previous memory feature map. The method further includes predicting, by the processor with a task network, an object from the memory feature map. The method also includes controlling an operation of a processor-based machine to react in accordance with the object.

Online flow guided memory networks for object detection in video

Systems and methods for generating an occlusion-aware bird's eye view map of a road scene include identifying foreground objects and background objects in an input image to extract foreground features and background features corresponding to the foreground objects and the background objects, respectively. The foreground objects are masked from the input image with a mask. Occluded objects and depths of the occluded objects are inferred by predicting semantic features and depths in masked areas of the masked image according to contextual information related to the background features visible in the masked image. The foreground objects and the background objects are mapped to a three-dimensional space according to locations of each of the foreground objects, the background objects and occluded objects using the inferred depths. A bird's eye view is generated from the three-dimensional space and displayed with a display device.

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Santa Clara, CA, United States of America

Samuel Schulter