SRI author: Han-Pang Chiu Abstract Class imbalance is a fundamental problem in computer vision applications such as semantic segmentation. Specifically, uneven class distributions in a training dataset often result in unsatisfactory performance on under-represented classes. Many works have proposed to weight the standard cross entropy loss function with pre-computed weights based on class statistics, such […]
Computer vision publications
Graph Mapper: Efficient Visual Navigation by Scene Graph Generation
SRI authors: Han-Pang Chiu, Supun Samarasekera, Rakesh Kumar Abstract Understanding the geometric relationships between objects in a scene is a core capability in enabling both humans and autonomous agents to navigate in new environments. A sparse, unified representation of the scene topology will allow agents to act efficiently to move through their environment, communicate the […]
SASRA: Semantically-aware Spatio-temporal Reasoning Agent for Vision-and-Language Navigation in Continuous Environments
SRI authors: Han-Pang Chiu, Supun Samarasekera, Rakesh Kumar Abstract This paper presents a novel approach for the Vision-and-Language Navigation (VLN) task in continuous 3D environments, which requires an autonomous agent to follow natural language instructions in unseen environments. Existing end-to-end learning-based VLN methods struggle at this task as they focus mostly on utilizing raw visual […]
Head-Worn Markerless Augmented Reality Inside a Moving Vehicle
SRI authors: Han-Pang Chiu, Supun Samarasekera, Rakesh Kumar Abstract This paper describes a system that provides general head-worn outdoor augmented reality (AR) capability for the user inside a moving vehicle. Our system follows the concept of combining pose estimation from both vehicle navigation system and wearable sensors to address the failure of commercial AR devices […]
SIGNAV: Semantically-Informed GPS-Denied Navigation and Mapping in Visually-Degraded Environments
SRI authors: Han-Pang Chiu, Supun Samarasekera Abstract Understanding the perceived scene during navigation enables intelligent robot behaviors. Current vision-based semantic SLAM (Simultaneous Localization and Mapping) systems provide these capabilities. However, their performance decreases in visually-degraded environments, that are common places for critical robotic applications, such as search and rescue missions. In this paper, we present […]
Resilient Data Augmentation Approaches to Multimodal Verification in the News Domain
With the advent of generative adversarial networks and misinformation in social media, there has been increased interest in multimodal verification. Image-text verification typically involves determining whether a caption and an image correspond with each other. Building on multimodal embedding techniques, we show that data augmentation via two distinct approaches improves results: entity linking and cross-domain local similarity scaling. We refer to the approaches as resilient because we show state-of-the-art results against manipulations specifically designed to thwart the exact multimodal embeddings we are using as the basis for all of our features.
Long-Range Augmented Reality with Dynamic Occlusion Rendering
Proper occlusion based rendering is very important to achieve realism in all indoor and outdoor Augmented Reality (AR) applications. This paper addresses the problem of fast and accurate dynamic occlusion reasoning by real objects in the scene for large scale outdoor AR applications. Conceptually, proper occlusion reasoning requires an estimate of depth for every point in augmented scene which is technically hard to achieve for outdoor scenarios, especially in the presence of moving objects. We propose a method to detect and automatically infer the depth for real objects in the scene without explicit detailed scene modeling and depth sensing (e.g. without using sensors such as 3D-LiDAR). Specifically, we employ instance segmentation of color image data to detect real dynamic objects in the scene and use either a top-down terrain elevation model or deep learning based monocular depth estimation model to infer their metric distance from the camera for proper occlusion reasoning in real time. The realized solution is implemented in a low latency real-time framework for video-see-though AR and is directly extendable to optical-see-through AR. We minimize latency in depth reasoning and occlusion rendering by doing semantic object tracking and prediction in video frames.
MaAST: Map Attention with Semantic Transformers for Efficient Visual Navigation
Visual navigation for autonomous agents is a core task in the fields of computer vision and robotics. Learning based methods, such as deep reinforcement learning, have the potential to outperform the classical solutions developed for this task; however, they come at a significantly increased computational load. Through this work, we design a novel approach that focuses on performing better or comparable to the existing learning-based solutions but under a clear time/computational budget. To this end, we propose a method to encode vital scene semantics such as traversable paths, unexplored areas, and observed scene objects–alongside raw visual streams such as RGB, depth, and semantic segmentation masks—into a semantically informed, top-down egocentric map representation. Further, to enable the effective use of this information, we introduce a novel 2-D map attention mechanism, based on the successful multi-layer Transformer networks. We conduct experiments on 3-D reconstructed indoor PointGoal visual navigation and demonstrate the effectiveness of our approach. We show that by using our novel attention schema and auxiliary rewards to better utilize scene semantics, we outperform multiple baselines trained with only raw inputs or implicit semantic information while operating with an 80% decrease in the agent’s experience.
RGB2LIDAR: Towards Solving Large-Scale Cross-Modal Visual Localization
We study an important, yet largely unexplored problem of large-scale cross-modal visual localization by matching ground RGB images to a geo-referenced aerial LIDAR 3D point cloud (rendered as depth images). Prior works were demonstrated on small datasets and did not lend themselves to scaling up for large-scale applications. To enable large-scale evaluation, we introduce a new dataset containing over 550K pairs (covering 143 km^2 area) of RGB and aerial LIDAR depth images. We propose a novel joint embedding based method that effectively combines the appearance and semantic cues from both modalities to handle drastic cross-modal variations. Experiments on the proposed dataset show that our model achieves a strong result of a median rank of 5 in matching across a large test set of 50K location pairs collected from a 14km^2 area. This represents a significant advancement over prior works in performance and scale. We conclude with qualitative results to highlight the challenging nature of this task and the benefits of the proposed model. Our work provides a foundation for further research in cross-modal visual localization.
