Computer vision publications
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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.
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Lifelong learning using Eigentasks: Task separation, skill acquisition, and selective transfer
We introduce the eigentask framework for lifelong learning. An eigentask is a pairing of a skill that solves a set of related tasks, paired with a generative model that can…
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Deep Adaptive Semantic Logic (DASL): Compiling Declarative Knowledge into Deep Neural Networks
We introduce Deep Adaptive Semantic Logic (DASL), a novel framework for automating the generation of deep neural networks that incorporates user-provided formal knowledge to improve learning from data.
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Bit Efficient Quantization for Deep Neural Networks
In this paper, we present a comparison of model-parameter driven quantization approaches that can achieve as low as 3-bit precision without affecting accuracy.
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Semantically-Aware Attentive Neural Embeddings for 2D Long-Term Visual Localization
We present an approach that combines appearance and semantic information for 2D image-based localization (2D-VL) across large perceptual changes and time lags.
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Multi-Sensor Fusion for Motion Estimation in Visually-Degraded Environments
This paper analyzes the feasibility of utilizing multiple low-cost on-board sensors for ground robots or drones navigating in visually-degraded environments.
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Stacked Spatio-Temporal Graph Convolutional Networks for Action Segmentation
We propose novel Stacked Spatio-Temporal Graph Convolutional Networks (Stacked-STGCN) for action segmentation, i.e., predicting and localizing a sequence of actions over long videos.
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Toward Runtime Throttleable Neural Networks
This paper presents an approach to creating runtime-throttleable NNs that can adaptively balance performance and resource use in response to a control signal.
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Lucid Explanations Help: Using a Human-AI Image-Guessing Game to Evaluate Machine Explanation Helpfulness
We propose a Twenty-Questions style collaborative image retrieval game as a method of evaluating the efficacy of explanations (visual evidence or textual justification) in the context of Visual Question Answering.
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Fast, Full Chip Image Stitching of Nanoscale Integrated Circuits
In this paper, we describe the algorithmic steps taken in the processing pipeline to quickly create a global image database of an entire advanced IC.
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Spectral Convolutional Networks on Hierarchical Multigraphs
In this work, we address this limitation by revisiting a particular family of spectral graph networks, Chebyshev GCNs, showing its efficacy in solving graph classification tasks with a variable graph…
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Bootstrapping Deep Neural Networks from Image Processing and Computer Vision Pipelines
We intend to replace parts or all of a target pipeline with deep neural networks to achieve benefits such as increased accuracy or reduced computational requirement.