Search Results for author: Camillo J. Taylor

Found 24 papers, 10 papers with code

A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners

no code implementations16 Jun 2024 Bowen Jiang, Yangxinyu Xie, Zhuoqun Hao, Xiaomeng Wang, Tanwi Mallick, Weijie J. Su, Camillo J. Taylor, Dan Roth

This study introduces a hypothesis-testing framework to assess whether large language models (LLMs) possess genuine reasoning abilities or primarily depend on token bias.

Logical Reasoning

Multi-Modal and Multi-Agent Systems Meet Rationality: A Survey

1 code implementation1 Jun 2024 Bowen Jiang, Yangxinyu Xie, Xiaomeng Wang, Weijie J. Su, Camillo J. Taylor, Tanwi Mallick

Rationality is the quality of being guided by reason, characterized by logical thinking and decision-making that align with evidence and logical rules.

Decision Making

EV-Catcher: High-Speed Object Catching Using Low-latency Event-based Neural Networks

no code implementations14 Apr 2023 ZiYun Wang, Fernando Cladera Ojeda, Anthony Bisulco, Daewon Lee, Camillo J. Taylor, Kostas Daniilidis, M. Ani Hsieh, Daniel D. Lee, Volkan Isler

Event-based sensors have recently drawn increasing interest in robotic perception due to their lower latency, higher dynamic range, and lower bandwidth requirements compared to standard CMOS-based imagers.

Hierarchical Relationships: A New Perspective to Enhance Scene Graph Generation

1 code implementation13 Mar 2023 Bowen Jiang, Camillo J. Taylor

This paper presents a finding that leveraging the hierarchical structures among labels for relationships and objects can substantially improve the performance of scene graph generation systems.

Contrastive Learning Graph Generation +2

Bayesian Deep Basis Fitting for Depth Completion with Uncertainty

no code implementations ICCV 2021 Chao Qu, Wenxin Liu, Camillo J. Taylor

By adopting a Bayesian treatment, our Bayesian Deep Basis Fitting (BDBF) approach is able to 1) predict high-quality uncertainty estimates and 2) enable depth completion with few or no sparse measurements.

Depth Completion Depth Estimation +1

Depth Completion via Deep Basis Fitting

no code implementations21 Dec 2019 Chao Qu, Ty Nguyen, Camillo J. Taylor

In this paper we consider the task of image-guided depth completion where our system must infer the depth at every pixel of an input image based on the image content and a sparse set of depth measurements.

Depth Completion

PST900: RGB-Thermal Calibration, Dataset and Segmentation Network

1 code implementation20 Sep 2019 Shreyas S. Shivakumar, Neil Rodrigues, Alex Zhou, Ian D. Miller, Vijay Kumar, Camillo J. Taylor

In this work we propose long wave infrared (LWIR) imagery as a viable supporting modality for semantic segmentation using learning-based techniques.

Camera Calibration Segmentation +2

DFineNet: Ego-Motion Estimation and Depth Refinement from Sparse, Noisy Depth Input with RGB Guidance

no code implementations15 Mar 2019 Yilun Zhang, Ty Nguyen, Ian D. Miller, Shreyas S. Shivakumar, Steven Chen, Camillo J. Taylor, Vijay Kumar

Depth estimation is an important capability for autonomous vehicles to understand and reconstruct 3D environments as well as avoid obstacles during the execution.

Autonomous Vehicles Depth Completion +2

DFuseNet: Deep Fusion of RGB and Sparse Depth Information for Image Guided Dense Depth Completion

1 code implementation2 Feb 2019 Shreyas S. Shivakumar, Ty Nguyen, Ian D. Miller, Steven W. Chen, Vijay Kumar, Camillo J. Taylor

In this paper we propose a convolutional neural network that is designed to upsample a series of sparse range measurements based on the contextual cues gleaned from a high resolution intensity image.

Depth Completion Super-Resolution

Predictive and Semantic Layout Estimation for Robotic Applications in Manhattan Worlds

no code implementations19 Nov 2018 Armon Shariati, Bernd Pfrommer, Camillo J. Taylor

This paper describes an approach to automatically extracting floor plans from the kinds of incomplete measurements that could be acquired by an autonomous mobile robot.

Motion Planning

Real Time Dense Depth Estimation by Fusing Stereo with Sparse Depth Measurements

1 code implementation20 Sep 2018 Shreyas S. Shivakumar, Kartik Mohta, Bernd Pfrommer, Vijay Kumar, Camillo J. Taylor

We present an approach to depth estimation that fuses information from a stereo pair with sparse range measurements derived from a LIDAR sensor or a range camera.

Depth Estimation

U-Net for MAV-based Penstock Inspection: an Investigation of Focal Loss in Multi-class Segmentation for Corrosion Identification

no code implementations18 Sep 2018 Ty Nguyen, Tolga Ozaslan, Ian D. Miller, James Keller, Giuseppe Loianno, Camillo J. Taylor, Daniel D. Lee, Vijay Kumar, Joseph H. Harwood, Jennifer Wozencraft

Periodical inspection and maintenance of critical infrastructure such as dams, penstocks, and locks are of significant importance to prevent catastrophic failures.

Simultaneous Localization and Layout Model Selection in Manhattan Worlds

no code implementations11 Sep 2018 Armon Shariati, Bernd Pfrommer, Camillo J. Taylor

In this paper, we will demonstrate how Manhattan structure can be exploited to transform the Simultaneous Localization and Mapping (SLAM) problem, which is typically solved by a nonlinear optimization over feature positions, into a model selection problem solved by a convex optimization over higher order layout structures, namely walls, floors, and ceilings.

Model Selection Simultaneous Localization and Mapping

Robust Fruit Counting: Combining Deep Learning, Tracking, and Structure from Motion

no code implementations1 Apr 2018 Xu Liu, Steven W. Chen, Shreyas Aditya, Nivedha Sivakumar, Sandeep Dcunha, Chao Qu, Camillo J. Taylor, Jnaneshwar Das, Vijay Kumar

We present a novel fruit counting pipeline that combines deep segmentation, frame to frame tracking, and 3D localization to accurately count visible fruits across a sequence of images.

Robust Stereo Visual Inertial Odometry for Fast Autonomous Flight

11 code implementations30 Nov 2017 Ke Sun, Kartik Mohta, Bernd Pfrommer, Michael Watterson, Sikang Liu, Yash Mulgaonkar, Camillo J. Taylor, Vijay Kumar

However, we still encounter challenges in terms of improving the computational efficiency and robustness of the underlying algorithms for applications in autonomous flight with micro aerial vehicles in which it is difficult to use high quality sensors and pow- erful processors because of constraints on size and weight.

Robotics

Unsupervised Deep Homography: A Fast and Robust Homography Estimation Model

3 code implementations12 Sep 2017 Ty Nguyen, Steven W. Chen, Shreyas S. Shivakumar, Camillo J. Taylor, Vijay Kumar

Homography estimation between multiple aerial images can provide relative pose estimation for collaborative autonomous exploration and monitoring.

Homography Estimation Pose Estimation

Hierarchically-Constrained Optical Flow

no code implementations CVPR 2015 Ryan Kennedy, Camillo J. Taylor

This paper presents a novel approach to solving optical flow problems using a discrete, tree-structured MRF derived from a hierarchical segmentation of the image.

Optical Flow Estimation

Online Algorithms for Factorization-Based Structure from Motion

no code implementations26 Sep 2013 Ryan Kennedy, Laura Balzano, Stephen J. Wright, Camillo J. Taylor

We present a family of online algorithms for real-time factorization-based structure from motion, leveraging a relationship between incremental singular value decomposition and recently proposed methods for online matrix completion.

Matrix Completion

Towards Fast and Accurate Segmentation

no code implementations CVPR 2013 Camillo J. Taylor

In this paper we explore approaches to accelerating segmentation and edge detection algorithms based on the gPb framework.

Edge Detection Segmentation

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