4 citations · 28 across the 22 of their papers we have counts for
17 papers · 1 filter
AROS: Affordance Recognition with One-Shot Human Stances
Abel Pacheco-Ortega, Walterio Mayol-Cuevas
We present AROS, a one-shot learning approach that uses an explicit representation of interactions between highly-articulated human poses and 3D scenes. The approach is one-shot as…
Egocentric Hand-object Interaction Detection and Application
Yao Lu, Walterio W. Mayol-Cuevas
In this paper, we present a method to detect the hand-object interaction from an egocentric perspective. In contrast to massive data-driven discriminator based method like \cite{Sh…
Direct Servo Control from In-Sensor CNN Inference with A Pixel Processor Array
Yanan Liu, Jianing Chen, Laurie Bose +2
This work demonstrates direct visual sensory-motor control using high-speed CNN inference via a SCAMP-5 Pixel Processor Array (PPA). We demonstrate how PPAs are able to efficiently…
Fully Embedding Fast Convolutional Networks on Pixel Processor Arrays
Laurie Bose, Jianing Chen, Stephen J. Carey +2
We present a novel method of CNN inference for pixel processor array (PPA) vision sensors, designed to take advantage of their massive parallelism and analog compute capabilities.…
Action Modifiers: Learning from Adverbs in Instructional Videos
Hazel Doughty, Ivan Laptev, Walterio Mayol-Cuevas +1
We present a method to learn a representation for adverbs from instructional videos using weak supervision from the accompanying narrations. Key to our method is the fact that the…
A Camera That CNNs: Towards Embedded Neural Networks on Pixel Processor Arrays
Laurie Bose, Jianing Chen, Stephen J. Carey +2
We present a convolutional neural network implementation for pixel processor array (PPA) sensors. PPA hardware consists of a fine-grained array of general-purpose processing elemen…
Egocentric affordance detection with the one-shot geometry-driven Interaction Tensor
Eduardo Ruiz, Walterio Mayol-Cuevas
In this abstract we describe recent [4,7] and latest work on the determination of affordances in visually perceived 3D scenes. Our method builds on the hypothesis that geometry on…
What can I do here? Leveraging Deep 3D saliency and geometry for fast and scalable multiple affordance detection
Eduardo Ruiz, Walterio Mayol-Cuevas
This paper develops and evaluates a novel method that allows for the detection of affordances in a scalable and multiple-instance manner on visually recovered pointclouds. Our appr…
The Pros and Cons: Rank-aware Temporal Attention for Skill Determination in Long Videos
Hazel Doughty, Walterio Mayol-Cuevas, Dima Damen
We present a new model to determine relative skill from long videos, through learnable temporal attention modules. Skill determination is formulated as a ranking problem, making it…
Towards CNN map representation and compression for camera relocalisation
Luis Contreras, Walterio Mayol-Cuevas
This paper presents a study on the use of Convolutional Neural Networks for camera relocalisation and its application to map compression. We follow state of the art visual relocali…
Improving Classification by Improving Labelling: Introducing Probabilistic Multi-Label Object Interaction Recognition
Michael Wray, Davide Moltisanti, Walterio Mayol-Cuevas +1
This work deviates from easy-to-define class boundaries for object interactions. For the task of object interaction recognition, often captured using an egocentric view, we show th…
Geometric Affordances from a Single Example via the Interaction Tensor
Eduardo Ruiz, Walterio Mayol-Cuevas
This paper develops and evaluates a new tensor field representation to express the geometric affordance of one object over another. We expand the well known bisector surface repres…
Towards CNN Map Compression for camera relocalisation
Luis Contreras, Walterio Mayol-Cuevas
This paper presents a study on the use of Convolutional Neural Networks for camera relocalisation and its application to map compression. We follow state of the art visual relocali…
Who's Better? Who's Best? Pairwise Deep Ranking for Skill Determination
Hazel Doughty, Dima Damen, Walterio Mayol-Cuevas
We present a method for assessing skill from video, applicable to a variety of tasks, ranging from surgery to drawing and rolling pizza dough. We formulate the problem as pairwise…
Trespassing the Boundaries: Labeling Temporal Bounds for Object Interactions in Egocentric Video
Davide Moltisanti, Michael Wray, Walterio Mayol-Cuevas +1
Manual annotations of temporal bounds for object interactions (i.e. start and end times) are typical training input to recognition, localization and detection algorithms. For three…
SEMBED: Semantic Embedding of Egocentric Action Videos
Michael Wray, Davide Moltisanti, Walterio Mayol-Cuevas +1
We present SEMBED, an approach for embedding an egocentric object interaction video in a semantic-visual graph to estimate the probability distribution over its potential semantic…
You-Do, I-Learn: Unsupervised Multi-User egocentric Approach Towards Video-Based Guidance
Dima Damen, Teesid Leelasawassuk, Walterio Mayol-Cuevas
This paper presents an unsupervised approach towards automatically extracting video-based guidance on object usage, from egocentric video and wearable gaze tracking, collected from…