5 citations · 11 across the 9 of their papers we have counts for
13 papers
SpatialMem: Metric-Aligned Long-Horizon Video Memory for Language Grounding and QA
Xinyi Zheng, Yunze Liu, Chi-Hao Wu +5
SpatialMem is a memory-based system which integrates 3D geometry, semantics, and language with one another in one combined, queryable representation. Starting with casually recorde…
From Detection to Anticipation: Online Understanding of Struggles across Various Tasks and Activities
Shijia Feng, Michael Wray, Walterio Mayol-Cuevas
Understanding human skill performance is essential for intelligent assistive systems, with struggle recognition offering a natural cue for identifying user difficulties. While prio…
EvoStruggle: A Dataset Capturing the Evolution of Struggle across Activities and Skill Levels
Shijia Feng, Michael Wray, Walterio Mayol-Cuevas
The ability to determine when a person struggles during skill acquisition is crucial for both optimizing human learning and enabling the development of effective assistive systems.…
CULTURE3D: A Large-Scale and Diverse Dataset of Cultural Landmarks and Terrains for Gaussian-Based Scene Rendering
Xinyi Zheng, Steve Zhang, Weizhe Lin +4
Current state-of-the-art 3D reconstruction models face limitations in building extra-large scale outdoor scenes, primarily due to the lack of sufficiently large-scale and detailed…
X-LeBench: A Benchmark for Extremely Long Egocentric Video Understanding
Wenqi Zhou, Kai Cao, Hao Zheng +8
Long-form egocentric video understanding provides rich contextual information and unique insights into long-term human behaviors, holding significant potential for applications in…
Re-localization acceleration with Medoid Silhouette Clustering
Hongyi Zhang, Walterio Mayol-Cuevas
Two crucial performance criteria for the deployment of visual localization are speed and accuracy. Current research on visual localization with neural networks is limited to examin…
Are you Struggling? Dataset and Baselines for Struggle Determination in Assembly Videos
Shijia Feng, Michael Wray, Brian Sullivan +4
Determining when people are struggling allows for a finer-grained understanding of actions that complements conventional action classification and error detection. Struggle detecti…
SuperTran: Reference Based Video Transformer for Enhancing Low Bitrate Streams in Real Time
Tejas Khot, Nataliya Shapovalova, Silviu Andrei +1
This work focuses on low bitrate video streaming scenarios (e.g. 50 - 200Kbps) where the video quality is severely compromised. We present a family of novel deep generative models…
On-Sensor Binarized Fully Convolutional Neural Network with A Pixel Processor Array
Yanan Liu, Laurie Bose, Yao Lu +2
This work presents a method to implement fully convolutional neural networks (FCNs) on Pixel Processor Array (PPA) sensors, and demonstrates coarse segmentation and object localisa…
The Object at Hand: Automated Editing for Mixed Reality Video Guidance from Hand-Object Interactions
Yao Lu, Walterio W. Mayol-Cuevas
In this paper, we concern with the problem of how to automatically extract the steps that compose real-life hand activities. This is a key competence towards processing, monitoring…
Understanding Egocentric Hand-Object Interactions from Hand Pose Estimation
Yao Lu, Walterio W. Mayol-Cuevas
In this paper, we address the problem of estimating the hand pose from the egocentric view when the hand is interacting with objects. Specifically, we propose a method to label a d…
Filter Distribution Templates in Convolutional Networks for Image Classification Tasks
Ramon Izquierdo-Cordova, Walterio Mayol-Cuevas
Neural network designers have reached progressive accuracy by increasing models depth, introducing new layer types and discovering new combinations of layers. A common element in m…
Towards Efficient Convolutional Network Models with Filter Distribution Templates
Ramon Izquierdo-Cordova, Walterio Mayol-Cuevas
Increasing number of filters in deeper layers when feature maps are decreased is a widely adopted pattern in convolutional network design. It can be found in classical CNN architec…