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Found 29 papers, 10 papers with code
Saliency-guided Adaptive Seeding for Supervoxel Segmentation
We propose a new saliency-guided method for generating supervoxels in 3D space.
Occlusion Resistant Object Rotation Regression from Point Cloud Segments
Rotation estimation of known rigid objects is important for robotic applications such as dexterous manipulation.
Neural Metaphor Detection in Context
We present end-to-end neural models for detecting metaphorical word use in context.
6D Object Pose Regression via Supervised Learning on Point Clouds
We use depth information represented by point clouds as the input to both deep networks and geometry-based pose refinement and use separate networks for rotation and translation regression.
Neural Image Compression via Attentional Multi-Scale Back Projection and Frequency Decomposition
In recent years, neural image compression emerges as a rapidly developing topic in computer vision, where the state-of-the-art approaches now exhibit superior compression performance than their conventional counterparts.
Early Performance Prediction using Interpretable Patterns in Programming Process Data
From the log data, we extract patterns of behaviors that are predictive of students' success using an approach called differential sequence mining.
CloudAAE: Learning 6D Object Pose Regression with On-line Data Synthesis on Point Clouds
In contrast, this domain gap is considerably smaller and easier to fill for depth information.
Guidance and Teaching Network for Video Salient Object Detection
Owing to the difficulties of mining spatial-temporal cues, the existing approaches for video salient object detection (VSOD) are limited in understanding complex and noisy scenarios, and often fail in inferring prominent objects.
Modeling and Validating Temporal Rules with Semantic Petri-Net for Digital Twins
Semantic rule checking on RDFS/OWL data has been widely used in the construction industry.
Reconstructing Missing EHRs Using Time-Aware Within- and Cross-Visit Information for Septic Shock Early Prediction
Real-world Electronic Health Records (EHRs) are often plagued by a high rate of missing data.
Simulating Bandit Learning from User Feedback for Extractive Question Answering
We study learning from user feedback for extractive question answering by simulating feedback using supervised data.
Facilitating Global Team Meetings Between Language-Based Subgroups: When and How Can Machine Translation Help?
We manipulated the exchange of subgroup conversation logs prior to team meetings: with MT-mediated exchanges versus without.
Taking a Language Detour: How International Migrants Speaking a Minority Language Seek COVID-Related Information in Their Host Countries
Our data indicated that participants often took language detours, or visits to Mandarin resources for information about the COVID outbreak in their host countries.
Client Selection in Federated Learning: Principles, Challenges, and Opportunities
As a privacy-preserving paradigm for training Machine Learning (ML) models, Federated Learning (FL) has received tremendous attention from both industry and academia.
A Geometric-Relational Deep Learning Framework for BIM Object Classification
It can be supplemented using deep learning.
Variational Latent Branching Model for Off-Policy Evaluation
In this work, we propose the variational latent branching model (VLBM) to learn the transition function of MDPs by formulating the environmental dynamics as a compact latent space, from which the next states and rewards are then sampled.
HOPE: Human-Centric Off-Policy Evaluation for E-Learning and Healthcare
Reinforcement learning (RL) has been extensively researched for enhancing human-environment interactions in various human-centric tasks, including e-learning and healthcare.
An Offline Time-aware Apprenticeship Learning Framework for Evolving Reward Functions
Apprenticeship learning (AL) is a process of inducing effective decision-making policies via observing and imitating experts' demonstrations.
Continually Improving Extractive QA via Human Feedback
We study continually improving an extractive question answering (QA) system via human user feedback.
HiNeRV: Video Compression with Hierarchical Encoding-based Neural Representation
Learning-based video compression is currently a popular research topic, offering the potential to compete with conventional standard video codecs.
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Emoji Promotes Developer Participation and Issue Resolution on GitHub
In this paper, we study how emoji usage influences developer participation and issue resolution in virtual workspaces.
Policy-Gradient Training of Language Models for Ranking
Text retrieval plays a crucial role in incorporating factual knowledge for decision making into language processing pipelines, ranging from chat-based web search to question answering systems.
Physician Detection of Clinical Harm in Machine Translation: Quality Estimation Aids in Reliance and Backtranslation Identifies Critical Errors
A major challenge in the practical use of Machine Translation (MT) is that users lack guidance to make informed decisions about when to rely on outputs.
Accelerating Learnt Video Codecs with Gradient Decay and Layer-wise Distillation
In recent years, end-to-end learnt video codecs have demonstrated their potential to compete with conventional coding algorithms in term of compression efficiency.
NeuSurf: On-Surface Priors for Neural Surface Reconstruction from Sparse Input Views
To achieve this, we train a neural network to learn a global implicit field from the on-surface points obtained from SfM and then leverage it as a coarse geometric constraint.
GridFormer: Point-Grid Transformer for Surface Reconstruction
Our method maximizes the spatial expressiveness of grid features and maintains computational efficiency.
Emojis Decoded: Leveraging ChatGPT for Enhanced Understanding in Social Media Communications
Our objective is to validate the hypothesis that ChatGPT can serve as a viable alternative to human annotators in emoji research and that its ability to explain emoji meanings can enhance clarity and transparency in online communications.
NatSGD: A Dataset with Speech, Gestures, and Demonstrations for Robot Learning in Natural Human-Robot Interaction
NatSGD serves as a foundational resource at the intersection of machine learning and HRI research, and we demonstrate its effectiveness in training robots to understand tasks through multimodal human commands, emphasizing the significance of jointly considering speech and gestures.
BU-NEmo: an Affective Dataset of Gun Violence News
As such, we introduce a U. S. gun violence news dataset that contains news headline and image pairings from 840 news articles with 15K high-quality, crowdsourced annotations on emotional responses to the news pairings.
