arXiv research

D$^2$-City is a diverse traffic dataset for AI development.

Paper introduces TAP-Vid, a benchmark for tracking any point in videos.

Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. Most autonomous vehicles, however, carry a combinatio…

Ultrasound (US) is the most widely used fetal imaging technique. However, US images have limited capture range, and suffer from view dependent artefacts such as acoustic shadows. Compounding of overlapping 3D US acquisitions into a high-resolution volume can extend the field of view and remove image artefacts, which is…

LISBET automates social behavior analysis using machine learning.

Wearable smart suit tracks infant movements with high accuracy.

Structured learning is appropriate when predicting structured outputs such as trees, graphs, or sequences. Most prior work requires the training set to consist of complete trees, graphs or sequences. Specifying such detailed ground truth can be tedious or infeasible for large outputs. Our main contribution is a large m…

Automated radar data labeling using GNSS improves efficiency without sacrificing accuracy.

New architecture tracks objects in cluttered scenes without supervision.

Neural networks promise to bring robust, quantitative analysis to medical fields, but adoption is limited by the technicalities of training these networks. To address this translation gap between medical researchers and neural networks in the field of pathology, we have created an intuitive interface which utilizes the…

SoccerCPD detects tactical changes in soccer matches using spatiotemporal tracking data.

The paper extends influence functions to sequence tagging tasks for better model interpretability.

This paper clusters networks with annotated time-series data using kernel-ARMA and Grassmannian geometry.

OpenEDS dataset captures eye images for VR applications.

Detecting a specific horizon in seismic images is a valuable tool for geological interpretation. Because hand-picking the locations of the horizon is a time-consuming process, automated computational methods were developed starting three decades ago. Older techniques for such picking include interpolation of control po…

Deep learning maps tongue movements to speech sounds for voiceless individuals.

Understanding procedural text requires tracking entities, actions and effects as the narrative unfolds. We focus on the challenging real-world problem of action-graph extraction from material science papers, where language is highly specialized and data annotation is expensive and scarce. We propose a novel approach, T…

Interactive weak supervision learns useful heuristics from user feedback.

Paper tackles musical version matching at segment level using contrastive learning from weakly-labeled data.

Study shows annotation instrument design affects model performance in hate speech detection.

Active learning selects both observations and annotation precision for Gaussian Processes.

One of the problems on the way to successful implementation of neural networks is the quality of annotation. For instance, different annotators can annotate images in a different way and very often their decisions do not match exactly and in extreme cases are even mutually exclusive which results in noisy annotations a…

Generative model combines multi-dimensional annotations for more accurate ground truth estimation.

Challenge evaluates semantic code search using annotated corpus.

Survey on AL strategies for cost-effective annotation in classification.

RAD improves robustness to domain annotation noise without explicit domain annotations.

Accurate annotation of medical image is the crucial step for image AI clinical application. However, annotating medical image will incur a great deal of annotation effort and expense due to its high complexity and needing experienced doctors. To alleviate annotation cost, some active learning methods are proposed. But …

Large financial dataset tracks FOMC communications and their impact.

New system for automatic music emotion recognition considers multiple emotions simultaneously.

The study challenges the notion that partial data annotation is inferior, suggesting it can sometimes outperform complete annotation.

A new method uses triplet embeddings to improve human annotation for hidden constructs.

PTBCC improves accuracy in multi-class annotation aggregation by learning from prototype confusion matrices.

ActiveLab improves classifier accuracy with fewer annotations by re-labeling.

Bayesian methods improve text annotation quality.

Paper proposes an efficient method for bounding box annotation in object detection.

Paper proposes a weak supervision technique for CNN semantic segmentation of lung diseases using partially annotated data.

Annotation guidelines used to guide the annotation of training and evaluation datasets can have a considerable impact on the quality of machine learning models. In this study, we explore the effects of annotation guidelines on the quality of app feature extraction models. As a main result, we propose several changes to…

Improves off-policy evaluation with imperfect annotations.

AxCell automates results extraction from machine learning papers.

A popular approach for large scale data annotation tasks is crowdsourcing, wherein each data point is labeled by multiple noisy annotators. We consider the problem of inferring ground truth from noisy ordinal labels obtained from multiple annotators of varying and unknown expertise levels. Annotation models for ordinal…

Crowdlab uses classifiers to estimate consensus labels and annotator quality.

The increase in data collection has made data annotation an interesting and valuable task in the contemporary world. This paper presents a new methodology for quickly annotating data using click-supervision and hierarchical object detection. The proposed work is semi-automatic in nature where the task of annotations is…

This paper presents a generic Bayesian framework that enables any deep learning model to actively learn from targeted crowds. Our framework inherits from recent advances in Bayesian deep learning, and extends existing work by considering the targeted crowdsourcing approach, where multiple annotators with unknown expert…

The paper proposes incentivizing human annotators with 'golden questions' to improve data quality.

Paper proposes active learning for sound event detection with reduced annotation effort.

The splendid success of convolutional neural networks (CNNs) in computer vision is largely attributable to the availability of massive annotated datasets, such as ImageNet and Places. However, in medical imaging, it is challenging to create such large annotated datasets, as annotating medical images is not only tedious…

Training 3D object detectors for autonomous driving has been limited to small datasets due to the effort required to generate annotations. Reducing both task complexity and the amount of task switching done by annotators is key to reducing the effort and time required to generate 3D bounding box annotations. This paper…

Ranking a set of objects involves establishing an order allowing for comparisons between any pair of objects in the set. Oftentimes, due to the unavailability of a ground truth of ranked orders, researchers resort to obtaining judgments from multiple annotators followed by inferring the ground truth based on the collec…