198 citations · 382 across the 33 of their papers we have counts for
37 papers
How Important is Importance Sampling for Deep Budgeted Training?
Eric Arazo, Diego Ortego, Paul Albert +2
Long iterative training processes for Deep Neural Networks (DNNs) are commonly required to achieve state-of-the-art performance in many computer vision tasks. Importance sampling a…
Semi-supervised dry herbage mass estimation using automatic data and synthetic images
Paul Albert, Mohamed Saadeldin, Badri Narayanan +5
Monitoring species-specific dry herbage biomass is an important aspect of pasture-based milk production systems. Being aware of the herbage biomass in the field enables farmers to…
Addressing out-of-distribution label noise in webly-labelled data
Paul Albert, Diego Ortego, Eric Arazo +2
A recurring focus of the deep learning community is towards reducing the labeling effort. Data gathering and annotation using a search engine is a simple alternative to generating…
Discerning Generic Event Boundaries in Long-Form Wild Videos
Ayush K Rai, Tarun Krishna, Julia Dietlmeier +3
Detecting generic, taxonomy-free event boundaries invideos represents a major stride forward towards holisticvideo understanding. In this paper we present a technique forgeneric ev…
PixInWav: Residual Steganography for Hiding Pixels in Audio
Margarita Geleta, Cristina Punti, Kevin McGuinness +3
Steganography comprises the mechanics of hiding data in a host media that may be publicly available. While previous works focused on unimodal setups (e.g., hiding images in images,…
Evaluating Contrastive Models for Instance-based Image Retrieval
Tarun Krishna, Kevin McGuinness, Noel O'Connor
In this work, we evaluate contrastive models for the task of image retrieval. We hypothesise that models that are learned to encode semantic similarity among instances via discrimi…