7 citations · 12 across the 6 of their papers we have counts for
7 papers
NICE: CVPR 2023 Challenge on Zero-shot Image Captioning
Taehoon Kim, Pyunghwan Ahn, Sangyun Kim +39
In this report, we introduce NICE (New frontiers for zero-shot Image Captioning Evaluation) project and share the results and outcomes of 2023 challenge. This project is designed t…
Large-Scale Bidirectional Training for Zero-Shot Image Captioning
Taehoon Kim, Mark Marsden, Pyunghwan Ahn +4
When trained on large-scale datasets, image captioning models can understand the content of images from a general domain but often fail to generate accurate, detailed captions. To…
Investigating Class-level Difficulty Factors in Multi-label Classification Problems
Mark Marsden, Kevin McGuinness, Joseph Antony +6
This work investigates the use of class-level difficulty factors in multi-label classification problems for the first time. Four class-level difficulty factors are proposed: freque…
People, Penguins and Petri Dishes: Adapting Object Counting Models To New Visual Domains And Object Types Without Forgetting
Mark Marsden, Kevin McGuinness, Suzanne Little +2
In this paper we propose a technique to adapt a convolutional neural network (CNN) based object counter to additional visual domains and object types while still preserving the ori…
ResnetCrowd: A Residual Deep Learning Architecture for Crowd Counting, Violent Behaviour Detection and Crowd Density Level Classification
Mark Marsden, Kevin McGuinness, Suzanne Little +1
In this paper we propose ResnetCrowd, a deep residual architecture for simultaneous crowd counting, violent behaviour detection and crowd density level classification. To train and…
Fully Convolutional Crowd Counting On Highly Congested Scenes
Mark Marsden, Kevin McGuinness, Suzanne Little +1
In this paper we advance the state-of-the-art for crowd counting in high density scenes by further exploring the idea of a fully convolutional crowd counting model introduced by (Z…