2 citations · 3 across the 5 of their papers we have counts for
5 papers
OmniGCD: Abstracting Generalized Category Discovery for Modality Agnosticism
Jordan Shipard, Arnold Wiliem, Kien Nguyen Thanh +2
Generalized Category Discovery (GCD) challenges methods to identify known and novel classes using partially labeled data, mirroring human category learning. Unlike prior GCD method…
Zoom-shot: Fast and Efficient Unsupervised Zero-Shot Transfer of CLIP to Vision Encoders with Multimodal Loss
Jordan Shipard, Arnold Wiliem, Kien Nguyen Thanh +2
The fusion of vision and language has brought about a transformative shift in computer vision through the emergence of Vision-Language Models (VLMs). However, the resource-intensiv…
SafeSea: Synthetic Data Generation for Adverse & Low Probability Maritime Conditions
Martin Tran, Jordan Shipard, Hermawan Mulyono +2
High-quality training data is essential for enhancing the robustness of object detection models. Within the maritime domain, obtaining a diverse real image dataset is particularly…
Diversity is Definitely Needed: Improving Model-Agnostic Zero-shot Classification via Stable Diffusion
Jordan Shipard, Arnold Wiliem, Kien Nguyen Thanh +2
In this work, we investigate the problem of Model-Agnostic Zero-Shot Classification (MA-ZSC), which refers to training non-specific classification architectures (downstream models)…
Does Interference Exist When Training a Once-For-All Network?
Jordan Shipard, Arnold Wiliem, Clinton Fookes
The Once-For-All (OFA) method offers an excellent pathway to deploy a trained neural network model into multiple target platforms by utilising the supernet-subnet architecture. Onc…