16 citations · 26 across the 5 of their papers we have counts for
7 papers
Towards Building Self-Aware Object Detectors via Reliable Uncertainty Quantification and Calibration
Kemal Oksuz, Tom Joy, Puneet K. Dokania
The current approach for testing the robustness of object detectors suffers from serious deficiencies such as improper methods of performing out-of-distribution detection and using…
Sample-dependent Adaptive Temperature Scaling for Improved Calibration
Tom Joy, Francesco Pinto, Ser-Nam Lim +2
It is now well known that neural networks can be wrong with high confidence in their predictions, leading to poor calibration. The most common post-hoc approach to compensate for t…
Learning Multimodal VAEs through Mutual Supervision
Tom Joy, Yuge Shi, Philip H. S. Torr +3
Multimodal VAEs seek to model the joint distribution over heterogeneous data (e.g.\ vision, language), whilst also capturing a shared representation across such modalities. Prior w…
Capturing Label Characteristics in VAEs
Tom Joy, Sebastian M. Schmon, Philip H. S. Torr +2
We present a principled approach to incorporating labels in VAEs that captures the rich characteristic information associated with those labels. While prior work has typically conf…
Real-Time Highly Accurate Dense Depth on a Power Budget using an FPGA-CPU Hybrid SoC
Oscar Rahnama, Tommaso Cavallari, Stuart Golodetz +5
Obtaining highly accurate depth from stereo images in real time has many applications across computer vision and robotics, but in some contexts, upper bounds on power consumption c…
Learning to Adapt for Stereo
Alessio Tonioni, Oscar Rahnama, Thomas Joy +3
Real world applications of stereo depth estimation require models that are robust to dynamic variations in the environment. Even though deep learning based stereo methods are succe…