no code implementations • 19 Feb 2024 • Markus Hiller, Krista A. Ehinger, Tom Drummond
We present a novel bi-directional Transformer architecture (BiXT) which scales linearly with input size in terms of computational cost and memory consumption, but does not suffer the drop in performance or limitation to only one input modality seen with other efficient Transformer-based approaches.
1 code implementation • 15 Jan 2024 • Chao Lei, Nir Lipovetzky, Krista A. Ehinger
The Abstraction and Reasoning Corpus (ARC) is a general artificial intelligence benchmark that poses difficulties for pure machine learning methods due to its requirement for fluid intelligence with a focus on reasoning and abstraction.
no code implementations • 26 Oct 2023 • Zhenkai Zhang, Krista A. Ehinger, Tom Drummond
This paper introduces two key contributions aimed at improving the speed and quality of images generated through inverse diffusion processes.
no code implementations • 3 Jul 2023 • Chao Lei, Nir Lipovetzky, Krista A. Ehinger
It has been shown recently that successful techniques in classical planning, such as goal-oriented heuristics and landmarks, can improve the ability to compute planning programs for generalized planning (GP) problems.
1 code implementation • 12 Mar 2023 • Jiayang Ao, Qiuhong Ke, Krista A. Ehinger
Images of realistic scenes often contain intra-class objects that are heavily occluded from each other, making the amodal perception task that requires parsing the occluded parts of the objects challenging.
1 code implementation • CVPR 2023 • Steven Spratley, Krista A. Ehinger, Tim Miller
While progressive-matrix problems (PMPs) are becoming popular for the development and evaluation of analogical reasoning in computer vision, we argue that the dominant methodology in this area struggles to expose the lack of meaningful generalisation in solvers, and reinforces an objectivist stance on perception -- that objects can only be seen one way -- which we believe to be counter-productive.
no code implementations • 5 Jul 2022 • Jiayang Ao, Qiuhong Ke, Krista A. Ehinger
The main purpose of this survey is to provide an intuitive understanding of the research hotspots, key technologies and future trends in the field of image amodal completion.
1 code implementation • 27 Jun 2020 • Ruihan Zhang, Prashan Madumal, Tim Miller, Krista A. Ehinger, Benjamin I. P. Rubinstein
Based on the requirements of fidelity (approximate models to target models) and interpretability (being meaningful to people), we design measurements and evaluate a range of matrix factorization methods with our framework.
1 code implementation • 25 Apr 2015 • Pingmei Xu, Krista A. Ehinger, yinda zhang, Adam Finkelstein, Sanjeev R. Kulkarni, Jianxiong Xiao
Traditional eye tracking requires specialized hardware, which means collecting gaze data from many observers is expensive, tedious and slow.