CVPR is a world-renowned international Artificial Intelligence (AI) conference co-hosted by the
Of the thesis papers submitted by
The first of these two oral presentations from the Toronto AI Center will focus on their paper 'P3IV: Probabilistic Procedure Planning from Instructional Videos with Weak Supervision', a study done on how to build next-level AI systems capable of analyzing and mimicking human behavior. Procedure planning is gaining attention in the field, as it could lead to technologies capable of assisting humans in solving goal-directed problems, such as cooking food or installing and repairing devices.
The research team's approach undercuts the previous requirement of costly data annotations that the start and end times of each intermediate instructional step were labeled with. Instead, the new approach allows AI to learn from natural language instructions, sourced from the internet for example, and predict the intermediate steps. Additionally, the model is enhanced with a probabilistic generative module to handle the uncertainty inherent to procedural planning.
? A section from the presentation for 'P3IV: Probabilistic Procedure Planning from Instructional Videos with Weak Supervision' by the Toronto AI Center
The second oral presentation to be given by the Toronto AI Center is a study on 'Day-to-Night Image Synthesis for Training Nighttime Neural ISPs'. This study is focused on how to synthesize the nighttime image data needed to train
? A visual from 'Day-to-Night Image Synthesis for Training Nighttime Neural ISPs' by the Toronto AI Center
As well as the two thesis papers submitted by the Toronto AI Center, other global
Two papers submitted by the Moscow AI Center were accepted into the conference. The first is a study on what is currently the world's most competitive Single-View Depth Estimation (SVDE). This study on depth estimation - a research area that concerns many forms of image manipulation, generation and analysis - has gained attention due to its high accuracy. Unlike its predecessors which require resource-intensive post-processing, the proposed GP2 (General-Purpose and Geometry-Preserving) SVDE approach demonstrates outstanding capabilities without the need for this post-processing.
The second paper, 'Stereo Magnification with Multi-Layer Images', is a study of a novel method of 3D photo synthesis. Unlike existing methods of 3D photo synthesis, which necessitate high-capacity memory and processing abilities, the method championed in this paper can be applied to mobile devices as well, thanks to a drastically increased memory efficiency which has not caused accuracy or processing effectiveness to suffer.
The Cambridge AI Center's paper on 'Gaussian Process Modeling of Approximate Inference Errors for Variational Autoencoders' achieves state-of-the-art performance by proposing a novel Gaussian Process (GP) modeling method. This enables test time inference using a single feed forward pass in Variational Autoencoder (VAE).
They also introduced the paper titled 'Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference'. In this paper, the research team proposes a novel neural network based on innovative transformer architecture for the few-shot learning which is a representative method for dealing with situations where data has scarce labelling.
These achievements, among many others, help emphasize
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