86 research outputs found
Learning Second-Order Attentive Context for Efficient Correspondence Pruning
Correspondence pruning aims to search consistent correspondences (inliers)
from a set of putative correspondences. It is challenging because of the
disorganized spatial distribution of numerous outliers, especially when
putative correspondences are largely dominated by outliers. It's more
challenging to ensure effectiveness while maintaining efficiency. In this
paper, we propose an effective and efficient method for correspondence pruning.
Inspired by the success of attentive context in correspondence problems, we
first extend the attentive context to the first-order attentive context and
then introduce the idea of attention in attention (ANA) to model second-order
attentive context for correspondence pruning. Compared with first-order
attention that focuses on feature-consistent context, second-order attention
dedicates to attention weights itself and provides an additional source to
encode consistent context from the attention map. For efficiency, we derive two
approximate formulations for the naive implementation of second-order attention
to optimize the cubic complexity to linear complexity, such that second-order
attention can be used with negligible computational overheads. We further
implement our formulations in a second-order context layer and then incorporate
the layer in an ANA block. Extensive experiments demonstrate that our method is
effective and efficient in pruning outliers, especially in high-outlier-ratio
cases. Compared with the state-of-the-art correspondence pruning approach
LMCNet, our method runs 14 times faster while maintaining a competitive
accuracy.Comment: 9 pages, 8 figures; Accepted to AAAI 2023 (Oral
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Advances in Probabilistic Meta-Learning and the Neural Process Family
A natural progression in machine learning research is to automate and learn from data increasingly many components of our learning agents.Meta-learning is a paradigm that fully embraces this perspective, and can be intuitively described as embodying the idea of learning to learn. A goal of meta-learning research is the development of models to assist users in navigating the intricate space of design choices associated with specifying machine learning solutions. This space is particularly formidable when considering deep learning approaches, which involve myriad design choices interacting in complex fashions to affect the performance of the resulting agents. Despite the impressive successes of deep learning in recent years, this challenge remains a significant bottleneck in deploying neural network based solutions in several important application domains. But how can we reason about and design solutions to this daunting task?
This thesis is concerned with a particular perspective for meta-learning in supervised settings. We view supervised learning algorithms as mappings that take data sets to predictive models, and consider meta-learning as learning to approximate functions of this form. In particular, we are interested in meta-learners that (i) employ neural networks to approximate these functions in an end-to-end manner, and (ii) provide predictive distributions rather than single predictors. The former is motivated by the success of neural networks as function approximators, and the latter by our interest in the few-shot learning scenario. The introductory chapters of this thesis formalise this notion, and use it to provide a tutorial introducing the Neural Process Family (NPF), a class of models introduced by Garnelo et al (2018) satisfying the above-mentioned modelling desiderata. We then present our own technical contributions to the NPF.
First, we focus on fundamental properties of the model-class, such as expressivity and limiting behaviours of the associated training procedures. Next, we study the role of translation equivariance in the NPF. Considering the intimate relationship between the NPF and the representation of functions operating on sets, we extend the underlying theory of DeepSets to include translation equivariance. We then develop novel members of the NPF endowed with this important inductive bias. Through extensive empirical evaluation, we demonstrate that, in many settings, they significantly outperform their non-equivariant counterparts.
Finally, we turn our attention to the development of Neural Processes for few-shot image-classification. We introduce models that navigate the important tradeoffs associated with this setting, and describe the specification of their central components. We demonstrate that the resulting models---CNAPs---achieve state-of-the-art performance on a challenging benchmark called Meta-Dataset, while adapting faster and with less computational overhead than their best-performing competitors
GECCO: Geometrically-Conditioned Point Diffusion Models
Diffusion models generating images conditionally on text, such as Dall-E 2
and Stable Diffusion, have recently made a splash far beyond the computer
vision community. Here, we tackle the related problem of generating point
clouds, both unconditionally, and conditionally with images. For the latter, we
introduce a novel geometrically-motivated conditioning scheme based on
projecting sparse image features into the point cloud and attaching them to
each individual point, at every step in the denoising process. This approach
improves geometric consistency and yields greater fidelity than current methods
relying on unstructured, global latent codes. Additionally, we show how to
apply recent continuous-time diffusion schemes. Our method performs on par or
above the state of art on conditional and unconditional experiments on
synthetic data, while being faster, lighter, and delivering tractable
likelihoods. We show it can also scale to diverse indoors scenes
Fully Differentiable RANSAC
We propose the fully differentiable -RANSAC.It predicts the inlier
probabilities of the input data points, exploits the predictions in a guided
sampler, and estimates the model parameters (e.g., fundamental matrix) and its
quality while propagating the gradients through the entire procedure. The
random sampler in -RANSAC is based on a clever re-parametrization
strategy, i.e.\ the Gumbel Softmax sampler, that allows propagating the
gradients directly into the subsequent differentiable minimal solver. The model
quality function marginalizes over the scores from all models estimated within
-RANSAC to guide the network learning accurate and useful
probabilities.-RANSAC is the first to unlock the end-to-end training of
geometric estimation pipelines, containing feature detection, matching and
RANSAC-like randomized robust estimation. As a proof of its potential, we train
-RANSAC together with LoFTR, i.e. a recent detector-free feature
matcher, to find reliable correspondences in an end-to-end manner. We test
-RANSAC on a number of real-world datasets on fundamental and essential
matrix estimation. It is superior to the state-of-the-art in terms of accuracy
while being among the fastest methods. The code and trained models will be made
public
The Catalog Problem:Deep Learning Methods for Transforming Sets into Sequences of Clusters
The titular Catalog Problem refers to predicting a varying number of ordered clusters from sets of any cardinality. This task arises in many diverse areas, ranging from medical triage, through multi-channel signal analysis for petroleum exploration to product catalog structure prediction. This thesis focuses on the latter, which exemplifies a number of challenges inherent to ordered clustering. These include learning variable cluster constraints, exhibiting relational reasoning and managing combinatorial complexity. All of which present unique challenges for neural networks, combining elements of set representation, neural clustering and permutation learning.In order to approach the Catalog Problem, a curated dataset of over ten thousand real-world product catalogs consisting of more than one million product offers is provided. Additionally, a library for generating simpler, synthetic catalog structures is presented. These and other datasets form the foundation of the included work, allowing for a quantitative comparison of the proposed methods’ ability to address the underlying challenge. In particular, synthetic datasets enable the assessment of the models’ capacity to learn higher order compositional and structural rules.Two novel neural methods are proposed to tackle the Catalog Problem, a set encoding module designed to enhance the network’s ability to condition the prediction on the entirety of the input set, and a larger architecture for inferring an input- dependent number of diverse, ordered partitional clusters with an added cardinality prediction module. Both result in an improved performance on the presented datasets, with the latter being the only neural method fulfilling all requirements inherent to addressing the Catalog Problem
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