17 research outputs found
Adaptive Anchor Label Propagation for Transductive Few-Shot Learning
Few-shot learning addresses the issue of classifying images using limited
labeled data. Exploiting unlabeled data through the use of transductive
inference methods such as label propagation has been shown to improve the
performance of few-shot learning significantly. Label propagation infers
pseudo-labels for unlabeled data by utilizing a constructed graph that exploits
the underlying manifold structure of the data. However, a limitation of the
existing label propagation approaches is that the positions of all data points
are fixed and might be sub-optimal so that the algorithm is not as effective as
possible. In this work, we propose a novel algorithm that adapts the feature
embeddings of the labeled data by minimizing a differentiable loss function
optimizing their positions in the manifold in the process. Our novel algorithm,
Adaptive Anchor Label Propagation}, outperforms the standard label propagation
algorithm by as much as 7% and 2% in the 1-shot and 5-shot settings
respectively. We provide experimental results highlighting the merits of our
algorithm on four widely used few-shot benchmark datasets, namely miniImageNet,
tieredImageNet, CUB and CIFAR-FS and two commonly used backbones, ResNet12 and
WideResNet-28-10. The source code can be found at
https://github.com/MichalisLazarou/A2LP.Comment: published in ICIP 202
Robust Transductive Few-shot Learning via Joint Message Passing and Prototype-based Soft-label Propagation
Few-shot learning (FSL) aims to develop a learning model with the ability to
generalize to new classes using a few support samples. For transductive FSL
tasks, prototype learning and label propagation methods are commonly employed.
Prototype methods generally first learn the representative prototypes from the
support set and then determine the labels of queries based on the metric
between query samples and prototypes. Label propagation methods try to
propagate the labels of support samples on the constructed graph encoding the
relationships between both support and query samples. This paper aims to
integrate these two principles together and develop an efficient and robust
transductive FSL approach, termed Prototype-based Soft-label Propagation
(PSLP). Specifically, we first estimate the soft-label presentation for each
query sample by leveraging prototypes. Then, we conduct soft-label propagation
on our learned query-support graph. Both steps are conducted progressively to
boost their respective performance. Moreover, to learn effective prototypes for
soft-label estimation as well as the desirable query-support graph for
soft-label propagation, we design a new joint message passing scheme to learn
sample presentation and relational graph jointly. Our PSLP method is
parameter-free and can be implemented very efficiently. On four popular
datasets, our method achieves competitive results on both balanced and
imbalanced settings compared to the state-of-the-art methods. The code will be
released upon acceptance
MetaNODE: Prototype Optimization as a Neural ODE for Few-Shot Learning
Few-Shot Learning (FSL) is a challenging task, \emph{i.e.}, how to recognize
novel classes with few examples? Pre-training based methods effectively tackle
the problem by pre-training a feature extractor and then predicting novel
classes via a cosine nearest neighbor classifier with mean-based prototypes.
Nevertheless, due to the data scarcity, the mean-based prototypes are usually
biased. In this paper, we attempt to diminish the prototype bias by regarding
it as a prototype optimization problem. To this end, we propose a novel
meta-learning based prototype optimization framework to rectify prototypes,
\emph{i.e.}, introducing a meta-optimizer to optimize prototypes. Although the
existing meta-optimizers can also be adapted to our framework, they all
overlook a crucial gradient bias issue, \emph{i.e.}, the mean-based gradient
estimation is also biased on sparse data. To address the issue, we regard the
gradient and its flow as meta-knowledge and then propose a novel Neural
Ordinary Differential Equation (ODE)-based meta-optimizer to polish prototypes,
called MetaNODE. In this meta-optimizer, we first view the mean-based
prototypes as initial prototypes, and then model the process of prototype
optimization as continuous-time dynamics specified by a Neural ODE. A gradient
flow inference network is carefully designed to learn to estimate the
continuous gradient flow for prototype dynamics. Finally, the optimal
prototypes can be obtained by solving the Neural ODE. Extensive experiments on
miniImagenet, tieredImagenet, and CUB-200-2011 show the effectiveness of our
method.Comment: Accepted by AAAI 202
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification
Transductive Few-Shot learning has gained increased attention nowadays
considering the cost of data annotations along with the increased accuracy
provided by unlabelled samples in the domain of few shot. Especially in
Few-Shot Classification (FSC), recent works explore the feature distributions
aiming at maximizing likelihoods or posteriors with respect to the unknown
parameters. Following this vein, and considering the parallel between FSC and
clustering, we seek for better taking into account the uncertainty in
estimation due to lack of data, as well as better statistical properties of the
clusters associated with each class. Therefore in this paper we propose a new
clustering method based on Variational Bayesian inference, further improved by
Adaptive Dimension Reduction based on Probabilistic Linear Discriminant
Analysis. Our proposed method significantly improves accuracy in the realistic
unbalanced transductive setting on various Few-Shot benchmarks when applied to
features used in previous studies, with a gain of up to in accuracy. In
addition, when applied to balanced setting, we obtain very competitive results
without making use of the class-balance artefact which is disputable for
practical use cases. We also provide the performance of our method on a high
performing pretrained backbone, with the reported results further surpassing
the current state-of-the-art accuracy, suggesting the genericity of the
proposed method
A Strong Baseline for Generalized Few-Shot Semantic Segmentation
This paper introduces a generalized few-shot segmentation framework with a
straightforward training process and an easy-to-optimize inference phase. In
particular, we propose a simple yet effective model based on the well-known
InfoMax principle, where the Mutual Information (MI) between the learned
feature representations and their corresponding predictions is maximized. In
addition, the terms derived from our MI-based formulation are coupled with a
knowledge distillation term to retain the knowledge on base classes. With a
simple training process, our inference model can be applied on top of any
segmentation network trained on base classes. The proposed inference yields
substantial improvements on the popular few-shot segmentation benchmarks
PASCAL- and COCO-. Particularly, for novel classes, the improvement
gains range from 5% to 20% (PASCAL-) and from 2.5% to 10.5% (COCO-)
in the 1-shot and 5-shot scenarios, respectively. Furthermore, we propose a
more challenging setting, where performance gaps are further exacerbated. Our
code is publicly available at https://github.com/sinahmr/DIaM.Comment: 13 pages, 4 figure