Few-shot Image Classification based on Gradual Machine Learning
- URL: http://arxiv.org/abs/2307.15524v1
- Date: Fri, 28 Jul 2023 12:30:41 GMT
- Title: Few-shot Image Classification based on Gradual Machine Learning
- Authors: Na Chen, Xianming Kuang, Feiyu Liu, Kehao Wang and Qun Chen
- Abstract summary: Few-shot image classification aims to accurately classify unlabeled images using only a few labeled samples.
We propose a novel approach based on the non-i.i.d paradigm of gradual machine learning (GML)
We show that the proposed approach can improve the SOTA performance by 1-5% in terms of accuracy.
- Score: 6.935034849731568
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Few-shot image classification aims to accurately classify unlabeled images
using only a few labeled samples. The state-of-the-art solutions are built by
deep learning, which focuses on designing increasingly complex deep backbones.
Unfortunately, the task remains very challenging due to the difficulty of
transferring the knowledge learned in training classes to new ones. In this
paper, we propose a novel approach based on the non-i.i.d paradigm of gradual
machine learning (GML). It begins with only a few labeled observations, and
then gradually labels target images in the increasing order of hardness by
iterative factor inference in a factor graph. Specifically, our proposed
solution extracts indicative feature representations by deep backbones, and
then constructs both unary and binary factors based on the extracted features
to facilitate gradual learning. The unary factors are constructed based on
class center distance in an embedding space, while the binary factors are
constructed based on k-nearest neighborhood. We have empirically validated the
performance of the proposed approach on benchmark datasets by a comparative
study. Our extensive experiments demonstrate that the proposed approach can
improve the SOTA performance by 1-5% in terms of accuracy. More notably, it is
more robust than the existing deep models in that its performance can
consistently improve as the size of query set increases while the performance
of deep models remains essentially flat or even becomes worse.
Related papers
- Evaluation of Confidence-based Ensembling in Deep Learning Image
Classification [0.6445605125467573]
Conf-Ensemble is an adaptation to Boosting to create ensembles based on model confidence instead of model errors.
We evaluate the Conf-Ensemble approach in the much more complex task of image classification with the ImageNet dataset.
arXiv Detail & Related papers (2023-03-03T16:29:22Z) - Fine-Grained Visual Classification using Self Assessment Classifier [12.596520707449027]
Extracting discriminative features plays a crucial role in the fine-grained visual classification task.
In this paper, we introduce a Self Assessment, which simultaneously leverages the representation of the image and top-k prediction classes.
We show that our method achieves new state-of-the-art results on CUB200-2011, Stanford Dog, and FGVC Aircraft datasets.
arXiv Detail & Related papers (2022-05-21T07:41:27Z) - CAD: Co-Adapting Discriminative Features for Improved Few-Shot
Classification [11.894289991529496]
Few-shot classification is a challenging problem that aims to learn a model that can adapt to unseen classes given a few labeled samples.
Recent approaches pre-train a feature extractor, and then fine-tune for episodic meta-learning.
We propose a strategy to cross-attend and re-weight discriminative features for few-shot classification.
arXiv Detail & Related papers (2022-03-25T06:14:51Z) - Learning What Not to Segment: A New Perspective on Few-Shot Segmentation [63.910211095033596]
Recently few-shot segmentation (FSS) has been extensively developed.
This paper proposes a fresh and straightforward insight to alleviate the problem.
In light of the unique nature of the proposed approach, we also extend it to a more realistic but challenging setting.
arXiv Detail & Related papers (2022-03-15T03:08:27Z) - A Contrastive Distillation Approach for Incremental Semantic
Segmentation in Aerial Images [15.75291664088815]
A major issue concerning current deep neural architectures is known as catastrophic forgetting.
We propose a contrastive regularization, where any given input is compared with its augmented version.
We show the effectiveness of our solution on the Potsdam dataset, outperforming the incremental baseline in every test.
arXiv Detail & Related papers (2021-12-07T16:44:45Z) - Distribution Alignment: A Unified Framework for Long-tail Visual
Recognition [52.36728157779307]
We propose a unified distribution alignment strategy for long-tail visual recognition.
We then introduce a generalized re-weight method in the two-stage learning to balance the class prior.
Our approach achieves the state-of-the-art results across all four recognition tasks with a simple and unified framework.
arXiv Detail & Related papers (2021-03-30T14:09:53Z) - Revisiting Deep Local Descriptor for Improved Few-Shot Classification [56.74552164206737]
We show how one can improve the quality of embeddings by leveraging textbfDense textbfClassification and textbfAttentive textbfPooling.
We suggest to pool feature maps by applying attentive pooling instead of the widely used global average pooling (GAP) to prepare embeddings for few-shot classification.
arXiv Detail & Related papers (2021-03-30T00:48:28Z) - Few-shot Action Recognition with Prototype-centered Attentive Learning [88.10852114988829]
Prototype-centered Attentive Learning (PAL) model composed of two novel components.
First, a prototype-centered contrastive learning loss is introduced to complement the conventional query-centered learning objective.
Second, PAL integrates a attentive hybrid learning mechanism that can minimize the negative impacts of outliers.
arXiv Detail & Related papers (2021-01-20T11:48:12Z) - One-Shot Object Detection without Fine-Tuning [62.39210447209698]
We introduce a two-stage model consisting of a first stage Matching-FCOS network and a second stage Structure-Aware Relation Module.
We also propose novel training strategies that effectively improve detection performance.
Our method exceeds the state-of-the-art one-shot performance consistently on multiple datasets.
arXiv Detail & Related papers (2020-05-08T01:59:23Z) - Distilling Localization for Self-Supervised Representation Learning [82.79808902674282]
Contrastive learning has revolutionized unsupervised representation learning.
Current contrastive models are ineffective at localizing the foreground object.
We propose a data-driven approach for learning in variance to backgrounds.
arXiv Detail & Related papers (2020-04-14T16:29:42Z) - Reinforced active learning for image segmentation [34.096237671643145]
We present a new active learning strategy for semantic segmentation based on deep reinforcement learning (RL)
An agent learns a policy to select a subset of small informative image regions -- opposed to entire images -- to be labeled from a pool of unlabeled data.
Our method proposes a new modification of the deep Q-network (DQN) formulation for active learning, adapting it to the large-scale nature of semantic segmentation problems.
arXiv Detail & Related papers (2020-02-16T14:03:06Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.