14,608 research outputs found
Not All Instances Contribute Equally: Instance-adaptive Class Representation Learning for Few-Shot Visual Recognition
Few-shot visual recognition refers to recognize novel visual concepts from a
few labeled instances. Many few-shot visual recognition methods adopt the
metric-based meta-learning paradigm by comparing the query representation with
class representations to predict the category of query instance. However,
current metric-based methods generally treat all instances equally and
consequently often obtain biased class representation, considering not all
instances are equally significant when summarizing the instance-level
representations for the class-level representation. For example, some instances
may contain unrepresentative information, such as too much background and
information of unrelated concepts, which skew the results. To address the above
issues, we propose a novel metric-based meta-learning framework termed
instance-adaptive class representation learning network (ICRL-Net) for few-shot
visual recognition. Specifically, we develop an adaptive instance revaluing
network with the capability to address the biased representation issue when
generating the class representation, by learning and assigning adaptive weights
for different instances according to their relative significance in the support
set of corresponding class. Additionally, we design an improved bilinear
instance representation and incorporate two novel structural losses, i.e.,
intra-class instance clustering loss and inter-class representation
distinguishing loss, to further regulate the instance revaluation process and
refine the class representation. We conduct extensive experiments on four
commonly adopted few-shot benchmarks: miniImageNet, tieredImageNet, CIFAR-FS,
and FC100 datasets. The experimental results compared with the state-of-the-art
approaches demonstrate the superiority of our ICRL-Net
Pseudo-Labeling Based Practical Semi-Supervised Meta-Training for Few-Shot Learning
Most existing few-shot learning (FSL) methods require a large amount of
labeled data in meta-training, which is a major limit. To reduce the
requirement of labels, a semi-supervised meta-training setting has been
proposed for FSL, which includes only a few labeled samples and numbers of
unlabeled samples in base classes. However, existing methods under this setting
require class-aware sample selection from the unlabeled set, which violates the
assumption of unlabeled set. In this paper, we propose a practical
semi-supervised meta-training setting with truly unlabeled data. Under the new
setting, the performance of existing methods drops notably. To better utilize
both the labeled and truly unlabeled data, we propose a simple and effective
meta-training framework, called pseudo-labeling based on meta-learning (PLML).
Firstly, we train a classifier via common semi-supervised learning (SSL) and
use it to obtain the pseudo-labels of unlabeled data. Then we build few-shot
tasks from labeled and pseudo-labeled data and run meta-learning over the
constructed tasks to learn the FSL model. Surprisingly, through extensive
experiments across two FSL datasets, we find that this simple meta-training
framework effectively prevents the performance degradation of FSL under limited
labeled data. Besides, benefiting from meta-training, the proposed method
improves the classifiers learned by two representative SSL algorithms as well
Is higher-order evidence evidence?
Suppose we learn that we have a poor track record in forming beliefs rationally, or that a brilliant colleague thinks that we believe P irrationally. Does such input require us to revise those beliefs whose rationality is in question? When we gain information suggesting that our beliefs are irrational, we are in one of two general cases. In the first case we made no error, and our beliefs are rational. In that case the input to the contrary is misleading. In the second case we indeed believe irrationally, and our original evidence already requires us to fix our mistake. In that case the input to that effect is normatively superfluous. Thus, we know that information suggesting that our beliefs are irrational is either misleading or superfluous. This, I submit, renders the input incapable of justifying belief revision, despite our not knowing which of the two kinds it is
Syntax-aware Hybrid prompt model for Few-shot multi-modal sentiment analysis
Multimodal Sentiment Analysis (MSA) has been a popular topic in natural
language processing nowadays, at both sentence and aspect level. However, the
existing approaches almost require large-size labeled datasets, which bring
about large consumption of time and resources. Therefore, it is practical to
explore the method for few-shot sentiment analysis in cross-modalities.
Previous works generally execute on textual modality, using the prompt-based
methods, mainly two types: hand-crafted prompts and learnable prompts. The
existing approach in few-shot multi-modality sentiment analysis task has
utilized both methods, separately. We further design a hybrid pattern that can
combine one or more fixed hand-crafted prompts and learnable prompts and
utilize the attention mechanisms to optimize the prompt encoder. The
experiments on both sentence-level and aspect-level datasets prove that we get
a significant outperformance
Detecting Misinformation with LLM-Predicted Credibility Signals and Weak Supervision
Credibility signals represent a wide range of heuristics that are typically
used by journalists and fact-checkers to assess the veracity of online content.
Automating the task of credibility signal extraction, however, is very
challenging as it requires high-accuracy signal-specific extractors to be
trained, while there are currently no sufficiently large datasets annotated
with all credibility signals. This paper investigates whether large language
models (LLMs) can be prompted effectively with a set of 18 credibility signals
to produce weak labels for each signal. We then aggregate these potentially
noisy labels using weak supervision in order to predict content veracity. We
demonstrate that our approach, which combines zero-shot LLM credibility signal
labeling and weak supervision, outperforms state-of-the-art classifiers on two
misinformation datasets without using any ground-truth labels for training. We
also analyse the contribution of the individual credibility signals towards
predicting content veracity, which provides new valuable insights into their
role in misinformation detection
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
- …