99 research outputs found
Can Continual Learning Improve Long-Tailed Recognition? Toward a Unified Framework
The Long-Tailed Recognition (LTR) problem emerges in the context of learning
from highly imbalanced datasets, in which the number of samples among different
classes is heavily skewed. LTR methods aim to accurately learn a dataset
comprising both a larger Head set and a smaller Tail set. We propose a theorem
where under the assumption of strong convexity of the loss function, the
weights of a learner trained on the full dataset are within an upper bound of
the weights of the same learner trained strictly on the Head. Next, we assert
that by treating the learning of the Head and Tail as two separate and
sequential steps, Continual Learning (CL) methods can effectively update the
weights of the learner to learn the Tail without forgetting the Head. First, we
validate our theoretical findings with various experiments on the toy MNIST-LT
dataset. We then evaluate the efficacy of several CL strategies on multiple
imbalanced variations of two standard LTR benchmarks (CIFAR100-LT and
CIFAR10-LT), and show that standard CL methods achieve strong performance gains
in comparison to baselines and approach solutions that have been tailor-made
for LTR. We also assess the applicability of CL techniques on real-world data
by exploring CL on the naturally imbalanced Caltech256 dataset and demonstrate
its superiority over state-of-the-art classifiers. Our work not only unifies
LTR and CL but also paves the way for leveraging advances in CL methods to
tackle the LTR challenge more effectively
LCReg: Long-Tailed Image Classification with Latent Categories based Recognition
In this work, we tackle the challenging problem of long-tailed image
recognition. Previous long-tailed recognition approaches mainly focus on data
augmentation or re-balancing strategies for the tail classes to give them more
attention during model training. However, these methods are limited by the
small number of training images for the tail classes, which results in poor
feature representations. To address this issue, we propose the Latent
Categories based long-tail Recognition (LCReg) method. Our hypothesis is that
common latent features shared by head and tail classes can be used to improve
feature representation. Specifically, we learn a set of class-agnostic latent
features shared by both head and tail classes, and then use semantic data
augmentation on the latent features to implicitly increase the diversity of the
training sample. We conduct extensive experiments on five long-tailed image
recognition datasets, and the results show that our proposed method
significantly improves the baselines.Comment: accepted by Pattern Recognition. arXiv admin note: substantial text
overlap with arXiv:2206.0101
Ensemble Modeling for Multimodal Visual Action Recognition
In this work, we propose an ensemble modeling approach for multimodal action
recognition. We independently train individual modality models using a variant
of focal loss tailored to handle the long-tailed distribution of the MECCANO
[21] dataset. Based on the underlying principle of focal loss, which captures
the relationship between tail (scarce) classes and their prediction
difficulties, we propose an exponentially decaying variant of focal loss for
our current task. It initially emphasizes learning from the hard misclassified
examples and gradually adapts to the entire range of examples in the dataset.
This annealing process encourages the model to strike a balance between
focusing on the sparse set of hard samples, while still leveraging the
information provided by the easier ones. Additionally, we opt for the late
fusion strategy to combine the resultant probability distributions from RGB and
Depth modalities for final action prediction. Experimental evaluations on the
MECCANO dataset demonstrate the effectiveness of our approach.Comment: 22nd International Conference on Image Analysis and Processing
Workshops - Multimodal Action Recognition on the MECCANO Dataset, 202
The Devil is the Classifier: Investigating Long Tail Relation Classification with Decoupling Analysis
Long-tailed relation classification is a challenging problem as the head
classes may dominate the training phase, thereby leading to the deterioration
of the tail performance. Existing solutions usually address this issue via
class-balancing strategies, e.g., data re-sampling and loss re-weighting, but
all these methods adhere to the schema of entangling learning of the
representation and classifier. In this study, we conduct an in-depth empirical
investigation into the long-tailed problem and found that pre-trained models
with instance-balanced sampling already capture the well-learned
representations for all classes; moreover, it is possible to achieve better
long-tailed classification ability at low cost by only adjusting the
classifier. Inspired by this observation, we propose a robust classifier with
attentive relation routing, which assigns soft weights by automatically
aggregating the relations. Extensive experiments on two datasets demonstrate
the effectiveness of our proposed approach. Code and datasets are available in
https://github.com/zjunlp/deepke
Simplifying Neural Network Training Under Class Imbalance
Real-world datasets are often highly class-imbalanced, which can adversely
impact the performance of deep learning models. The majority of research on
training neural networks under class imbalance has focused on specialized loss
functions, sampling techniques, or two-stage training procedures. Notably, we
demonstrate that simply tuning existing components of standard deep learning
pipelines, such as the batch size, data augmentation, optimizer, and label
smoothing, can achieve state-of-the-art performance without any such
specialized class imbalance methods. We also provide key prescriptions and
considerations for training under class imbalance, and an understanding of why
imbalance methods succeed or fail.Comment: NeurIPS 2023. Code available at
https://github.com/ravidziv/SimplifyingImbalancedTrainin
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