3 research outputs found
Mutual Balancing in State-Object Components for Compositional Zero-Shot Learning
Compositional Zero-Shot Learning (CZSL) aims to recognize unseen compositions
from seen states and objects. The disparity between the manually labeled
semantic information and its actual visual features causes a significant
imbalance of visual deviation in the distribution of various object classes and
state classes, which is ignored by existing methods. To ameliorate these
issues, we consider the CZSL task as an unbalanced multi-label classification
task and propose a novel method called MUtual balancing in STate-object
components (MUST) for CZSL, which provides a balancing inductive bias for the
model. In particular, we split the classification of the composition classes
into two consecutive processes to analyze the entanglement of the two
components to get additional knowledge in advance, which reflects the degree of
visual deviation between the two components. We use the knowledge gained to
modify the model's training process in order to generate more distinct class
borders for classes with significant visual deviations. Extensive experiments
demonstrate that our approach significantly outperforms the state-of-the-art on
MIT-States, UT-Zappos, and C-GQA when combined with the basic CZSL frameworks,
and it can improve various CZSL frameworks. Our codes are available on
https://anonymous.4open.science/r/MUST_CGE/
Deconstructed Generation-Based Zero-Shot Model
Recent research on Generalized Zero-Shot Learning (GZSL) has focused primarily on generation-based methods. However, current literature has overlooked the fundamental principles of these methods and has made limited progress in a complex manner. In this paper, we aim to deconstruct the generator-classifier framework and provide guidance for its improvement and extension. We begin by breaking down the generator-learned unseen class distribution into class-level and instance-level distributions. Through our analysis of the role of these two types of distributions in solving the GZSL problem, we generalize the focus of the generation-based approach, emphasizing the importance of (i) attribute generalization in generator learning and (ii) independent classifier learning with partially biased data. We present a simple method based on this analysis that outperforms SotAs on four public GZSL datasets, demonstrating the validity of our deconstruction. Furthermore, our proposed method remains effective even without a generative model, representing a step towards simplifying the generator-classifier structure. Our code is available at https://github.com/cdb342/DGZ
Zero-Shot Logit Adjustment
Semantic-descriptor-based Generalized Zero-Shot Learning (GZSL) poses
challenges in recognizing novel classes in the test phase. The development of
generative models enables current GZSL techniques to probe further into the
semantic-visual link, culminating in a two-stage form that includes a generator
and a classifier. However, existing generation-based methods focus on enhancing
the generator's effect while neglecting the improvement of the classifier. In
this paper, we first analyze of two properties of the generated pseudo unseen
samples: bias and homogeneity. Then, we perform variational Bayesian inference
to back-derive the evaluation metrics, which reflects the balance of the seen
and unseen classes. As a consequence of our derivation, the aforementioned two
properties are incorporated into the classifier training as seen-unseen priors
via logit adjustment. The Zero-Shot Logit Adjustment further puts
semantic-based classifiers into effect in generation-based GZSL. Our
experiments demonstrate that the proposed technique achieves state-of-the-art
when combined with the basic generator, and it can improve various generative
Zero-Shot Learning frameworks. Our codes are available on
https://github.com/cdb342/IJCAI-2022-ZLA.Comment: IJCAI 202