Browse State-of-the-Art › Generalized Zero-Shot Learning
Generalized Zero-Shot Learning
63 papers with code · 12 benchmarks · 10 datasets archive 2025-07-28
In generalized zero shot learning (GZSL), the set of classes are split into seen and unseen classes, where training relies on the semantic features of the seen and unseen classes and the visual representations of only the seen classes, while testing uses the visual representations of the seen and unseen classes.
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
12 leaderboard tables shown for this task, 12 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted. 10 shown of 12 until expanded.
Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
10 datasets whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
No subtask under this task in the archive's task tree.
Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
30 shown of 63 papers with code (161 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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4 Dec 2017 4 repositories listedSuffering from the extreme training data imbalance between seen and unseen classes, most of existing state-of-the-art approaches fail to achieve satisfactory results for the challenging generalized zero-shot learning…
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30 Mar 2021 3 repositories listed Syntology ran 3 of 5 samples · 2 unverified · 2 pointer-only (licence)To tackle this issue, we propose to integrate the generation model with the embedding model, yielding a hybrid GZSL framework.
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19 Jun 2020 3 repositories listedNormalization techniques have proved to be a crucial ingredient of successful training in a traditional supervised learning regime.
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18 Jun 2020 3 repositories listedZero-shot learning relies on semantic class representations such as hand-engineered attributes or learned embeddings to predict classes without any labeled examples.
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17 Aug 2020 2 repositories listedIn constrast, Zero-shot Learning (ZSL) and Generalized Zero-shot Learning (GZSL) tasks inherently lack supervision across all classes.
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9 Aug 2020 2 repositories listed Syntology ran 0 of 1 samples · 1 unverifiedUsing a gating mechanism that discriminates the unseen samples from the seen samples can decompose the GZSL problem to a conventional Zero-Shot Learning (ZSL) problem and a supervised classification problem.
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5 Dec 2018 2 repositories listed Syntology ran 2 of 2 samples · 0 unverifiedMany approaches in generalized zero-shot learning rely on cross-modal mapping between the image feature space and the class embedding space.
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2 Aug 2024 1 repository listedWe first employ generative adversarial networks to synthesize unseen features, enabling the training of an OOD detector alongside classifiers for seen and unseen classes.
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29 Jul 2024 1 repository listedTo address this, zero-shot learning (ZSL) aims to classify data of unseen classes with the help of semantic information.
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21 Jun 2024 1 repository listed Syntology ran 2 of 3 samples · 1 unverified · 3 pointer-only (licence)Our method achieves an absolute increase of 3.
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25 Apr 2024 1 repository listed Syntology ran 0 of 2 samples · 2 unverified · 2 pointer-only (licence)Zero-shot learning has consistently yielded remarkable progress via modeling nuanced one-to-one visual-attribute correlation.
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9 Apr 2024 1 repository listedHowever, existing benchmarks predate the popularization of large multi-modal models, such as CLIP and CLAP.
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21 Mar 2024 1 repository listedDifferent from existing GZSL methods which alleviate DSP by generating features of unseen classes with semantics, CDGZSL needs to construct a common feature space across domains and acquire the corresponding intrinsic…
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18 Feb 2024 1 repository listedTo counter this, we introduce an end-to-end generative GZSL framework called D³GZSL.
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28 Jan 2024 1 repository listed Syntology ran 9 of 14 samples · 5 unverified · 14 pointer-only (licence)Firstly, to recover the virtual features of the base data, we model the CLIP features of base class images as samples from a von Mises-Fisher (vMF) distribution based on the pre-trained classifier.
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1 Jan 2024 1 repository listedThis motivates us to study GZSL in the more practical setting where unseen classes can be either similar or dissimilar to seen classes.
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23 Nov 2023 1 repository listedIn this paper, we propose a simple yet effective Attribute-Aware Representation Rectification framework for GZSL, dubbed (𝐀𝐑)², to adaptively rectify the feature extractor to learn novel features while keeping original…
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25 Sep 2023 1 repository listedTo address these issues, we propose a novel Dual Feature Augmentation Network (DFAN), which comprises two feature augmentation modules, one for visual features and the other for semantic features.
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1 Aug 2023 1 repository listedTo take the advantage of image augmentations while mitigating the semantic distortion issue, we propose a novel ZSL approach by Harnessing Adversarial Samples (HAS).
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14 Jul 2023 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)Thereafter, we fine-tune CLIP with off-the-shelf methods by combining labeled and synthesized features.
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27 Mar 2023 1 repository listed Syntology ran 5 of 7 samples · 2 unverified · 7 pointer-only (licence)Generalized Zero-Shot Learning (GZSL) identifies unseen categories by knowledge transferred from the seen domain, relying on the intrinsic interactions between visual and semantic information.
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1 Feb 2023 1 repository listedImage geolocalization is the challenging task of predicting the geographic coordinates of origin for a given photo.
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23 Nov 2022 1 repository listedTo address this, we introduce a scaled-down instantiation of this challenge: Evolutionary Generalized Zero-Shot Learning (EGZSL).
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22 Nov 2022 1 repository listedGeneralized Zero-Shot Learning (GZSL) aims to train a classifier that can generalize to unseen classes, using a set of attributes as auxiliary information, and the visual features extracted from a pre-trained…
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26 Jun 2022 1 repository listedWe focus here on the subsumption or \texttt{isOfClass} predicate, which is fundamental to encode most semantic image interpretation tasks.
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3 Jun 2022 1 repository listedIllustrations contained in field guides deliberately focus on discriminative properties of each species, and can serve as side information to transfer knowledge from seen to unseen bird species.
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1 May 2022 1 repository listedGeneralized zero-shot text classification aims to classify textual instances from both previously seen classes and incrementally emerging unseen classes.
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25 Apr 2022 1 repository listedAs a consequence of our derivation, the aforementioned two properties are incorporated into the classifier training as seen-unseen priors via logit adjustment.
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24 Apr 2022 1 repository listedRecent research on Generalized Zero-Shot Learning (GZSL) has focused primarily on generation-based methods.
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30 Mar 2022 1 repository listedSecondly, we introduce a unified feature-generative framework for CGZSL that leverages bi-directional incremental alignment to dynamically adapt to addition of new classes, with or without labeled data, that arrive over…
Syntology lines on 8 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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