GenZSL: Generative Zero-Shot Learning Via Inductive Variational Autoencoder
Authors: Shiming Chen
Dingjie Fu
Salman Khan
Fahad Shahbaz Khan
Venue: ICML 2025 poster (2025)
Keywords: Zero-shot learning; transfer learning
Primary area: applications->computer_vision
TL;DR: We propose an inductive variational autoencoder for generative zero-shot learning

Abstract: Remarkable progress in zero-shot learning (ZSL) has been achieved using generative models. However, existing generative ZSL methods merely generate (imagine) the visual features from scratch guided by the strong class semantic vectors annotated by experts, resulting in suboptimal generative performance and limited scene generalization. To address these and advance ZSL, we propose an inductive variational autoencoder for generative zero-shot learning, dubbed GenZSL. Mimicking human-level concept learning, GenZSL operates by inducting new class samples from similar seen classes using weak class semantic vectors derived from target class names (i.e., CLIP text embedding). To ensure the generation of informative samples for training an effective ZSL classifier, our GenZSL incorporates two key strategies. Firstly, it employs class diversity promotion to enhance the diversity of class semantic vectors. Secondly, it utilizes target class-guided information boosting criteria to optimize the model. Extensive experiments conducted on three popular benchmark datasets showcase the superiority and potential of our GenZSL with significant efficacy and efficiency over f-VAEGAN, e.g., 24.7\% performance gains and more than $60\times$ faster training speed on AWA2. Codes are available at \url{https://github.com/shiming-chen/GenZSL}.

GenZSL: Generative Zero-Shot Learning Via Inductive Variational Autoencoder
Shiming Chen' Dingjie Fu²
Salman Khan 13 Fahad Shahbaz Khan
Abstract
Yoak Clas fewedda Vina dftere Rameted
Remarkable progress in zero-shot learning (ZSL.) has been achieved using generative models. How- ever, existing generative ZSL methods merely generate (imagine) the visual features from scratch guided by the strong class semantic vec- tors annotated by experts, resulting in subopti- mal generative performance and limited scene generalization. To address these and advance ZSL, we propose an inductive variational autoen- coder for generative zero-shot learning, dubbed GenZSL. Mimicking human-level concept learn- ing, GenZSL operates by inducting new class samples from similar seen classes using weak class semantic vectors derived from target class names (i.e., CLIP text embedding). To ensure the generation of informative samples for training an effective 7SL classifier, our Gen7SL. incor- porates two key strategies. Firstly, employs class diversity promotion to enhance the diver- sity of class semantic vectors. Secondly, it uti- lizes target class-guided information boosting cri- teria to optimize the model. Extensive exper- iments conducted on three popular benchmark datasets showcase the superiority and potential of our GenZSL with significant efficacy and ef- ficiency over f-VAEGAN, e.g.. 24.7% perfor- mance gains and more than 60x faster training speed on AWA2. Codes are available at https: / //github. com/ sh http://iiinnnnn/ene/aa...
(a) Existing Generative ZSL
(b) Our GenZSL
Figure I. Motivation illustration. (a) Existing generative ZSL methods merely generate (imagine) the visual features from scratch guided by the strong class semantic vectors, resulting in suboptimal generative performance and
generalization. For example, the generator inevitably generates similar classes of "Zebra" or others, c.g., "Donkey". (b) Our GenZSL generates (induces) the reliable visual features of unseen classes from the similar seen classes with the clues of weak class semantic vector, e.g., from "Horse" to "Zebra".
et al., 2009). Recently, generative models such as genera- tive adversarial networks (GANs) (Goodfellow et al., 2014), variational autoencoders (VAEs) (Kingma & Welling, 2014), and normalizing flows (Dinh et al., 2017) have been suc- cessfully applied in ZSL, achieving significant performance improvements. These models synthesize images visual features of unscen classes to alleviate the lack of samples for those classes (Arora et al., 2018; Xian et al., 2018; 2019b; Chen et al., 202 Narayan et al., 2020; Chen et al., 2021b).
Given that GAN architectures can generate higher-qualin visual sample features, there's a growing trend in synthesiz- ing features using GANs (Xian et al., 2018; 2019b; Chen et al., 2021a; Narayan et al., 2020; Yue al., 2021; Chen et al., 2025). However, existing generative ZSL methods typically generate (imagine) visual features from scratch (c.g., Gaussian noises) guided by strong class semantic vec- tors annotated by expert (Xian et al., 2018: 0018; 2019b; Chen et al., 2021a; Narayan et al., 2020; Çetin et al., 2022; Chen et al., 2023a). This approach often fails produce reliable feature samples and generalize to various scene tasks, as illustrated in Figure (a). The shortcomings arise from: the generator learning from scratch without sufficient data to capture the high-dimensional data distribution, and ii) the reliance on strong class semantic vectors, which are time- consuming and intensive to collect for various scene generations. Hence, there's a pressing need explore novel
1. Introduction
Zero-shot learning (ZSL) enables the recognition of un- seen classes by transferring semantic knowledge from some seen classes to unseen ones (Palatucci et al., 2009; Lampert
Mohamed bin Zayed University of Artificial Intelligence HHazhong University of Science and Technology 'Australian National University LLikööping University. Correspondence to: Fahad Shahbaz Khan <fahad khan www.embb..cc... fa- had.khan liu.se>.
Proceedings of the 42nd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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GenZSL: Inductive Variational Autoencoder for Generative Zero-Shot Learning
generative paradigms for ZSL.
nents. This process enables all class semantic vectors to become nearly perpendicular to each other but keep the ori- gin relationships between all classes, thus enhancing the diversity among them. Then, we employ a semantically similar sample selection module to select the referent class samples for seen/unseen classes from seen classes. Finally, we design a target class-guided information boosting loss to guide the inductive variational autoencoder to synthesize the visual features belonging to target classes.
Cognitive psychologist often frame the process of learning new concepts as "the problem of induction" " (Carey, 1985; and, 2000). For instance, children typically induce novel concepts from a few familiar objects, guided by certain pri- ors (Tenenbaum et al., 2011; Lake et al., 2015). Essentially, rich concepts can be induced "compositionally" from sim- pler primitives under a Bayesian criterion, and the model "learns to learn" by developing hierarchical priors that fa- cilitate the learning of new concepts based on previous ex- periences with related concepts. These priors represent a learned inductive bias that abstracts the key regularities and dimensions of variation across both types of concepts and instances of a concept within a given domain. Following this paradigm, our objective is devise a novel generative zero- shot learning (ZSL) model capable of generating (inducing) new/target classes based on samples from similar/referent seen classes. As illustrated in Figure 1 (b), our generative ZSL model can generate informative samples of new classes (e.g., "Zebra") by inducing them from referent seen classes (e.g.. "Horse", "TTier", and "Panda").
Our main contributions are summarized in the following: i) We propose an induction- based GenZSL for generative ZSL, which can synthesize the samples of unseen classes based on the weak class semantic vectors inducting from the similar seen classes. ii) We enable GenZSI. to synthe- size informative samples by designing the class diversity promotion, semantically similar sample selection, and in- ductive vatiational autoencoder modules. iii) We conduct extensive experiments on three wide-used ZSL benchmarks (e.g., CUB (Welinder et al., 2010), SUN (Patterson & Hays, 2012), and AWA2 (Xian et al., 2019a)), results demonstrate the significant efficacy and efficiency over the existing ZSL. methods, e.g., 24.7% performance gains and more than 60 faster training speed on AWA2. More importantly, our Gen- ZSL can be flexibly extended on various scene tasks without the guidance of expert-annotated attributes.
However, there are two challenges in targeting this goal. Firstly, addressing the issue of weak class semantic vec- tors. These vectors, extracted from sources like the CLIP text encoder (Radford et al., 2021), often lack specific class information, such as attributes, compared to vectors anno- tated by experts. As a result, they may not effectively guide generative methods. Furthermore, these vectors can be mis- aligned in the rision-language space. For instance, the text embedding of class name might be close to embeddings of unrelated classes but distant from image embeddings (Hu et al., 2023; Tanwisuth et al., 2023; Khattak et al., 2023: Chen et al., 2024a). How can we enhance the diversity of weak class semantic vectors to distinguish between various classes effectively, thereby avoiding the problem of gener- ating visual features that are too similar to other classes? Secondly, ensuring that novel generative method evolves samples of referent classes into target classes with the guid- ance of weak class semantic vectors is equally challenging- This involves transforming samples of seen classes into sam- ples that accurately represent unseen classes, guided only by the limited information provided by weak class semantic vectors. How can we achieve this induction reliably and effectively within a generative ZSL framwokkwkkk?
2. Related Work
Zero-Shot Learning. Zero-shot leamning is proposed to tackle the classification problem when some classes unknown. To recognize the unseen classes, the side- informatioon/semanie (e.g., attribute descriptions (Lampert et al., 2014). DNA information (Badirli et al., 2021)) is uti- lized to bridge the gap between seen and unseen classes. As such, the key task of ZSL to conduct effective interactions between visual and semantic domains. Typically, there are two methodologies to target on this goal, i.c., embedding- based methods that learn et al., 2016; Xu et al., 2020; Zhu et al., 2019; Wan et al., 2019 Han et al., 2022), and generative methods that learn semantic-vvisual mapping (Xian et al., 90; Chen et al., 2021a; Huynh & Elhamifar, 2020b: Çetin et al., 2022: Chen et al., 2020). Considering the semantic representations, embedding- methods focus recently on learning the region-based visual features rather than the holistic visual features (Huynh & Elhamifar, 2020a; Xu et al., 2020; Chen et al., 2022a;b; 2024c). Since these methods learn the ZSL classifier only on seen classes, inevitably resulting in the models overfitting to seen classes. To tackle this challenge, generative ZSL methods employ the generative models (c.g.. VAE, and GAN) to generate the unseen features for data augmentation, and thus ZSL is converted to supervised classification task. As such, the generative ZSL methods
mapping (Xian
To guide the induction towards creating informative samples for training effective ZSL. classifiers, we propose a novel in- ductive variational autoencoder for generative ZSL, namely GenZSL GenZSL essentially considers two criteria, i.c., class diversity promotion and target class-guided informa- tion boosting. Specifically, we first deploy class diversity promotion module to reduce redundant information from class semantic vectors by eliminating their major compo-
2

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GenZSL: Inductive Variational Autoencoder for Generative Zero-Shot Learning
photo of (Class Name]
Vision
Frozen
Training
Loss Function
Encoder
Eacoder
Semantically Similar Sample Selection
Class Diversity Promotion
C
danget
T.
target
T furgel
CTS
Inductive Encader
8- Nio 1) +
Inductive Decoder
Top-k Referennd
Iove Samples
Lx
Inductive Variational Autocneoder
Figure 2. Pipeline of GenZSL. GenZSL first takes class diversity promotion reduce the redundant information from class semantic vectors, and to improve the identity for all class semantic vectors. Then, it employs semantically similar sample selection module to select the top-k referent class from the seen classes for each target class training inputs. Based the referent samples, GenZSI. learns an inductive variational autoencoder to create the new informative feature samples for unseen classes via induction optimized by target class-guided information boosting criteria.
have shown significant performance and become very pop- ular recently. Furthermore, Li et al. (Li et al., 2023) intro- duces Stable Diffusion to perform zero-shot classification without any additional training by leveraging the ELBO as an approximate class-conditional log-likelihood.
out involving human annotators, it requires anchor samples for imagination. As such, these imagination- -bsse genera- tive models are not feasible for ZSL tasks. In contrast, we introduce a novel generative method to synthesize new in- formative data for ZSL via induction inspired by the human perception process (Carey, 1985; and, 2000).
However, existing generative ZSL methods simply imagine the visual feature from Gaussian distribution with the guid- ance of strong class semantic vector. Thus, they limited in i) there lacks enough data for training a generative model to learn the high-dina nigh-dimension data distribution, resulting in un- desirable generation performance; ii) they rely on the strong condition guidance (c.g., expert-annotated attributes) for synthesizing target classes, so they cannot easily generalize to various scenes. As such, we propose a novel generative method create samples of unseen classes for advancing ZSL via induction rather than imagination.
3. Inductive Variational Autoencoder for ZSL
Problem Setting. The problem setting of ZSL and no- tations are defined in the following. Assume that data of seen classes D = {(rf, vf)) has C classes, where xf E X' denotes the i-th visual feature, and yē E )" the corresponding class label. D is further divided into training set Dir and test set Dic following (Xian al., 2019a). The unseen classes C has unlabeled samples Dj = [(= v)"), where x. E X are the visual samples of unseen classes, and y. E ) are the corresponding labels. Notably, y nys = Ø. A set of class semantic vectors of the class c E C UC" = are extracted from CLIP text en- coder, defined as =f. In the conventional zero-shot learning (CZSL) setting, we learn classifier only classifying unseen classes, i.e., fcZsi X - >yU while we learn classifier for both seen and unseen classes in the generalized zero-shot learning (GZSL.) setting, i.e., fezsL X - D) uys.
Generative Model for Data Augmentation. Synthesiz- ing new data using a generative model for data augmentation is promising direction (Zhou et al., 2023; Jahanian al., 2022). Many recent studies (Azizi et al., 2023; He et al., 2023) explored generative models to generate new data for model training. However, these methods fail to ensure that the synthesized data bring sufficient new information and accurate labels for the target small datasets. Because they imagine the new data from scratch (e.g., Gaussian distribu- tion), which is infeasible with very limited/diverse training data. Zhang et al. (Zhang et al., 2023) introduce GIF to expanding small-scale datasets with guided imagination us- ing pre-trained large-scale generative models, c.g., Stable Diffusion (Rombach et al., 2022) or DALL-E2 (Ramesh et al., 2022). Although GIF can expand a small dataset into a larger labeled one in a fully automatic manner with-
Pipeline Overview. To enable the generative ZSL method to synthesize high-quality visual features with good scene generalization, we propose an inductive variational autoen- coder for ZSL (namely GenZSL). Towards creating infor- mative new samples for unseen classes, GenZSL considers two important criteria, i.c., class diversity promotion and target class-guided information boosting. As shown in Fig.
3

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GenZSL: Inductive Variational Autoencoder for Generative Zero-Shot Learning
2, GenZSL first takes class diversity promotion to reduce the redundant information from class semantic vectors by removing their major components, enabling all class se- mantic vectors nearly perpendicular to each other. Based on the refined class semantic vectors, GenZSL. employs a semantically similar sample selection module to select the top-k referent class from the seen classes for each target class. Subsequently, GenZSL learns the inductive varia- tional autoencoder (IVAH) with the Kullback-Leibler diver- gence (KL) loss, target class reconstruction loss, and target class- class-guided information boosting loss, which ensures Gen- ZSL inducts the target class samples from their similar class samples. After training. GenZSL takes IVAE to synthe- size visual features of unseen classes to learn a supervised classifier.
i.e., U, sed(Z), where U = ee,, orthonormal basis. As suggested in Principal Component Analysis, the first dimension et of the outer-space basis U will be the major component, which overlaps on most class semantic vectors [ ,., , We directly remove the major component el to define the new projection matrix P = U'U'T with U = =[e", ee", ,.. e. Accordingly, we obtain the refined class semantic vectors, formulated as:
,2,
is the
= (3'.22
(1)
As shown in Fig, 3(b), we make the refined class seman- tic vectors nearly perpendicular to each other, such as the mean similarity between various classes drops from 0.5726 to 1.825e on the CUB. Meanwhile, CDP preserves the original relationships of classes, and thus it will not destroy the class semanties. As such, the refined class semantic vectors will be the significant conditions for induction.
3.1. Class Diversity Promotion
To avoid the ZSL model relying on the expert-annotated class semantic vectors, we adopt CLIP (Radford et al., 2021) text encoder extract the class semantic vectors, i.e., text embedding of the class names. However, we observed that the CLIP text encoder fails to capture discriminative class information, especially on fine-grained datasets. As shown in Fig. 3(a), the class semantic vectors have high similarity with other classes, that is, all class semantic vectors are highly adjacent to ones of other classes. If we directly take such class semantic vectors as conditions to guide GenZSL, it inevitably causes the synthesized visual features confusion as the class semantic vectors with limited diversity.
3.2. Semantically Similar Sample Selection
In this paper, we are interested in semantically similar sam- ples they can serve reliable known data for inducing new samples of other similar classes. Specifically, we select the semantically similar samples in seen classes (defined a referent class samples) with respect to the target seen/unseen classes ctarget during training/testing, respectively. Accord- ing to the cosine similarity, we define similar samples as the referent ones whose class semantic vectors g<" top-k closed to the target class semantic vectors gtarget formu- lated as:
Class Scrmunic Viatinn Similary Hostuap 20 siae
Clao. Scraattc Varkers Similariy Hostrap
starget x ze
Mean Statarit (15724)
gefer = arg max top k(c)) Starget
(2)
||
where k is the number of referent classes with respect to the corresponding target classes. Accordingly, we can obtain set of referent samples grefer x refer of the target seen/unseen classes from seen classes for training/testing.
Clas
Clas Index
Class Indes
(a) CLIP text eneoder
(b) CLIP w/ Class Diversity Promotion
3.3. Inductive Variational Autoencoder
Network Components. Our GenZSL aims to generate informative new samples for novel classes by inducing from seen classes. To achieve this, we devise a novel generative model called the inductive variational autoencoder (IVAE). We formulate the induction of new samples for target classes å from reference samples refer as = IV Ex + o, gtarget), where o represents the perturbation applied to feefer enable IVAE to variationally generate & distinct from refer.
Figure 3. Class semantic vectors' similarity heatmaps are extracted by CLIP text encoder and CLIP with class diversity promotion on the CUB dataset. The similarity heatmaps on SUN and AWA2 are presented in Appendix B.
As such, we introduce class diversity promotion (CDP) to improve the diversity of class semantic vectors. CDP re- duces the redundant information from class semantic vectors by removing their major components, enabling all class se- mantic vectors nearly perpendicular to each other but to keep the original class relationships. Specifically, we take Singular Value Decomposition to get the orthonormal basis of the span of class semantic vectors Z = [2], ,,,,..... .......,,. 2
Specifically, IVAE consists of an inductive encoder (IE) and an inductive decoder (ID). The IE and ID are the Multi- Layer Perceptron (MLP) networks. The IE encodes the ref- erent samples refer into latent space o conditioned by the
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GenZSL: Inductive Variational Autoencoder for Generative Zero-Shot Learning
target class semantic vectors ztarget, i.e., 0 = 6-N(0, +) where nrd 5 7 [= (targe)) Subsequently, The ID further comprises hidden layers with a progressively larger number of nodes that decode the latent features to be re- construction of the target classes samples x.orget guided by ztarget, formulated as ē = [D, ztarget). This is different to VAE which ultimately reconstructs the data back to its original input reffer
Different from the standard VAËs that synthesize samples from scratch (e.g., Gaussian noise), we synthesize the visual features of unseen classes inducting from referent seen class samples and take Gaussian noise as variations. As such, our GenZSL. can more easily create informative new samples for unseen classes
Then, we take the synthesized unseen visual features and the real visual features of seen classes x" E Dí, learn a classifier (e.g., softmax), i.e., fazet X - ) in the CZSL setting and fg : X' + >"">" in the setting. Once the classifier is trained, we use the real sample in the test set Di, to test the model further. The details of the testing process are shown in Appendix A.
Network Optimization. Similar to the conditional VAE (Sohn et al., 2015), our IVAE includes the KI. loss LK and the target class reconstruction loss LTR, formulated as:
LIVAE = LKL CTR KLlg(o x, u,= starget) starget)lpp()) (
=
Bylolareter gtaraee) log parlarget o, gtarget)]
4. Experiments
(3)
Datasets. We evaluate our GenZSL three well-known ZSL benchmark datasets, i.e., two fine-grained datasets ( CUB (Welinder et al., 2010) and SUN (Patterson & Hays, 2012)) and one coarse- coarse-grained dataset (AWA2 (Xian et al., 2019a)). CUB has 11,788 images of 200 bird classes (seen/unseen classes 150/50). SUN contains 14,340 im- ages of 717 scene classes (seen/unseen classes = 645/72). AWA2 consists of 37,322 images of 50 animal classes (seen/unseen classes = 40/10).
where q(0 | a., gtarget) is modeled by 1 [E(rrefer glarget),
p(o | starget) is assumed to be (O,0 1), and platarget o, 0. is represented by [D, gtarget). Essentially,
LyR towards the target class class-guided information boosting criteria in vision-level, encouraging IVAE to synthesize high-quality target class samples.
To ensure IVAE evolves the referent samples to belong to target classes, GenZSL further employs a target class- class-guided information boosting loss Boost for optimization. Consid- ering CLIP'' full prior knowledge, Boost aims to improve the information entropy between the synthesized visual fea- tures of target classes ftarget and their corresponding class semantic vectors glorget, formulated as:
During testing, we adopt the uni- fied evaluation protocols following (Xian al., 2019a). The top-1 1 accuracy of the unseen class (denoted as acc) is used for evaluating the CZSL performance. In the GZSL setting, the top-1 accuracy on seen and unseen classes is adopted, denoted as S and U, respectively. Meanwhile, their har- monic mean (defined as H (2 S x US + +U) is a better protocols in the GZSL.
Evaluation Protocols.
exp (< jtarget, gtarget > >/T)
Boost
(4)
Σj= exp (< Storget, gtarget > /T)*
where is the temperature parameter and set to 0.07, < .,. >>>>e denotes the similarity between the two elements. Indeed, Boost and LTR cooperatively ensure IVAE to syn- thesize desirable target class samples from semantic- and vision-level, respectively.
Implementation Details. We use the training splits pro- posed in (Xian et al., 2018). Meanwhile, the visual fea- tures with 512 dimensions are extracted from the CLIP vision encoder (Radford et al., 2021). The IE and ID are the MLP networks. The specific network settings are fc(512) - fc(1024) - fc(2048) - Re and fc(512) - fc(1024) - fc(2048) - ReLu - fc(512) for IE and ID, respectively. We synthesize 1600, 800, and 5000 features per unseen class to train the classifier for CUB, SUN, and AWA2 datasets, respectively. We empirically set the loss weight À as 0.1 for CUB and AWA2, and 0.001 for SUN. The top-2 similar classes serve as the referent classes for inductions on all datasets. Furthermore, to enlarge the ref- erence of the referent samples for effective model training, we take mixup technique (Zhang et al., 2018) to randomly fuse the samples of various referent classes for data aug- mentation, i.c., refer = 0.8 _ _ + 0.2 x top-2 All experiments are performed on a single NVIDIA RTX 3090
As such, the total optimization loss function can be written as:
Listel LIV + XLBoost
(5)
where X is a weight to control the Loosst, enabling model optimization to be more effective.
3.4. ZSL Classification
After training, we first take the pre-trained IVAE to synthe- size visual features for unseen classes:
H² = (D, z0)),
where = 8- 6NN(00,)) 1) u. and p,d - refer .e")..
(6)
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GenZSL: Inductive Variational Autoencoder for Generative Zero-Shot Learning
Table Staate-ofttheart comparisons for generative 7SL. methods CUB, SUN, and AWA2 under the G7SI. settings. The best and
second-bes results are marked in Red and Blue. respectively. denotes methods use CLIP visual features.
CUB
SUN
AWA2
Methods
S
H
U
S
H
U
S
CADA-VAE (Schönfeld ct al., 2019)
51.6
53.5
52.4
47.2
35.7
40.6
55.8
75.0
63.9
EVAEGAN (Xian ct al., 2019b)
48.7
58.0
52.9
45.1
38.0
41.3
57.6
70.6
63.5
LisGAN (Li al., 2019)
46.5
57.9
51.6
42.9
37.8
40.2
52.6
76.3
62.3
LsrGAN (Vyas et al., 2020)
48.1
59.1
53.0
44.8
37.7
40.9
54.6
74.6
63.0
IZF-NBC (Shen et al., 2020)
44.2
56.3
49.5
58.1
76.0
65.9
AGZSL (Chou el al., 2021)
48.3
58.9
53.1
29.9
40.2
34.3
65.1
78.9
71.3
HSVA (Chen et al., 2021b)
52.7
58.3
55.3
48.6
39.0
43.3
59.3
76.6
66.8
ICCE (Kong et al., 2022)
65.3
82.3
72.8
ACE-GZSL (Han et al., 2022)
45.9
41.7
43.7
64.3
77.5
70.3
FREE+ESZSL (Cetin al., 2022)
51.6
60.4
55.7
48.2
36.5
41.5
51.3
78.0
61.8
CLSWGAN + DSP (Chen et al., 2023a)
51.4
63.8
56.9
48.3
43.0
45.5
60.0
86.0
70.7
ViFR (Chen al., 2025)
57.8
62.7
60.1
48.8
35.2
40.9
58.4
81.4
68.0
f-VAEGAN (Xian et al., 2019b)
22.5
82.2
35.3
61.2
95.9
74.7
TF-VAEGAN' (Narayan et al., 2020)
21.1
84.4
34.0
43.7
96.3
60.1
GenZSL
53.5
61.9
57.4
50.6
43.8
47.0
86.1
88.7
87.4
with 24G memory. We employ Pytorch to implement our experiments.
7SL. consistently achieves the best results with the H of 47. 47.0% and 87.4% on SUN and AWA2, and second best re- sults with the H of 57.4% on CUB, respectively. Notably, our GenZSL relies solely on weak class semantic vectors, while the compared methods utilize strong ones annotated by experts. Furthermore. when imagination- ative ZSI. methods (e.g., f-VAEGAN (Xian al., 2019b) and TT-VAEGGANNarayaa sual and semantic features, GenZSL still obtains improve- ments of H = 22. 12.1% respectively. This indicates that GenZSL is more adapt- able to generalizing across various scenes. These results consistently demonstrate our induction- GenZSL is desirable generative paradigm for 7SI...
Table State-of -f-e----t-hhrrrr-eee comparisons for ZSL methods on SUN and AWA2 under the CZSL setting. Embedding based methods are categorized as t. and generative methods are categorized denotes ZSL methods using the VIT visual features. The best
gener-
second-best results are marked Red and Blue, respectively.
al., 2020)) using CLIP vi-
SUN
AWA2 acc
Methods
acc
1%/11.55 11.5%/11..7 on CUB/SUN/WWA2,
APN (Xu et al. 2020)
62.6
66.8
DAZLE (Huynh & Elhamifar, 2020a)
67.9
GEM-ZSL (Liu al., 2021)
62.8
67.3
TransZero (Chen al., 2022a)
65.6
70.1
MSDN (Chen et al., 20226)
65.8
70.1
ICIS (Christensen ct al., 2023)
51.8
64.6
DUET (Chen et al., 2023b)
64.4
69.9
12MVFormer- Wiki (Naeem et al., 2023)
79.6
We also take our GenZSL to compare with the state-of-the- art ZSL methods under the CZSL setting, including the embedding methods and generative methods. Results are shown Table 2. Compared to the embedding- based methods, our GenZSL consistently achieves the best per- formance on SUN and AWA2. When taking our GenZSL to compare with the imagination-base generative meth- ods, GenZSL performs best results of acc=73.5% and acc=92.2% on SUN and AWA2, respectively. Notably, our GenZSL obtains the performance gains by 20.3% at least on AWA2 over the imagination- based generative ZSL methods. Compared with other ViT visual features based methods (Naeem al., 2024; Chen et al., 2024c:b), our GenZSL still obtains competitive performance gains. These com- petitive results demonstrate the superiority and potential of our induction-based generative method, which significantly synthesizes informative new samples for unseen classes.
HAS (Chen al., 2023c)
63.2
71.4
12DFormer+ (Nacem al., 2024)
77.3
BG-GZSL (Chen et al., 2024d)
69.5
77.6
ZSLViT (Chen al., 2024c)
68.3
70.7
CVsC* (Chen et al., 2024b)
71.5
73.1
CLSWGAN (Xian al., 2018)
60.8
68.2
EVAEGAN (Xian et al., 2019h)
64.7
71.1
CADA-VAE (Schönfeld et al., 2019)
61.7
63.0
LisGAN (Li et al., 2019)
61.7
70.6
IZF-NBC (Shen et al., 2020)
63.0
71.9
LsrGAN (Vyas al., 2020)
62.5
66.4
IISVA (Chen et al., 202
63.8
70.6
GG (Cavazza et al., 2023)
62.7
70.1
+VAEGAN+DSP (Chen et al., 2023a)
68.6
71.6
VADS (Hou et al., 2024)
82.5
GenZSL (Ours)
73.5
92.2
4.1. Comparisons with State-of-the-Art Methods
We first compare our GenZSL with the various imagination- based generative ZSL methods (e.g., VAE, GAN, VAEGAN, and normalizing flow) under the GZSL setting. Table shows the evaluation results on three datasets. Our Gen-
6

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GenZSL: Inductive Variational Autoencoder for Generative Zero-Shot Learning
Table 3. Results of ahlation study for GenZSI. CUB and AWA2.
CUB
AWA2
Methods
CZSL acc
GZSL
CZSL
GZSL
S
H
acc
U
S
H
GenZSL w/o CDP
60.9
48.2
64.6
55.2
90.7
82.3
87.9
85.0
GenZSL w/o Selection
62.5
48.0
67.0
55.9
91.1
84.2
86.4
85.3
GenZSL w/o LTR
48.3
20.1
37.5
26.2
87.5
39.9
83.1
53.9
GenZSL w/o Boost
61.1
47.7
66.4
55.5
90.5
75.3
91.4
82.6
GenZSL w/o Coo&L
60.0
42.5
69.3
52.7
87.7
89.0
75.3
81.6
GenZSL (full)
63.3
53.5
61.9
57.4
92.2
86.1
88.7
87.4
Table Results various models using weak class semantic vec- tors side andeiinformation on CUB.
CUB
Methods
U
S
H
CLIP (Radford ct al., 2021)
55.2
54.8
55.0
CoOp (Zhou et al., 2021)
49.2
63.8
55.6
CoOp + SHIP (Wang et al., 2023)
55.3
58.9
57.1
f-VAEGAN (Xian al., 2019b)
22.5
82.2
35.3
TF-VAEGAN (Narayan al., 2020)
21.1
84.4
34.0
(a) F-VAEGAN
(b) Our GenZSL
GenZSL (Ours)
53.5
61.9
57.4
Figure 4. Qualitative evaluation with t-SNE visualization. The sample features from f-VAEGAN (Xian al., 2019b) are shown the left, and from our GenZSL shown the right. We use 10 colors to denote randomly selected 10 classes from SUN. The "x" and "o" are denoted the real and synthesized sample features, respectively. The synthesized sample features and the real features distribute differently the left while distributing similarly on the right. The visualization on the CUB and AWA2 shown Appendix D.
4.2. Ablation Study
Various Model Components of Our GenZSL. To gain further insights into GenZSL, we conducted ablation studies to evaluate the effect of various model components, specifi- cally class diversity promotion (CDP), semantically similar sample selection, target class reconstruction loss CrR, and target class-guided information boosting loss Boosting on the CUB and AWA2 datasets. The ablation results marized in Table 3. When GenZSL lacks CDP to consider class diversity criteria, there is notable degradation in per- formance. This attributed to the inability of class semantic vectors extracted from the CLIP text encoder to capture dis- criminative class information, resulting weak diversity among class semantic vectors. Moreover, if GenZSL does not incorporate Lrn for target class information boosting, there is significant drop in performance, with the har- monic mean decreasing by 30.8% and 33.5% on CUB and AWA2, respectively. These findings underscore the impor- tance CrR it as a fundamental loss for target class-guided information boosting, ensuring that our IVAE accurately in- duces referent samples to target class samples. Furthermore, Boosting enhances the induction process the semantic level, complementing CrR- Semantically similar sample selection can slightly improve the performances of GenZSL, this means GenZSL is relatively robust in various source samples for model induction. Overall, these results demon- strate the effects of various components GenZSL and underscore the significance of the two criteria for induction.
Various Models with Weak Class Semantic Vectors. We conducted comparative analysis of various models utiliz- ing weak class semantic vectors extracted from the CLIP text encoder. The results are presented in Table 4. Compared to large-scale visual-language methods (e.g., CLIP (Radford et al., 2021) and CoOp (Zhou et al., 2021)). our GenZSL demonstrates substantial improvements, indicating the effec- tiveness of our inductive generative paradigm 7SL. model. When imagination-bass -aased generative 7SSL meth- ods (e.g., f-VAEGAN (Xian et al., 2019b) and TF-VAEGAN (Narayan et al., 2020)) utilize weak class semantic vectors as side information, GenZSL achieves significant performance gains, with a minimum increase of 22. 1.15% % in harmonic mean over these methods. Additionally, we observed that when imagination- based generative ZSL methods use weak class semantic vectors, their performances experience more sig- nificant drops compared to when they utilize strong class semantic vectors. For instance, the harmonic mean of f- VAEGAN decreases from 52.9% to 35.3%. These findings highlight the superiority of our induction. based generative method over imagination- based approaches in ZSL, as can synthesize high-quality sample features for unseen classes
desirable

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GenZSL: Inductive Variational Autoencoder for Generative Zero-Shot Learning
H
H
H
Accuracy (%)
(%)
Accuracy( (%)
Accuracy (%)
"4
54
55
032
A
top-k
192
3600
Nayn
2400
(a) Varying À effect
(b) Varying top-k effect
(c) Varying Neyn effect
Figure Hyper-paramene analysis. We show performance variations on CUB by adjusting the value of loss weight in (a), the number of the top referent classes top-k in (b), and the number of synthesized samples of each unseen class Nay in (c).
confirms that GenZSL is a desirable generative ZSL model, and the induction- based generative paradigm holds value for ZSL tasks.
Accuray (%6
Accorsscy
fater
4.4. Induction vs Imagination
HR Iicaai"".......
We analyze the efficiency and efficacy of induction- generative ZSL (c.g., our GenZSL) and imagination generative ZSL (e.g.. f-VAEGAN (Xian et al., 2019b), which most typical generative ZSL method) on AWA2 and CUB. Results are shown in Fig. 6. We find that our Gen- ZSL eases the optimization by providing faster convergence at the early stage, while f- f-VAEGAN towards convergence slowly, For example, GenZSL achieves the best GZSL per- formance with remarkable 60 and 10x acceleration in training speed than f-VAEGAN on AWA2 and CUB, respec- tively. Mcanwhile, our GenZSL obtains better performance both in the GZSL and CZSL settings. These demonstrate the efficiency and efficacy of our GenZSL and the great potential of the induction-based generative paradigm.
Step
(a) AWA2
(b) CUB
Figure 6. Induction- vy Imagination-based methods on AWA2 and CUB
with feasible scene generalization. Moreover, our work bridges the gap between large-scale visual-language ZSL methods and classical ZSL methods, leveraging the advan- tages of both approaches achieve improved performance in ZSL tasks. More discussions are in Appendix C.
4.5. Hyper- Perr-Paa Analysis.
4.3. Qualitative Evaluation
We analyze the effects of different hyper- ppram-paraetters of our GenZSL on the CUB dataset. These hyper-parameters in- clude the loss weight À in Eq. 5. the number of the top referent classes top-k, and the number of synthesized sam- ples for each unseen class Nayn- Fig. shows the CZSL and GZSL performances using different hyper-parametent In (a), the results indicate that GenZSL robust to vary- ing values of À and achieves good performance when À is relatively small (i.e., À 0.1). This is because Loost is semantic-level toward target class-guided information boosting criteria, which is supplement to the vision-level one (e.g., CLR). In (b), we evaluate the top similar classes as referent classes varying GenZSL uses the top referent classes to obtain better performance, which brings the mixup technique for data
We conducted qualitative evaluation intuitively show- case the performance of imagination-base generative ZSL methods (e.g., FAVAGN (Xian et al., 2019b)) and our induction-based approach (GenZSL). The t-SNE visualiza- tion (Maaten & Hinton, 2008) of real and synthesized sam- ple features is presented Fig. 4. We randomly selected 10 classes from SUN and visualized the sample features generated by f-VAEGAN and GenZSL. Fig. 4(a) illustrates that sample features synthesized by F-VAGAAN and real features exhibit significant differences, indicating that the synthesized visual features may not facilitate reliable classi- fication for ZSL. In contrast, Fig. 4(b) demonstrates that our GenZSL synthesizes informative samples for unseen classes that closely match real sample features. This visualization
{1, 2, 4, 8). We find that our
8

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GenZSL: Inductive Variational Autoencoder for Generative Zero-Shot Learning
augmentation. In (c), our GenZSL is shown robust to N syn when it is not set in a large number. The Syn can be set as 1600 to balance between the data amount and the ZSL performance. Overall, Fig. 5 shows that our GenZSL is robust to overcome hyper-parameter variations. The hyper- parameter analysis on SUN and AWA2 are presented in Appendix Accordingly, we empirically set these hyper- parameters {A, Nyyn as {0.1, 2, 1600). {0.001, 800} and 20.1, 5000) for CUB, SUN and AWA2, respectively.
Cavazza, J., Murino, V., and Bue, A. D. No adversaries zero-shot learning: Distilling ensemble of gaussian feature generators. IEEE Transactions on Pattern Anal- ysis and Machine Intelligence, 45:12167-12178 100 6
2023,
Chen, H.. Liu, Y., Ma, Y., Zheng, N., and Yu, X. TPR: copology-preserving reservoirs for generalized zero-shot learning, In NeurlPS, 2024a. 2
Chen, S., Wang, W., Xia, B., Peng, Q. You, X., Zheng, F,, and Shao, L. Free: Feature refinement for generalized zero-shot learning. In ICCV, 2021a. 1.2
5. Conclusion
We propose an inductive variational autoencoder as a new generative model for zero-shot learning, namely GenZSL. Inspired by human perception, GenZSL operates on an induction-based approach synthesize informative and high-quality sample features for unseen classes. To achieve this, we introduce class diversity promotion to enhance the diversity and discrimination of class semantic vectors. Ad- ditionally, we design two losses targeting the criteria of target class-guided information boosting to optimize the model. Through qualitative and quantitative analyses, we demonstrate that GenZSL consistently outperforms existing generative 7SL methods in terms of efficacy and efficiency.
Chen, S., Xie, G.-S,, Yang Liu, Y., Peng, Q. Sun, B., Li, H., You, X., and Shao, L. Hsva: Hierarchical semantic- visual adaptation for zero zero-shot learning. In NeurIPS, 2021b. 1, 6
Chen, S., Hong, Z., Liu, Y., Xie, G.-S,, Sun, B., Li, H., Peng. Q. Lu, K., and You, X. Transzero: Attribute-uided transformer for zero-shot learning. In AAAI, 2022a. 2, 6
Chen, S., Hong, Z., Xie, G.-.,, Yang, W., Peng, Q.. Wang, K., Zhao, J., and You, X. Msdn: Mutually semantic distillation network for zero-shot learning. In CVPR, 2022b. 2, 6
Impact Statement
Chen, S., Hou, W. Q., Hong, Z., Ding, X., Song, Y., You, X., Liu, T., and Zhang, K. Evolving semantic prototype improves generative zero-shot learning. In ICML, 2023a. 1, 6
Our induction-based generative method 1) offers new in- sights into ZSL and other generation tasks, 2) aligns with vision-language models (e.g.. CLIP) enable attribute-fre generalization, which paves the way for further advance- ments in ZSL, 3) bridges the gap between classical ZSL method (e.g., generative ZSL) and VLM-based methods CLIP),
Chen, S., Fu, D., Chen, S., Ye, S., Hou, W., and You, X. Causal vesual-semantic correlation for zero-shot learning. In ACM MM, pp. 4246-4255, 2024b. 6
Chen, S., Hou, W. Q., Khan, S. H., and Khan, F. S. Pro- gressive comantic--uuide vision transformer for zero-shot learning. In CVPR, 2024c. 2, 6
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GenZSL: Inductive Variational Autoencoder for Generative Zero-Shot Learning
Appendix organization:
Appendix A: Testing process of GenZSL.
Appendix B: Class semantic vectors' similarity heatmaps.
Appendix C: Generative ZSL with weak class semantic vectors.
Appendix D: t-SNE visualization on CUB and AWA2.
Appendix E: Hyper-parameter analysis SUN and AWA2.
A. Testing Process of GenZSL
We present the testing process of GenZSL in Fig. 7. Different to the standard VAE that samples the data from Gaussian noise, our Gen7SL. inducts the informative new sample features for unseen classes from the similar seen classes and takes Gaussian noises to enable IVAE to synthesize variable and diverse samples. Then, we take the synthesized unseen class samples & to learn a supervised classifier (e.g., softmax), which is used for ZSL evaluation further.
T fefer
A
Inductive Encuder
6.A. 13 F2
Inductive Decoder
Classifier
8
Figure
Testing process GenZSL.
B. Class Semantic Vectors' Similarity Heatmaps
We show the lass semantic vectors' similarity heatmaps of SUN and AWA2 in Fig. 8. Results show that our CDP effectively improves the discrimination and diversity for class semantic vectors, avoiding the confusion of synthesized visual features between various classes. For example, the mean similarity of class semantic vectors on AWA2 is reduced from 0.7609 to 0.0005. As such, the class semantic vectors served a distinct conditions for effective generation. Furthermore, we note that due to the number of classes in SUN is more than AWA2, the impact of self-similarity of classes in AWA2 is more heavier. This is why the mean similarity in AWA2 (coarse-grained dataset) is larger than SUN (fine-grained dataset).
Cluss Setrantic Vecsors Similarit Heaterup
Class Summonic Vectors Similarit Heaammu
Clus Scrmitic Victoon Similarity listtrap Simmaariy (0.2609)
Class Semuniic Vocker' Siillait 1kimmp
Miaa Similarit (8.3312)
Mean Similarity 663e 5
Saniaaii (02085)
Charstodo Todox
Class Iodex
Class Jades.
(a) CLIP text encoder
(b) CLIP w/ CDP
(c) CLIP text encoder
(d) CLIP w/ CDP
Figure 8, Class semantic vectors' similarity heatmaps are extracted by CLIP text encoder and CLIP with class diversity promotion on
SUN (a.b) and AWA2 (c.d).
12

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GenZSL: Inductive Variational Autoencoder for Generative Zero-Shot Learning
C. Generative ZSL Methods with Weak Class Semantic Vectors
We provide the results imagination-based ZSL (e.g., f-VAEGAN) ) and induction-based generative ZSL (e.g. GenZSL) using weak class semantic vectors (e.g.. CLIP text embeddings of class names) on SUN and AWA2. Results are shown in Table 5. We find that i) the performances of f-VAEGAN drop heavily on SUN (acc 64.7% - 45.2%; H 41.3% + 33.3%) and AWA2 (acc: : 71.1% - 67.1%; H 63.5% + 59.8%) when it uses the weak class semantic vector rather than the strong one (e.g,, expert-annotated attributes); ii) our GenZSL achieves significant performance gains over f-VAEGAN. These demonstrate that induction-based generative model is more feasible for ZSL than the imagination-based ones.
Table 5. Results of various generative ZSL methods with weak class semantic vectors on SUN and AWA2.
SUN
AWA2
Methods
CZSL
GZSL
CZSL
GZSL
acc
0
S
H
acc
0
S
H
f-VAEGAN (strong)
64.7
45.1
38.0
71.1 57.6 70.6 63.5
f-VEAGAN (weak)
45.2
32.4
34.3
33.3
67.0
43.3 83.2 59.8
GenZSL (weak)
73.5
50.6 43.8
47.0
92.2 86.1 88.7 87.4
D. t-SNE Visualization on CUB and AWA2
As shown in Fig. 9, I-SNE visualizations of visual features learned by the f-VABGAN and our GenZSL on CUB (a.b) and AWA2 (c,d). Analogously, the visual features generated by VAEGAN are also far away from their corresponding real ones, and the discrimination of these real/synthesized visual features is undesirable. In contrast, our GenZSI. synthesize visual features close to their corresponding real ones. As such, our GenZSL significantly improves the performances of f.VAEGAN on CUB and AWA2. This demonstrates that GenZSL is effective generative ZSL model.
E. Hyper-Parameter Analysis on SUN and AWA2
We analyze the effects of different hyper-parameters of our GenZSL. on SUN and AWA2 datasets. These hyper-parameters include the loss weight in Eq. the number the top referent classes top-k, and the number synthesized samples for each unseen class Naya. Fig. 10 shows the GZSL performances of using different syper-parameters. We observe that our GenZSL is robust and easy train. We empirically set these hyper-paramett {A, k, Negn as {0.001, 800) and 20.1,2,5000) for SUN and AWA2, respectively.
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GenZSL: Inductive Variational Autoencoder for Generative Zero-Shot Learning
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Figure 9. Qualitative evaluation with t-SNE visualization. The sample features from F-VARGAN shown the left, and from GenZSL are shown on the right. We use 10 colors to denote randomly selected 10 classes from CUB (a.b) and AWA2 (c.d). The "x" and "o" denoted the real and synthesized sample features, respectively. The synthesized sample features and the real features distribute differently on the left while distributing similarly on the right.
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GenZSL: Inductive Variational Autoencoder for Generative Zero-Shot Learning
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Figure Hyper- ppper-pareetter analysis. We show the performance variations loss weight the number of the top referent classes top-k, and the number of synthesized samples of each unseen class Nagn on SUN (a,b,c) and AWA2 (d.c.f).
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Reviews:
[{"review_id": "GEVbAK7KYf", "reviewer": "Reviewer_eZyG", "summary": "This paper introduces GenZSL for generative zero-shot learning. It first employs a class diversity promotion module to reduce redundant information in class semantic vectors. Additionally, a semantically similar sample selection module is used to select referent class samples. Experiments conducted on three popular benchmark datasets demonstrate the effectiveness of the proposed method. \n## update after rebuttal\nThe proposed method lacks comparison with recent approaches, and its performance is over 10% worse than a 2024 method (VADS) on two datasets. Additionally, the authors attribute the performance drop with more generated samples to \"limited diversity,\" which directly contradicts their claim of improving diversity. This explanation is unconvincing. Thus, I recommend rejecting the paper.", "questions": "", "limitations": "", "rating": 1, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Response:**\nThank you for reviewing our submission and the comments! Here are our responses to your concerns:\n\n**Q1:** Can the authors provide evidence that these vectors are also more diverse?\n\n**A1:** In Fig. 3 and Fig. 8, we present the class semantic vectors’ similarity heatmaps that are extracted by the CLIP text encoder and CLIP with our class diversity promotion (CDP). Results show that similarities between various class semantic vectors are smaller with CDP, such as the mean similarity between various classes drops from 0.5726 to 1.825$e^{-5}$ on CUB. That is, CDP makes the refined class semantic vectors nearly perpendicular to each other. As such, the refined class semantic vectors are more diverse.\n\n**Q2:** Can the authors discuss why the performance does not positively correlate with the amount of augmented data in Fig. 5(c)?\n\n**A2:** When the number of augmented data for unseen classes is set to small (e.g., smaller than 1600 on CUB), GenZSL can synthesize high-quality samples of unseen classes to effectively train a supervised classifier. However, if the number is set to too large, the model may fail to synthesize diverse unseen samples, resulting in overfitting to seen classes. This is because there exists an upper bound on synthetic diversity. We will add this discussion into Sec. 4.5 in the final version.\n\n**Q3:** Using weak class semantic vectors for feature generation has been explored in previous ZSL/FSL studies [1]. The sample selection module also aligns with prior works that transfer information from base classes to novel classes for data generation [2][3].\n\n\n**A3:** We should emphasize that our GenZSL is different from existing FSL studies. The reasons are illustrated below:\n\nFirst, [1] is an imagination-based generative model to synthesize data for data augmentation in FSL tasks, it selects representive samples to learn an imagination-based generative model (e.g., standard VAE) for data augmentation. On the contrary, our GenZSL is an induction-based ZSL model, which is an novel induction-based generative model mimicking human-level concept learning. It is effective in synthesizing high-quality samples for unseen classes. The imagination-based generative model synthesizes samples from scratch data, in which the generator learns from scratch without sufficient data to capture the high-dimensional data distribution.\n\nSecondly, [2][3] samples the top-k samples based on the data distribution of visual features, which requires the samples of novel classes. However, there are no samples of unseen classes in the ZSL task, and thus they can be used for FSL but not for ZSL. On the contrary, GenZSL is based on the class semantic vectors to select similar classes for synthesizing unseen samples. Accordingly, GenZSL can applied in the ZSL task, which is focused in this manuscript.\n\nTo avoid reader’s confusion, we will add these discussions into the final version.\n\n*[1]Xu & Le, Generating representative samples for few-shot classification, CVPR 2022.*\n\n*[2]Yang et al, Free Lunch for Few-shot Learning: Distribution Calibration, ICLR 2021.*\n\n*[3]Schwartz et al, ∆-encoder: an effective sample synthesis method for few-shot object recognition, NeurIPS 2018.*\n\n**Q4:** Adding GG (Cavazza et al., 2023) and VADS (Hou et al., 2024) into Tab. 2 for comparison?\n\n**A4:**  Yes, we will add these two works for comparison in Tab. 2.\n\n\n**Q5:** Can the authors provide some analysis for choosing temperature parameter in equation (4)?\n\n**A5:**  Initially, we set the temperature parameter $\\tau$ to 0.007 following [a] default. Following [4], we set $\\tau$ to 0.007/0.2/0.3/1.0 respectively on CUB for analysis. Results show that GenZSL achieves better performance when $\\tau$=0.007.\n\n| setting | U | S | H | acc | \n| :-----: | :-----: | :-----: | :-----: | :-----: | \n|GenZSL($\\tau$=0.007) | 53.5 | 61.9 | 57.4 | 63.3 |\n|GenZSL($\\tau$=0.02) | 50.4 | 65.7 | 57.0 | 63.0|\n|GenZSL($\\tau$=0.03) | 49.5 | 65.7 | 56.5 | 62.4|\n|GenZSL($\\tau$=1.0) | 49.1 | 65.7 | 56.2 | 62.4|\n\n*[4] \"Understanding the Behaviour of Contrastive Loss.\" In CVPR, 2021.*\n\n\n**Q6:** Can the authors also show the effectiveness of the semantically similar sample selection module?\n \n**A6:**  We conduct additional experiments on GenZSL without a semantically similar sample selection module, results show that the performances will decrease compared to GenZSL (full). We will add these results to the Tab. 3.\n\n*Results on CUB.*\n| Method | U | S | H | acc | \n| :-----: | :-----: | :-----: | :-----: | :-----: | \n| GenZSL w/o similar sample selection | 48.0 | 67.0 | 55.9 | 62.5|\n| GenZSL w/ similar sample selection | 53.5 | 61.9 | 57.4 | 63.3 |\n\n\n\n*Results on AWA2.*\n\n| Method | U | S | H | acc | \n| :-----: | :-----: | :-----: | :-----: | :-----: | \n| GenZSL w/o similar sample selection | 84.2 | 86.4 | 85.3 | 91.1|\n| GenZSL w/ similar sample selection | 86.1 | 88.7 | 87.4 | 92.2 |", "claims_and_evidence": "The authors claim that their proposed class diversity promotion (CDP) module enhances the diversity of class semantic vectors. However, Fig. 3 only shows that the vectors with CDP are less similar to each other. Can the authors provide evidence that these vectors are also more diverse?", "methods_and_evaluation_criteria": "Yes, the proposed method and the evaluation criteria make sense for the problem.", "theoretical_claims": "The paper does not provide formal proofs.", "experimental_designs_or_analyses": "Yes, I checked the ablation study in 4.2, the qualitative evaluation in 4.3, the comparison between induction-based generative ZSL  and imagination-based generative ZSL in 4.4., and hyper-parameter analysis in 4.5. Based on Fig. 5(c),  the accuracy fluctuates as the number of synthetic samples increase. Can the authors discuss why the performance does not positively correlate with the amount of augmented data?", "relation_to_broader_scientific_literature": "Using weak class semantic vectors for feature generation has been explored in previous ZSL/FSL studies [1]. The sample selection module also aligns with prior works that transfer information from base classes to novel classes for data generation [2][3].\n\n[1]. Xu & Le, Generating representative samples for few-shot classification, CVPR 2022\n\n[2]. Yang et al, Free Lunch for Few-shot Learning: Distribution Calibration, ICLR 2021\n\n[3]. Schwartz et al, ∆-encoder: an effective sample synthesis method for few-shot object recognition, NeurIPS 2018", "essential_references_not_discussed": "In Tab. 2, only one method from 2023 is listed for comparison. Can the authors also include comparison with these works?\n\n[1] Hou et al, Visual-Augmented Dynamic Semantic Prototype for Generative Zero-Shot Learning, CVPR 2024\n\n[2] Cavazza et al, No adversaries to zero-shot learning: Distilling an ensemble of gaussian feature generators, TPAMI 2023", "other_strengths_and_weaknesses": "Strengths:\n1. The proposed method is simple and easy to implement.\n2. It outperforms previous methods on various datasets.\n\nWeaknesses:\n1. The temperature parameter in equation (4) is set to 0.07. How is this value determined? Can the authors provide some analysis for choosing this specific parameter?\n2. In Section 4.2, the authors provide an analysis of the class diversity promotion module, the target class reconstruction loss, and the target class-guided information boosting loss. Can the authors also show the effectiveness of the semantically similar sample selection module?", "other_comments_or_suggestions": "N/A.", "questions_for_authors": "N/A.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "rkl1I5jTjb", "reviewer": "Reviewer_omKE", "summary": "This paper introduces ​GenZSL, a novel inductive framework for generative zero-shot learning (ZSL) that addresses limitations in existing generative ZSL methods. Traditional approaches generate visual features \"from scratch\" using expert-annotated class semantic vectors, leading to suboptimal performance and poor generalization. Inspired by human concept induction, GenZSL synthesizes unseen class features by ​inductively transforming similar seen-class samples guided by weak semantic vectors (e.g., CLIP text embeddings of class names) and class diversity promotion, achieving state-of-the-art accuracy.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Response:**\nThank you for the comprehensive reviews and detailed comments! We are very happy to help to address your concerns!\n\n\n**Q1:** The claim for CDP lacks quantitative validation (e.g., semantic similarity metrics pre/post-CDP)\n\n**A1:** The quantitative validation for CDP is presented below, we take these results in ablation study section. Results show that the CDP effectively improves the performance of GenZSL.\n\n*Here are the results of pre-CDP.*\n\n| Dataset | U | S | H | acc | \n| :-----: | :-----: | :-----: | :-----: | :-----: | \n| CUB | 48.2 | 64.6 | 55.2 | 60.9|\n| AWA2 | 82.3 |87.9 | 85.0| 90.7|\n\n\n*Here are the results of post-CDP.*\n\n| Dataset | U | S | H | acc | \n| :-----: | :-----: | :-----: | :-----: | :-----: | \n| CUB | 53.5 | 61.9| 57.4| 63.3 |\n| AWA2 | 86.1 |88.7| 87.4 | 92.2|\n\n\n**Q2:** Speed comparisons (Fig. 6) lack details on hardware parity or implementation optimizations.\n\n**A2:** We implement the results in Fig. 6 on a single NVIDIA RTX 3090 graphic card with 24-GB memory without any further implementation optimization following the official codes. Furthoremore, our GenZSL inductives the unseen samples from the similar seen classess with the guidance of target class semantic vectors, which is more easy to learn the target distribution than the imagination-based generative models that learns from the scratch (e.g., Gaussian distribution). As such, GenZSL learns target distribution of unseen classes efficiently. We will further highlight these discussions in the final version.\n \n\n**Q3:**  Sensitivity to top-k is untested on SUN.\n\n**A3:**  Due to the page limitation, the hyperparater analysis on SUN and AWA2 are presented in Appendix E. Specicfically, the evaluation of top-k on SUN is presented on Fig. 10(b).\n\n\n**Q4:** Are gains due to induction or CLIP’s inherent cross-modal alignment?.\n\n**A4:**  As shown in Tab. 4, GenZSL achieves improvements over CLIP-based models (e.g., CoOp, CoOp+SHIP). Meanwhile, GenZSL also outperforms the imagination-based generative model with identical class semantic vectors (e.g., CLIP embeddings). Due to the different dimensions between CLIP visual features and human-annotated attributes, GenZSL cannot be implemented based on them. As such, we cannot provide the results of GenZSL (strong) similar to f-VAEGAN in Tab. 5. However, f-VAEGAN (strong) obtains better performances than f-VAEGAN (weak), which demonstrates human-annotated attributes could be a better semantic condition in generative ZSL than CLIP embeddings. Furthermore, GenZSL (weak) achieves performance gains over f-VAEGAN with various semantic vectors. These results demonstrate the effectiveness of the induction mechanism in generative ZSL.\n\nWe will further highlight these discussions in the final version.\n\n\n**Q5:** Comparisons between embedding-based and generative methods.\n\n**A5:**  In Tab. 1, we mainly categorize the compared methods based on the visual features extracted from ViT or CNN structure, and CLIP visual features are also extracted from the ViT network structure. Furthermore, we also compared GenZSL with VADS (Hou et al., 2024) which uses CLIP visual features. Results show the superior performances of GenZSL.\n\n\n**Q6:** Comparison between description based ZSL vs. class-name based ZSL.\n\n**A6:**  Thank you for this helpful comment. Indeed, we take description based ZSL (e.g., I2MVFormer-Wiki (Naeem et al., 2023)  and I2MVFormer+ (Naeem et al., 2024)  into comparison under the CZSL setting in Tab. 1. Recently, TPR (Chen et al., 2024)  provided comprehensive results for description based ZSL under the GZSL setting. We will take these description based GZSL methods (especially prompt based methods, e.g., TPR, SHIP, PromptSRC, MaPLe) into discussions in the final version.\n\n**Q7:** Fig.1 caption line 4: strong should be limited.\n\n**A7:**  Thank you for this helpful comment, we will update it.", "claims_and_evidence": "1. \"Class Diversity Promotion (CDP) preserves original class relationships\". CDP’s SVD-based orthogonalization (Eq. 1) removes redundancy but risks distorting semantic relationships. The claim lacks ​quantitative validation (e.g., semantic similarity metrics pre/post-CDP).\n2. \"60× faster training\". Speed comparisons (Fig. 6) lack details on hardware parity or implementation optimizations. GANs are notoriously slower, so gains may reflect architectural simplicity rather than algorithmic superiority.\n3. Robustness to hyperparameters (Fig. 5). Results show stability for $λ$ and $N_{syn}$, but ​top-k referent classes (critical for induction) are fixed to k=2. Sensitivity to k is untested on fine-grained datasets (e.g., SUN’s 717 classes).", "methods_and_evaluation_criteria": "Yes", "theoretical_claims": "NA", "experimental_designs_or_analyses": "1. While GenZSL outperforms methods using strong semantics (Table 5), the paper does ​not isolate the impact of semantic source (CLIP embeddings vs. attributes). Are gains due to induction or CLIP’s inherent cross-modal alignment? And there is no comparison to hybrid approaches (e.g., CLIP + expert attributes).\n2. The authors compare with embedding-based and generative methods. They use CLIP features, which might give an unfair advantage because other methods don't use similar features. \n3. Overreliance on CLIP’s implicit alignment: Attributes gains to \"induction\" but does ​not isolate CLIP’s role. No experiments compare 1) GenZSL with CLIP vs. expert-annotated attributes and 2) induction vs. imagination using identical semantic vectors.\n4. Description based ZSL vs. class-name based ZSL. Recently, there have been methods proposed to use natural language descriptions as semantic information. What is the comparison result with these methods under this setting?\n[a] TPR: Topology-Preserving Reservoirs for Generalized Zero-Shot Learning, NIPS 2024.", "relation_to_broader_scientific_literature": "GenZSL bridges cognitive science-inspired induction mechanisms (e.g., Bayesian concept learning with generative ZSL, advancing prior imagination-based methods (e.g., f-VAEGAN) by replacing expert-dependent attributes with scalable CLIP-guided induction, while aligning with vision-language models (e.g., CLIP) to enable attribute-free generalization.", "essential_references_not_discussed": "The authors should compare with more prompt learning based methods, such as SHIP, PromptSRC, Maple.", "other_strengths_and_weaknesses": "No", "other_comments_or_suggestions": "Fig.1 caption line 4: strong should be limited.", "questions_for_authors": "No", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "M5Aqq6qFcT", "reviewer": "Reviewer_vHk3", "summary": "This paper proposes a novel generative paradigm for zero-shot learning (GenZSL), which is based on the idea of induction rather than imagination. To ensure the generation of informative samples for training an effective ZSL classifier, GenZSL incorporates two key strategies, e.g., class diversity promotion and target class-guided information boosting criteria. The experiment results are extensive and meaningful.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Response:**\nThank you for the comprehensive reviews and detailed comments! We are very happy to help to address your concerns!\n\n\n**Q1:** If there are not similar classes for the unseen classses in the seen class set, the method may out of work?\n\n**A1:** Thank you for this constructive comment. In response to the Q6 of Reviewer eZyG, if we randomly sample the seen class samples to synthesize unseen class samples, the performances of GenZSL may drop slightly, i.e., CUB (acc: 63.3%$\\rightarrow$62.5%; H: 57.4%$\\rightarrow$55.9%) and AWA2  (acc: 92.2%$\\rightarrow$91.1%; H: 87.4%$\\rightarrow$85.3%). These results show that GenZSL may not heavily rely on the similar samples from seen classes for synthesizing unseen class samples, and the similar classes for unseen classes in the seen class set can further improve GenZSL.\n\n**Q2:** In Sec 3, the authors state that the refinement of text embeddings preserves the semantic relationships between classes. Is there any theoretical evidence or empirical analysis to validate this statement?\n\n**A2:**  As shown in Fig. 3 and Fig. 8, the qualitative results show that the high similarities of two classes, they still keep relatively high similarities after refinement using CDP. We statistic the similarities between class semantic vectors of pre/post-CDP, and we find that they keep similar class relationships. We will further highlight this in Sec. 3.1.\n\n\n**Q3:** Provide more discussions on inconsistent performances on Tab. 2.\n\n**A3:**  As shown in Tab. 2, GenZSL achieves better improvements on SUN and AWA2 over CUB. This is because CUB is a fine-grained dataset consisting of 200 bird classes, which are very similar to each other for all classes, GenZSL inevitably synthesizes similar classes for various unseen classes. That is, the diversity of the synthesized samples is limited, and thus the performance gains are not significant. Although SUN is also a fine-grained dataset, it consists of more classes (717 classes) and has higher diversities between various classes. Meanwhile, AWA2 is a coarse-grained dataset, all classes also have higher diversities. Accordingly, GenZSL obtains better performance gains on SUN and AWA2.\n\n**Q4:** In Tale 4, the unseen class performance of GenZSL falls short of standalone CLIP results. The improved seen class performance, while valuable, somewhat diverges from the primary objectives of zero-shot learning.\n\n\n**A4:**  In the GZSL setting, the goal of ZSL methods is to achieve good performance both in seen and unseen classes. That is, we mainly evaluate the harmonic mean in GZSL. The unseen accuracy of GenZSL slightly drops over CLIP on CUB, but it significantly improves the performance on seen classes (i.e., 7.1% improvement). This means that GenZSL well address the seen-unseen bias issue for GZSL. We will add these discussions to the final version.", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "Yes", "theoretical_claims": "Yes", "experimental_designs_or_analyses": "Yes", "relation_to_broader_scientific_literature": "Please see my detailed comments in strengths and weaknesses.", "essential_references_not_discussed": "No", "other_strengths_and_weaknesses": "Strengths:\n+ This paper well-organized, and the motivation is clear and interesting.\n+ The technical contributions are novel and clearly presented.\n+ The extensive results demonstrate the effectiveness of the proposed method.\n\n\nWeaknesses:\n- The method is based on the assumptions that the target classes are inducted from the similar referent classes. If there are not similar classes for the unseen classses in the seen class set, the method may out of work?\n- In Sec 3, the authors state that the refinement of text embeddings preserves the semantic relationships between classes. Is there any theoretical evidence or empirical analysis to validate this statement?\n- As shown in Table 2, the GenZSL achieves sota results on SUN and AWA2, except for CUB. Please provide more discussions on such inconsistent performances.\n - In Tale 4, the unseen class performance of GenZSL falls short of standalone CLIP results. The improved seen class performance, while valuable, somewhat diverges from the primary objectives of zero-shot learning.", "other_comments_or_suggestions": "No", "questions_for_authors": "Please see my detailed comments in strengths and weaknesses.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper proposes a generative zero shot learning (GenZSL) method for generating samples for unseen classes using their weak CLIP embeddings via induction from the most similar seen classes. For ZSL from weak labels it shows SOTA performance. Three reviewers provided scores of "Reject", "Weak Accept" and "Weak Accept". The AC carefully read the paper, all reviews, the authors' responses to the reviewers' comments, as well as all discussion between the reviewers and the authors. The main concerns raised by the reviewers were around the lack of apples-to-apples comparison in the strong semantic label setting to the prior VADS method. Two reviewers raised this concern. However, the authors did not provide this comparison. Given this, while the method is novel and intuitive, and has been shown to improve performance in the weak semantic information setting of using CLIP embeddings, it has not been adequately verified to improve performance also in the strong semantic label setting. Hence, the AC feels that while it advances research in the field of ZSL, its performance has only been partially validated. All things considered, the AC feel that this paper is at the borderline of acceptance and recommends "Weal accept". It would not be bad to reject it if there isn't space in the program.
Decision: Accept (poster)