SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Training settings For all experiments, datasets, and learning strategies, we use ViT-B/32 as the vision backbone. For both visual and textual prompt learning, we set the prefix size to 16 [48, 18]. Multimodal prompts have length 8 [44]. We use SGD as the optimizer and train for 150 epochs. We use 5 warmup epochs at a learning rate of 0.0001, and then set the learning rate to  $l$ , which is decayed by the cosine annealing rule. For textual and visual prompt learning,  $l = 0.1$ , while for multimodal prompt learning,  $l = 0.01$ . In SSL, we use 2 labeled samples per class to assess the impact of pseudolabels in the scenario of very few labeled data and abundant unlabeled data. The number of iterations  $I$  is 10. FPL and IFPL have the number of pseudolabels per class fixed to 16 since it is indicated as the optimal  $K$  in the previous research on pseudolabeling with CLIP [15]. In general,  $K$  is a hyperparameter that may require optimization in practical cases. We decide to be consistent with the literature and apply this fixed value of  $K$  in order to reduce the addition of more confounding factors in our analysis.
4.1 Exploring the design space
GRIP consistently enhances CLIP across prompt modalities and learning settings Table 1 reports the performance of GRIP, the best performing among the training strategies in Section 3.3, compared to CLIP and prompt-tuning baselines. Overall, GRIP consistently improves the performance of CLIP and the baselines across prompt modalities and learning settings. By tuning textual prompts, the average improvement over CLIP is 20.7 points in SSL, 14.9 in UL, and 32.4 in TRZSL, while the improvement on CoOp is 9.6 points in SSL, and 26.6 in TRZSL. Similar results for the visual prompts show that GRIP improves CLIP by 18.2 points in SSL, 15.7 in UL, and 30.8 in TRZSL, and VPT by 12.9 points in SSL, and 20.8 in TRZSL. We note that CoOp and VPT applied to the SSL setting correspond to learning only on the labeled data, and we do not run them in the UL setting as
Textual prompts
Method	Flowers102	RESICS45	FGVCAircraft
SSL	UL	TRZSL	SSL	UL	TRZSL	SSL	UL	TRZSL
CLIP	63.670.00	63.400.00	54.480.00	54.460.00	17.580.00	17.860.00
CoOp	76.761.11	-	63.220.02	58.5310.81	-	63.370.02	14.913.22	-	21.700.03
GRIP	83.6068	69.841.06	86.260.00	74.110.68	70.550.88	81.070.00	16.980.82	15.220.71	26.080.00
Δ CLIP	↑19.93	↑6.17	↑22.86	↑19.63	↑16.07	↑26.61	↓0.6	↓2.36	↑8.22
Δ CoOp	↑6.84	-	↑23.04	↑15.58	-	↑17.70	↑2.07	-	↑4.38
MNIST	EuroSAT	DTD
CLIP	25.100.00	20.770.00	32.880.00	30.540.00	43.240.00	43.450.00
CoOp	56.422.66	-	21.150.09	59.514.55	-	49.680.08	37.105.45	-	46.300.03
GRIP	71.783.59	67.882.76	74.060.00	58.662.64	57.211.77	92.330.00	56.070.85	46.091.06	65.300.01
Δ CLIP	↑46.68	↑42.78	↑53.29	↑25.78	↑24.33	↑61.79	↑12.83	↑2.85	↑21.85
Δ CoOp	↑15.36	-	↑52.91	↓0.85	-	↑42.65	↑18.97	-	↑19.00
Visual prompts
Method	Flowers102	RESICS45	FGVCAircraft
SSL	UL	TRZSL	SSL	UL	TRZSL	SSL	UL	TRZSL
CLIP	63.670.00	63.400.00	54.480.00	54.460.00	17.580.00	17.860.00
VPT	63.731.52	-	64.710.00	60.801.65	-	67.060.00	17.760.68	-	26.690.00
GRIP	67.951.2	63.090.55	77.180.00	71.220.77	68.430.61	82.190.00	19.430.5	17.510.61	26.420.00
Δ CLIP	↑4.28	↓0.58	↑13.78	↑16.74	↑13.95	↑27.73	↑1.85	↓0.07	↑8.56
Δ VPT	↑4.22	-	↑12.47	↑10.42	-	↑15.13	↑1.67	-	↓0.27
MNIST	EuroSAT	DTD
CLIP	25.100.00	20.770.00	32.880.00	30.540.00	43.240.00	43.450.00
VPT	42.5314.13	-	25.510.05	47.131.34	-	62.240.02	36.412.17	-	44.160.01
GRIP	69.665.51	68.041.11	69.540.01	63.483.09	63.683.42	96.970.00	54.574.86	50.510.99	62.780.00
Δ CLIP	↑44.56	↑42.94	↑48.77	↑30.60	↑30.80	↑66.43	↑11.33	↑7.27	↑19.33
Δ VPT	↑27.14	-	↑44.03	↑16.35	-	↑34.73	↑18.16	-	↑18.62
Table 1: For each learning paradigm, we compare the accuracy of GRIP with CLIP zero-shot (ViT-B/32), CoOp, and VPT. Results are for SSL, UL, and TRZSL on FRAMED. We average the accuracy on 5 seeds and report the standard deviation.  $\Delta$  METHOD is the difference between the accuracy of GRIP and METHOD. We note that for UL we can not apply CoOp and VPT since no labeled data is available.
there is no labeled data. Results are similar for multimodal prompts. We defer them to Appendix A.3, due to space constraints.
No prompt modality is clearly superior Using pseudolabels dynamically is beneficial for each modality. However, determining the clear superiority of one prompt modality over the other is challenging, as it depends on the specific tasks. For example, visual prompts work better for EuroSAT, while textual prompts excel in Flowers102. Despite intuitive explanations (Section 3.1), the scientific consensus remains elusive [44]. Hence, we prefer to emphasize that the dynamic use of pseudolabels consistently improves performance for each prompt modality, without declaring one modality as definitively better than the other.
Unsupervised learning is equivalent or more robust than learning with very few shots The accuracy of GRIP when applied to the fully unsupervised setting is either higher or equivalent to the accuracy of VPT, which
is trained using two labeled instances per class (Table 1). This shows that pseudolabeled data can substitute very few labeled examples for prompt tuning. However, the significant improvement of GRIP over CoOp and VPT in the semi-supervised setting (see Table 1) suggests that leveraging unlabeled data through pseudolabeling is advantageous in scenarios where labeled data is scarce but there is an abundance of unlabeled data.
Figure 2: Balance of seen and unseen accuracies vs. model's overall accuracy. Points close to 0 indicate a good balance. Negatives represent better accuracy for the seen classes.
Textual prompts
Flowers102	RESICS45	DTD
Method	SSL	UL	TRZSL	SSL	UL	TRZSL	SSL	UL	TRZSL
FPL	75.960.74	65.670.23	80.970.00	68.130.55	63.070.38	72.110.00	37.105.45	44.960.55	46.30.03
IFPL	78.680.75	69.561.05	82.080.00	70.521.12	64.110.98	75.510.00	55.240.97	47.771.15	59.140.02
GRIP	83.600.48	69.841.06	86.260.00	74.110.68	70.550.88	81.070.00	56.070.85	46.091.06	65.300.01
Δ IFPL	↑2.72	↑3.89	↑1.11	↑2.39	↑1.07	↑3.4	↑18.14	↑2.81	↑12.84
Δ GRIP	↑7.64	↑4.17	↑5.29	↑5.89	↑7.48	↑8.96	↑18.97	↑1.13	↑19.00
Visual prompts
FPL	67.030.65	65.500.41	71.940.00	65.140.25	62.240.22	67.850.00	47.601.09	47.690.48	52.430.00
IFPL	68.690.45	66.120.46	76.910.00	67.111.19	62.931.23	73.530.00	51.650.70	50.340.65	57.860.01
GRIP	67.951.2	63.090.56	77.180.00	71.220.77	68.430.61	82.190.00	54.574.86	50.510.99	62.780.00
Δ IFPL	↑1.66	↓0.38	↑4.97	↑1.97	↑0.69	↑5.68	↑4.05	↑2.65	↑5.43
Δ GRIP	↑0.92	↓3.41	↑5.24	↑6.08	↑6.19	↑14.34	↑6.97	↑2.82	↑10.35
Multimodal prompts
FPL	72.540.36	65.260.38	77.470.00	62.841.05	62.320.65	71.430.00	43.712.19	44.850.31	54.860.00
IFPL	73.140.87	65.391.33	81.470.00	70.601.04	63.690.53	46.040.36	53.211.24	47.591.04	43.170.25
GRIP	74.562.02	64.821.63	82.010.00	73.780.91	69.370.61	82.170.00	54.072.25	47.370.70	63.420.00
Δ IFPL	↑1.91	↑0.13	↑4.00	↑7.76	↑1.37	↓25.39	↑9.5	↑2.74	↓11.69
Δ GRIP	↑2.02	↓0.44	↑4.54	↑10.84	↑7.05	↑10.74	↑10.36	↑2.52	↑8.56
Table 2: For each learning paradigm, we compare FPL, IFPL, and GRIP on Flowers102, RESICS45, and DTD, for all the learning settings SSL, UL, TRZSL. We average across 5 runs and report the standard deviation.  $\Delta$  METHOD is the difference between the accuracy of FPL and METHOD.
Transductive zero-shot learning effectively transfers knowledge In the TRZSL setting, GRIP improves over CLIP and the baselines by a large margin (Table 1). Figure 2 displays the balance of seen and unseen classes of each method alongside its accuracy. The class balance is  $(acc_{\text{seen}} - acc_{\text{seen}}) / acc_{\text{seen}}$ , where values close to zero indicate a good balance, negative values indicate better accuracies for seen classes, and positive values indicate better accuracies for unseen classes. Methods employing an iterative usage of pseudolabels maintain a good balance, as opposed to CoOp/VPT and FPL. This balance in accuracy is likely a combined effect of the quality of the pseudolabels and the transfer of knowledge from the seen to the unseen classes. The latter point is significant because it implies that even if we only possess unlabeled data for a specific target task, we can still use labeled data from related classes [30] within the same domain to enhance CLIP's performance.
(a) Flowers102
(b) RESICS45
Figure 3: Evolution of pseudolabels accuracy during training. The rows refer to SSL, UL, and TRZSL, in order. IFPL refers to the top x-axis, while CLIP and GRIP to the bottom.
There is a trade-off between quality and quantity of pseudolabels Table 2 shows the performance of CLIP employing prompts learned with different training strategies, all leveraging pseudolabels (Section 3.3). Iterative strategies are more effective than FPL which, similarly to [15], use a static set of a few pseudolabels for one iterat
On Flowers102, RESICS45, and DTD, IFPL improves on average FPL by 5.6 points in SSL, 1.7 in UL, and 5.6 in TRZSL. GRIP boosts the performance even more by on average 7.8 points in SSL, 3.1 in UL, and 9.7 in TRZSL. Results on the other tasks are comparable or larger and we report them in Appendix A.3 due to space constraints.
Figure 3 shows the progression of pseudolabels quality for the iterative learning of textual prompts. IFPL maintains a fixed set of 16 pseudolabels, improving their quality with each iteration (top x-axis).
On the other hand, GRIP and CLIP expand pseudolabels by incorporating an additional decile of unlabeled data in each iteration (bottom x-axis). Initially, GRIP maintains accuracy, but as it nears completion the quality tends to decrease, while a larger dataset with good-quality pseudolabels becomes available.
Comparing GRIP and CLIP, GRIP's expanded pseudolabels exhibit superior quality and performs better (Table 1). Even though IFPL's pseudolabel accuracy surpasses GRIP in the final iteration, GRIP's overall performance remains better due to training on a larger number of pseudolabels (Table 2). This suggests that numerous, slightly noisier pseudolabels can yield better results, highlighting a trade-off and offering insights for future approaches.
GRIP benefits adaptation even for larger image encoders We measure how much the effect of the iterative strategies changes if we consider a larger pre-trained image encoder. In Table 3, we report the average improvements of GRIP on CLIP for Flowers102, RESICS45, and DTD. The magnitude of improvements slightly decreases when using a larger image encoder. However, we still see significant benefits for both modalities. The smaller relative improvements with respect to smaller visual encoders align with our expectations. Larger encoders possess a stronger base knowledge, making it relatively more challenging to attain further improvements on top of it. In Table 10, we break
down the accuracy of CLIP with different backbones. The performance of the larger backbone is higher, indicating higher quality pseudolabels.
Textual prompts
SSL	UL	TRZSL
Avg. Δ CLIP (ViT-B/32)	17.4612.83	8.362.85	23.772.51
Avg. Δ CLIP (ViT-L/14)	15.856.44	8.166.12	19.966.19
Visual prompts
SSL	UL	TRZSL
Avg. Δ CLIP (ViT-B/32)	10.784.28	6.88-0.58	20.2813.78
Avg. Δ CLIP (ViT-L/14)	7.613.27	4.89-0.48	16.1411.13
Table 3: Average improvement of GRIP with different backbones on Flowers102, RESICS45, and DTD.  $\Delta$  CLIP is the difference between the accuracy of GRIP and CLIP. Alongside the average, we provide the minimum improvement across tasks.
4.2 The Robin Hood effect
Although training models with pseudolabels can lead to good performance, it can also result in biased predictions and generate disparate impacts on sub-populations, i.e., the "Matthew effect" [49, 8]. Particularly, the use of pseudolabels can lead to improved performance in well-behaved (high accuracy) classes but can cause stagnation or decreased performance in poorly behaved (low accuracy) classes. As we explore the use of pseudolabels, we investigate how the accuracy of the analyzed approaches distributes across classes. Figure 4 show an opposite scenario from typical SSL. The solid line represents the sorted per-class accuracies of CLIP. The arrows indicate the per-class accuracies of GRIP. For all learning paradigms, the iterative training strategies increase the accuracy of classes where CLIP is not proficient, while maintaining or decreasing the accuracy of initially well-behaved classes. This effect, which we call the "Robin Hood effect," is very interesting because it shows how CLIP can mitigate its own bias toward certain classes by learning from itself.
To understand the roots of the Robin Hood effect, we examine two factors: (1) the role of pseudolabels generated by CLIP, and (2) the role of prompt tuning. To disentangle these factors, we explore the variation in per-class accuracy of a basic linear classifier trained on CLIP's ViT-B/32 image representation.
"Second generation" pseudolabels are a good treatment for class disparity We train the linear classifier in the SSL setting
on 2 labeled examples per class and pseudolabels. The pseudolabels are obtained through conventional methods, where a threshold of .95 is applied, or by using CLIP to generate 16 pseudolabels per class.
(a) FPL
Figure 4: Improvements of FPL and GRIP on CLIP's per-class accuracies (RESICS45). The x-axis is the ranked class index, while y-axis is the accuracy.
(b) GRIP
We find that both approaches yield similar overall accuracies. However, we observe the "Matthew effect" when using the first approach. In contrast, when using CLIP-based pseudolabels, the class disparity of the regressor trained solely on seen classes is reduced. Particularly, we see a significant improvement on initially poor classes, together with a significant diminishing of the accuracy of well-behaved classes. We observe a clear manifestation of the "Robin Hood effect." We present plots illustrating this effect in Appendix A.4.
Prompt tuning retains the accuracy of already rich classes better than linear probing To evaluate the role of prompt tuning in the "Robin Hood effect," we train a linear classifier and textual prompts in the UL setting using GRIP's training strategy. Comparing the per-class accuracies of the two approaches, GRIP on prompts shows an average improvement of 22.85 points for the poor classes across tasks, along with a slight average decrease of 0.3 points for the rich classes. On the other hand, linear probing determines a 14.42 points improvement for the poor classes, but it results in an average decrease of 9.39 points in accuracy for the rich classes (Appendix A.4).
We show that prompt tuning using pseudolabels generated by CLIP itself is a successful approach to enhance CLIP across various learning settings. Training strategies that iteratively refine pseudolabels turn out to be effective ways of leveraging pseudolabeled data. These approaches not only enhance CLIP's accuracy but also mitigate model biases toward certain classes. We hope this work lays a solid groundwork for reducing reliance on labeled data when adapting pre-trained vision-language models like CLIP to new tasks.
**Limitations** The effectiveness of the training strategies examined in this paper depends on both the strategies themselves and the quality of pseudolabels. The latter is particularly crucial. If CLIP performs poorly on a task, we may struggle to obtain a reliable set of pseudolabels to begin with, potentially diminishing CLIP's performance. Despite this potential risk, we have not observed any relevant failure of GRIP, even in tasks where CLIP's initial accuracy is extremely low (such as FGVCAircraft). Also, the pseudolabeling strategy we adopt involves selecting  $K$  pseudolabels per class, which can create a strong assumption about the distribution of the training data if we attempt to cover all unlabeled data. During the final iteration, it is as if we assume a uniform class balance.
Another important consideration is the efficiency of the explored methods. Repeating the training process multiple times brings impressive improvements at the cost of a non-negligible increase of computation time. At each iteration, we generate pseudolabels for the unlabeled data from scratch. While we parallelized the pseudolabeling procedure to cut some of the cost, reducing those for the iterative training presents more significant challenges. We decided to mainly focus on the analysis of qualitative and quantitative effects of pseudolabels in prompt tuning. Future research should address budget constraints and investigate optimal stopping criteria for the iterative process, considering the possibility of reaching a plateau or decreased pseudolabel quality after a certain point, to maximize efficiency while maintaining performance.
We are thankful to our reviewers for the fruitful and insightful discussions that contributed to the refinement of the paper. We also thank Reza Esfandiarpoor and Zheng-Xin Yong for the comments on our drafts. This material is based on research sponsored by Defense Advanced Research Projects Agency (DARPA) and Air Force Research Laboratory (AFRL) under agreement number FA8750-19-2-1006. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of Defense Advanced Research Projects Agency (DARPA) and Air Force Research Laboratory (AFRL) or the U.S. Government. We gratefully acknowledge support from Google and Cisco. Disclosure: Stephen Bach is an advisor to Snorkel AI, a company that provides software and services for data-centric artificial intelligence.
[42] Yunlong Yu, Zhong Ji, Xi Li, Jichang Guo, Zhongfei Zhang, Haibin Ling, and Fei Wu. Transductive zero-shot learning with a self-training dictionary approach. IEEE Transactions on Cybernetics, 48(10):2908-2919, 2018.
[43] Lu Yuan, Dongdong Chen, Yi-Ling Chen, Noel C. F. Codella, Xiyang Dai, Jianfeng Gao, Houdong Hu, Xuedong Huang, Boxin Li, Chunyuan Li, Ce Liu, Mengchen Liu, Zicheng Liu, Yumao Lu, Yu Shi, Lijuan Wang, Jianfeng Wang, Bin Xiao, Zhen Xiao, Jianwei Yang, Michael Zeng, Luowei Zhou, and Pengchuan Zhang. Florence: A new foundation model for computer vision. ArXiv, abs/2111.11432, 2021.
[44] Yuhang Zang, Wei Li, Kaiyang Zhou, Chen Huang, and Chen Change Loy. Unified vision and language prompt learning. ArXiv, abs/2210.07225, 2022.
[45] Bowen Zhang, Yidong Wang, Wenxin Hou, HAO WU, Jindong Wang, Manabu Okumura, and Takahiro Shinozaki. Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling. In Advances in Neural Information Processing Systems, 2021.
[46] X. Zhang, Yusuke Iwasawa, Yutaka Matsuo, and Shixiang Shane Gu. Domain prompt learning for efficiently adapting clip to unseen domains. 2021.
[47] Tony Z. Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. Calibrate before use: Improving few-shot performance of language models. 2021.
[48] Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Learning to prompt for vision-language models. International Journal of Computer Vision, 2021.
[49] Zhaowei Zhu, Tianyi Luo, and Yang Liu. The rich get richer: Disparate impact of semi-supervised learning. In International Conference on Learning Representations, 2022.
We include here extra information that supports the results presented in the main body of the paper.
Reproducibility We have provided the code to run the experiments as supplementary material for the submission. However, we plan to release it as an open repository upon acceptance.
A.1 Trainable Prompts
Text Prompt Tuning The primary objective of text prompt tuning is to improve the alignment between the class token and the image features extracted by the image encoder. This is achieved by adding learnable vectors, i.e., prefix, before the CLASS token to create a contextualized representation. Specifically, the sequence
$$
\boldsymbol {t} = [ \mathrm {V} ] _ {1} [ \mathrm {V} ] _ {2} \dots [ \mathrm {V} ] _ {M} [ \mathrm {C L A S S} ]
$$
is fed into the textual encoder, where each vector  $[\mathrm{V}]_m$  ( $m \in 1, \dots, M$ ) has the same dimension as word embeddings, and  $M$  is a hyperparameter that determines the length of the prefix.
Context Optimization (CoOp) [48] was the first work to explore continuous prompts for VLMs. Follow-up works have experimented with different training strategies to enhance the generalizability of the learned prompts while preserving the core concept of continuous vector tuning [34, 12, 27, 46, 13, 37].
Tuning the text prefix vector changes the resulting  $n$  linear weight vectors  $w_{i} = \psi(p_{i})$ , while leaving the image features unchanged. Therefore, text prompt tuning may be most beneficial when image features are well-separated by class but may not be aligned with the corresponding textual prompt. Conversely, text prompt tuning may not be as effective when the image features are poorly separated, as in specialized or novel domains where CLIP may lack sufficient training data.
Visual Prompt Tuning Instead of tuning the text prompts, one can also tune the inputs of the vision encoder. In this case, a learnable visual prefix is prepended to the image tokens as input to the image transformer as follows:
$$
\hat {\boldsymbol {I}} = [ p ] _ {1} \dots [ p ] _ {K} [ I ] _ {1} \dots [ I ] _ {P}
$$
where  $p$  represents a sequence of  $K$  learnable prefix vectors, and  $[\mathrm{I}]_1 \ldots [\mathrm{I}]_P$  are the image tokens from the corresponding  $P$  patches of the input images. The new sequence  $\hat{\pmb{I}}$  is the input to the image encoder  $\phi$ .
Visual Prompt Tuning (VPT) was introduced in the context of efficiently adapting pre-trained vision transformers to downstream tasks [18]. However, the approach has since been applied in the context of VLM [34].
Whereas text prompt tuning does not alter the image features, visual prompt tuning does. By rearranging the image features within the projection space, VPT has the potential to improve CLIP when the image features are not well separated by class, such as in specialized domains.
Multimodal Prompt Tuning The previous approaches are unimodal, as they either involve modifying the text or visual input, but never both. This choice may be suboptimal as it does not allow the flexibility to dynamically adjust both representations on a downstream task. Recently, multimodal prompt tuning has been introduced [44, 19]. We focus on Unified Prompt Tuning (UPT) [44] which essentially learns a tiny neural network to jointly optimize prompts across different modalities. UPT learns a set of prompts  $\mathbf{U} = [\mathbf{U}_T,\mathbf{U}_V]\in \mathbb{R}^{d\times n}$  with length  $n$ , where  $\mathbf{U}_T\in \mathbb{R}^{d\times n_T},\mathbf{U}_V\in \mathbb{R}^{d\times n_V}$ .  $\mathbf{U}$  is transformed as follows:
$$
\pmb {U} ^ {\prime} = \mathrm {S A} (\pmb {U}) + \mathrm {L N} (\pmb {U})
$$
$$
\hat {\boldsymbol {U}} = \operatorname {F F N} \left(\ln \left(\boldsymbol {U} ^ {\prime}\right)\right) + \ln \left(\boldsymbol {U} ^ {\prime}\right)
$$
where SA is the self-attention operator, LN is the layer normalization operator, and FFN is a feed forward network. After transformation, we obtain  $\hat{U} = \left[\hat{U}_T,\hat{U}_V\right]\in \mathbb{R}^{d\times n}$ , such that  $\hat{U}_{T}$  is to be used as a text prompt, and  $\hat{U}_{V}$  is to be used as a visual prompt.
Num. classes (|Y|)	Num. seen classes (|S|)	Num. unseen classes (|U|)	Size training data	Avg. labeled data per class	Size test
Flowers102	102	63	39	2040	16	6149
RESICS45	45	27	18	6300	110	25200
FGVC-Aircraft	100	62	38	6667	53	3333
MNIST	10	6	4	60000	4696	10000
EuroSAT	10	6	4	27000	2200	5000
DTD	47	29	18	3760	64	1880
Table 4: For each dataset we report the number of classes, the number of seen and unseen classes in the TRZSL setting, the size of training data (including both labeled and unlabeled data), the average number of labeled examples per class, and the size of the test set which is the same across learning paradigms. We recall that we use the datasets gathered by the recent ELEVATER [23] benchmark for vision-language models.