Image Generation with Supervised Selection Based on Multimodal Features for Semantic Communications

Chengyang Liang; Dong Li

REVIEW 4 major objections 4 minor 3 cited by

Image Generation with Supervised Selection Based on Multimodal Features for Semantic Communications

T0 review · 4 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read This paper claims that transmitting both image features and CLIP text prompts lets a receiver generate thousands of candidate images and select the best one, keeping reconstruction quality high even under noisy channels.

desk verdict Promising selection-based idea, but the headline PSNR numbers are likely retrieval artifacts from training-set overlap. read the letter →

arxiv 2411.17428 v3 pith:EDCDYGX3 submitted 2024-11-26 eess.IV

Chengyang Liang , Dong Li This is my paper · ORCID

classification eess.IV

keywords semanticcommunicationmultimodalsemanticsdiffusionmodelimagereconstructionCLIPselectionmechanismwirelesstransmissiongenerative

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper proposes a semantic communication system in which the receiver does not reconstruct an image from noisy features directly. Instead, the transmitter sends compact multimodal features, a CNN image descriptor plus CLIP text prompts, and the receiver's diffusion model generates a large set of candidate images from which the closest match is selected. The central claim is that this generation-and-selection decoding keeps image fidelity high at low signal-to-noise ratios, with the sequential variant reporting 35.62 dB PSNR at 10 dB SNR on in-distribution test images. A sympathetic reader would care because it recasts the hard decoding problem as a retrieval problem over clean generated images, which is more tolerant of channel noise.

What carries the argument

The central object is the MultiSem multimodal semantic representation: a ResNet-50 image feature vector and CLIP text prompts, either concatenated or fused through a multi-head cross-attention network with residual connections and layer normalization. The receiver uses a class-conditional diffusion U-Net to generate a candidate image set, and a selection module scores each candidate by a combined cosine and Euclidean distance against the received features. Text prompts act as a coarse filter that narrows the candidate set, and image features then refine the choice, which is what makes sequential MMSemCom robust at low signal-to-noise ratios.

What would settle it

Transmit a test image whose class or visual style was excluded from diffusion training and measure PSNR at 10 dB SNR; if the best candidate among the 5,000 generated images is far from the target, the selection mechanism cannot recover the reported 35 dB and performance falls to the quality of the closest class-conditional sample.

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Extended reading notes

Core claim

Adding text-level semantics to image-level semantics for supervising generation improves both fidelity and robustness in wireless image transmission. The system extracts image features with a ResNet-50 backbone and derives text prompts with CLIP, combines them into a multimodal semantic representation called MultiSem, and transmits that representation over AWGN or Rayleigh channels. At the receiver, a class-conditional diffusion U-Net trained on the same dataset samples 5,000 candidate images, and a selection module ranks them by averaged cosine and Euclidean distances to the received multimodal features. Sequential selection, which filters first by text then by image features, is the most noise-robust variant and reaches 35.62 dB PSNR at 10 dB SNR, while simultaneous selection using only 256 bytes of fused features reaches 30.75 dB. The paper argues that because noise affects which candidate is chosen rather than the pixels of the output, the system avoids the performance cliff that end-to-end decoders show at low SNR.

Load-bearing premise

The reported high PSNR depends on the receiver's diffusion model having been trained on the same dataset from which the test images are drawn, so a near-identical copy of the target image is likely to exist somewhere among the 5,000 generated candidates.

Editorial extensions

If this is right

Channel noise acts on the selection query rather than on the output pixels, so the decoded image is always a clean sample from the diffusion model's learned distribution.
Sequential MMSemCom sustains above 25 dB PSNR even at 0 dB SNR and reaches 35.62 dB at 10 dB SNR on in-distribution data, avoiding the sharp degradation seen in end-to-end baselines.
Simultaneous MMSemCom transmits only 256 bytes of fused features and still achieves 30.75 dB PSNR at 10 dB SNR, giving the highest PSNR-per-kilobyte efficiency among the compared modes.
Increasing the number of CLIP prompts improves selection accuracy up to about three to five prompts, after which communication overhead grows faster than reconstruction quality.
Walsh-code orthogonal multiplexing extends the generation-and-selection framework to multiple users, with both users maintaining high PSNR and MS-SSIM once the SNR is above roughly 10 dB.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the candidate set is generated offline and fixed, the receiver-side selection is effectively a nearest-neighbor search in a shared embedding space; channel noise then perturbs only the query features, so the scheme is a form of robust retrieval rather than generation.
The best achievable PSNR is capped by the best candidate among the generated images, so increasing the candidate count should raise that ceiling at the price of linearly more sampling; a PSNR-versus-N curve would quantify this trade-off directly.
The simultaneous mode's 256-byte overhead invites a direct comparison with conventional codecs transmitting the same byte budget, which would isolate whether the gain comes from the fused features or from the diffusion prior.
The authors' stated future move to text-to-image synthesis would remove the dependence on the training dataset, but it would also change the claim from pixel-level fidelity of a known image to semantic fidelity of a described scene.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

At its core, this paper tests a reasonable idea: use both image and CLIP text features to pick the best candidate from a diffusion-generated set, instead of decoding directly from noisy semantics. That's a clean extension of the authors' earlier selection-based system, and they add a fused attention variant and a Walsh-code multiuser scheme. The complexity and overhead analysis is a nice touch, and Table II (prompt count ablation) shows they were thinking about design trade-offs.

The problem is the load-bearing experiment. The receiver's diffusion model is trained on the complete CIFAR-100 and STL-10 training sets (Section VI-A), and the test images are randomly drawn from those same datasets (Section VI-C). With 50,000 epochs and p=5,000 candidates per image, the candidate set can contain a near-duplicate of the target; the selection mechanism then retrieves it, not reconstructs it. That reading is supported by the paper's own sentence in the MS-SSIM analysis: 'The images produced through diffusion training closely resemble the original images.' The 35.62 dB PSNR and 0.996 MS-SSIM are exactly what you'd expect from near-duplicate lookup. This makes the headline comparison against DeepJSCC, WITT, and LaMoSC uninformative. The OOD tests on FGVC-Aircraft and Stanford-Cars were the right idea, but Fig. 11 is garbled and there is no table, so they don't rescue the paper.

Smaller issues: the simultaneous method's cliff at low SNR is explained as noise sensitivity, but it could just be that the fused feature is less discriminative for retrieval; the paper doesn't separate those. The inference time in Table III says 35ms, which cannot include sampling 5,000 diffusion candidates; that number needs a footnote or clarification. And they never compare against a simple retrieval baseline (e.g., transmitting enough feature bits to look up the image from a shared database), which would sharpen what the generative model actually contributes. It's also not clear how the attention fusion module is trained—jointly with the selection or separately—and that matters for reproducibility.

Net: the idea deserves a test, but this is a pilot study, not a demonstration. To be convincing they'd need a disjoint train/test split, a retrieval baseline, quantitative OOD results, a description of the fusion training, and a clear statement of the true inference cost. I would not accept these numbers as they stand, but I'd send it to a serious referee because the architecture is coherent, the problem is real, and the fix is expensive but well-defined. It could become a solid contribution after major revision.

Referee Report

4 major / 4 minor

Summary. The paper proposes MMSemCom, a semantic communication framework that transmits multimodal semantic features (ResNet-50 image features and CLIP text prompts) and reconstructs the image at the receiver by generating a large candidate set with a class-conditional diffusion model and then selecting the best candidate using sequential or simultaneous selection mechanisms. The authors also extend the scheme to a multiuser scenario using Walsh codes and provide communication-overhead and computational-complexity analyses. The headline empirical claims are high reconstruction fidelity (e.g., 35.62 dB PSNR and 0.996 MS-SSIM for sequential MMSemCom at 10 dB SNR in Table I) and robustness at low SNR.

Significance. The generate-then-select paradigm is, in principle, an interesting alternative to direct neural decoding in semantic communication, and the manuscript contains a fairly complete architecture description with explicit overhead and complexity formulas. However, the empirical validation is undermined by a train/test overlap: the receiver's diffusion model is trained on the complete CIFAR-100 and STL-10 sets, while evaluation images are drawn from those same sets. With 50,000 training epochs and p=5,000 sampled candidates, the reported near-perfect PSNR/MS-SSIM figures are plausibly explained by retrieval of memorized near-duplicates rather than by generative reconstruction. A second architectural gap is that the class condition required by the diffusion model is not specified as being transmitted or recovered in the simultaneous mode. The OOD generalization claim is supported only by a garbled figure with no quantitative table. The paper does not provide code or model checkpoints, so the experiments are not independently reproducible.

major comments (4)

[VI-A, VI-C, Table I] The evaluation protocol allows the diffusion model to have memorized the test images. Section VI-A states that the receiver's diffusion model is trained on the complete CIFAR-100 and STL-10 image sets, and Section VI-C states that test images are randomly selected from those same datasets; no held-out split is described. After 50,000 training epochs on datasets of 60,000 and 13,000 images, and with p=5,000 diffusion samples per transmitted image at inference, the candidate set can contain the original image or a near-identical copy. The selection mechanism in Eqs. (14)-(19) then retrieves that near-duplicate using the transmitted image and text features, which explains the near-perfect PSNR (35.62 dB) and MS-SSIM (0.996) in Table I. This makes the headline result a retrieval artifact rather than a generative reconstruction result. The authors must evaluate on a properly disjoint train/test split and report the overlap rate between the generated candidate set and the target image, or otherwise demonstrate that the reported gains persist for images not in the training set.
[III-B1, III-B2, Section VI] The receiver's diffusion model is class-conditional (Eq. (9), Algorithm 1), but the manuscript never specifies how the receiver obtains the class condition c. In the sequential method, the transmitted text prompts may indirectly carry class information, but in the simultaneous method the transmitter sends only the fused feature vector FFused (Section VI-B). No class label or equivalent condition is included in the transmitted overhead (Eq. (25)-(26)), and no mechanism is described for recovering c from the received fused feature. Without a specification of how c is known at the receiver, the conditional generation step is not well defined, and the claimed end-to-end operation cannot be reproduced. Please clarify how c is transmitted or inferred, and update the overhead analysis accordingly.
[VI-D4, Fig. 11] The out-of-distribution generalization claim is not quantitatively supported. Section VI-D4 asserts that SeqMMSemCom and SimMMSemCom exhibit only minor degradation on FGVC-Aircraft and Stanford-Cars and maintain a significant advantage over BPG and LaMoSC, but Fig. 11 is rendered as unreadable placeholder text and no numerical PSNR/MS-SSIM results or comparison table are provided. Without numbers, the reader cannot verify the claimed OOD robustness. Provide a table with mean PSNR, MS-SSIM, and baselines on these datasets.
[VI-C, Figs. 7-10] The comparison with BPG and WebP is not a fair channel comparison. BPG and WebP are source codecs, yet they are plotted as functions of SNR and transmission distance in Figs. 7-10, without specifying how compressed bits are mapped to channel symbols, protected, and transmitted over the AWGN or Rayleigh channel. To support the claim of outperforming 'existing communication systems,' all schemes should be evaluated under the same channel-use/bit-rate constraints and the same channel model. As presented, the gains over BPG and WebP are not well defined.

minor comments (4)

[Eqs. (14)-(19)] The summation indices in the distance formulas are inconsistent with the feature dimensions: in Eq. (14) the sum runs over p (the number of generated images) while the text feature FText(x) has dimension ntext × dtext; in Eq. (17) the sum runs over q, but FImage(x) is a single vector of dimension dimage; in Eq. (19) the sum again runs over p for a fused feature of dimension dfusion. Please correct these expressions or clarify the intended averaging.
[VI-A, VI-C] The training budget is described as '50,000 epochs' in Section VI-A and '50,000 iterations' in the bullet points of Section VI-C; please use consistent terminology, since the number of epochs is highly relevant to the memorization concern.
[Table III] The table header says 'DeePJSCC' while the text and references use 'DeepJSCC'; please fix the capitalization.
[VI-D1] In the path-loss paragraph, 'in Eq. 2' appears to refer to the channel model in Section II, but that equation is not numbered; please add the equation number or fix the cross-reference.

Circularity Check

1 steps flagged · score 6.0 of 10

Table I's 35.62 dB PSNR is largely a retrieval artifact: the receiver's diffusion model is trained on the complete CIFAR-100/STL-10 sets and evaluated on the same sets, so the selection step can recover a memorized near-duplicate rather than reconstruct from transmitted semantics.

fitted input called prediction [Section VI-A (Dataset), Section VI-B (Demonstration), Section VI-C (Performance Comparison); Table I]
"Our training datasets include the complete CIFAR-100 and STL-10 image sets... In the receiver, we train the DM for 50,000 epochs utilizing the same dataset, sampling p = 5000 images at the inference time step T = 40... we randomly select five images from the CIFAR-100 and STL-10 datasets and evaluate the average PSNR and MS-SSIM of these images under varying SNRs."

The receiver's diffusion model is trained on the complete CIFAR-100 and STL-10 sets, and the test images are drawn from those same datasets. After 50,000 epochs and with p=5000 sampled candidates per test image, a near-copy of the target image can be present in the candidate set; the selector in Eqs. (14)-(19) then retrieves it using the transmitted features. The reported PSNR (35.62 dB at 10 dB in Table I) therefore measures retrieval from the diffusion model's training data, not reconstruction from noisy semantics. The paper's own statement that 'the images produced through diffusion training closely resemble the original images' confirms that candidate images are near-duplicates of training images. OOD tests are only qualitative (Fig. 11) and do not rescue the in-distribution numbers.

full rationale

There is no equation-level circularity in the proposed encoder/decoder architecture: the fusion and selection mechanisms are defined independently of the evaluation images, and the system is benchmarked against external codecs (BPG, WebP, DeepJSCC, LaMoSC, WITT). However, the headline empirical claim reduces, by construction, to a retrieval benchmark. The diffusion model is trained on the complete CIFAR-100 and STL-10 sets, and the test images are randomly selected from those same sets; with 50,000 training epochs and 5,000 sampled candidates per inference, the candidate pool can contain memorized near-duplicates of the target. The selection step then identifies the closest such duplicate, so the reported PSNR/MS-SSIM primarily reflect the diffusion model's training-set coverage rather than the semantic communication pipeline's ability to reconstruct from noisy transmitted features. This is a fitted-input-called-prediction pattern: the generative model is fit to the test distribution, and the 'prediction' is the retrieved training image. The central architectural idea (multimodal features improve selection) retains independent content, and the OOD experiments, though qualitative, attempt to address generalization, so the circularity is partial rather than total.

Assumptions & free parameters 7 free parameters · 5 assumptions · 1 invented entities

The central claim depends on standard pretrained models (CLIP, ResNet-50), a trained class-conditional diffusion model, and hand-chosen system parameters (p, q, prompt set, feature dimensions). The most consequential free choice is the per-image prompt set, and the most consequential assumption is that a good candidate exists in the generated set, which is helped by training and testing on the same images.

free parameters (7)

p (number of generated candidate images) = 5000
Hand-chosen in Section VI-B; larger p improves the chance that a good candidate exists but increases receiver compute.
q (text-filtered candidate subset size) = 100
Hand-chosen in Section VI-B for the sequential selection stage.
ntext (number of CLIP prompts) = 3
Chosen as a trade-off between overhead and performance; Table II justifies this choice by scanning 1, 3, 5, 7 prompts.
dimage (image feature dimension) = 32
Hand-chosen in Section VI-B; affects overhead and discriminability.
dfusion (fused feature dimension) = 64
Hand-chosen in Section VI-B for the simultaneous transmission mode.
T (diffusion inference steps) = 40
Hand-chosen in Section VI-B; training uses 1,000 timesteps.
test-specific CLIP prompt set = e.g., bird, yellow, on a tree
The prompt list is selected per test image or class and is not generated by an automated captioning pipeline; this is privileged information for the selection step.

assumptions (5)

standard math Walsh code matrix Wd satisfies Wd Wd^T = d I, giving orthogonal user separation.
Invoked in Section IV, Eqs. (22)-(24), for multiuser decoding.
domain assumption CLIP embeddings map images and text into a shared space where cosine similarity reflects semantic relevance.
Assumed in Section III-A2 for prompt selection and in the receiver for feature comparison.
domain assumption Pre-trained ResNet-50 ImageNet features are effective semantic representations for the transmitted images.
Assumed in Section III-A1; the final classification layer is removed and two fully connected layers are added.
domain assumption The class-conditional diffusion model can generate images that include a candidate close to the target image.
Core to the decoder in Section III-B1; the PSNR ceiling is set by the best candidate in the generated set.
ad hoc to paper Evaluation images are drawn from the same distribution used to train the diffusion model, which permits memorization.
Section VI-A trains on complete CIFAR-100 and STL-10; Section VI-C evaluates on random images from those same datasets. This is a design choice that can inflate selection performance.

invented entities (1)

MultiSem multimodal semantic feature
purpose: A fused representation of image features and CLIP text prompts used as the transmitted signal and selection reference.
Introduced in Sections II and III-A3; its benefit is demonstrated only on the paper's own selection task, and it has no falsifiable handle outside the system.

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Cite this review

Pith. "Pith review of Image Generation with Supervised Selection Based on Multimodal Features for Semantic Communications." pith.science (2026). https://pith.science/paper/EDCDYGX3

@misc{pith2026241117428,
  author       = {Pith},
  title        = {Pith review of: Image Generation with Supervised Selection Based on Multimodal Features for Semantic Communications},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EDCDYGX3}},
  note         = {Machine review of arXiv:2411.17428}
}

read the original abstract

Semantic communication (SemCom) has emerged as a promising technique for the next-generation communication systems, in which the generation at the receiver side is allowed with semantic features' recovery. However, the majority of existing research predominantly utilizes a singular type of semantic information, such as text, images, or speech, to supervise and choose the generated source signals, which may not sufficiently encapsulate the comprehensive and accurate semantic information, and thus creating a performance bottleneck. In order to bridge this gap, in this paper, we propose and investigate a SemCom framework using multimodal information to supervise the generated image. To be specific, in this framework, we first extract semantic features at both the image and text levels utilizing the Convolutional Neural Network (CNN) architecture and the Contrastive Language-Image Pre-Training (CLIP) model before transmission. Then, we employ a generative diffusion model at the receiver to generate multiple images. In order to ensure the accurate extraction and facilitate high-fidelity image reconstruction, we select the "best" image with the minimum reconstruction errors by taking both the aided image and text semantic features into account. We further extend multimodal semantic communication (MMSemCom) system to the multiuser scenario for orthogonal transmission. Experimental results demonstrate that the proposed framework can not only achieve the enhanced fidelity and robustness in image transmission compared with existing communication systems but also sustain a high performance in the low signal-to-noise ratio (SNR) conditions.

Figures

Figures reproduced from arXiv: 2411.17428 by the authors.

**Figure 2.** Illustration of the hierarchical sequential transmission method. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Illustration of the simultaneous transmission of fused semantic information. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Figures from the paper (9 more)

**Figure 4.** Figure 4: Demonstration of the diffusion network architecture designed [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

**Figure 5.** Figure 5: Weighted heat maps of text features with the proposed [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]

**Figure 6.** Figure 6: Demonstration of results for two multimodal-supervised [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]

**Figure 7.** Figure 7: Comparison of the PSNR performance in different datasets with AWGN and Rayleigh fading. [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]

**Figure 8.** Figure 8: Comparison of the PSNR performance in different datasets for AWGN and Rayleigh fading with path loss. [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]

**Figure 9.** Figure 9: Comparison of the MS-SSIM performance in different datasets with AWGN and Rayleigh fading. [PITH_FULL_IMAGE:figures/full_fig_p012_9.png]

**Figure 10.** Figure 10: Comparison of the MS-SSIM performance in different datasets for AWGN and Rayleigh fading with path loss. [PITH_FULL_IMAGE:figures/full_fig_p012_10.png]

**Figure 11.** Figure 11: The generalization performance of the trained model in the [PITH_FULL_IMAGE:figures/full_fig_p014_11.png]

**Figure 12.** Figure 12: PSNR and MS-SSIM performance of multiuser scenarios on [PITH_FULL_IMAGE:figures/full_fig_p015_12.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

A Survey on Semantic Communication for Vision: Categories, Frameworks, Enabling Techniques, and Applications
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A survey that classifies visual semantic communication into preservation, expansion, and refinement categories and reviews their machine-learning components and applications.
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cs.LG 2025-08 unverdicted novelty 4.0 of 10

Guiding a pretrained topology-diffusion generator with human-preference reward classifiers is claimed to suppress floating-material and boundary-violation failure modes without retraining the generator.
Semantic Communications for Digital Signals via Carrier Images
eess.SP 2024-12 reject novelty 4.0 of 10

A method for joint transmission of images and digital signals by mapping binary data to mask locations in a Masked Autoencoder, with a sparse index encoding to reduce overhead.

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Enhancing multimodal understanding with clip-based image-to-text transformation,

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2023
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Conditional text image generation with diffusion models,

Y . Zhu, Z. Li, T. Wang, M. He, and C. Yao, “Conditional text image generation with diffusion models,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , Jun. 2023, pp. 14 235–14 245

2023
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Deep residual learning for image recognition,

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2016
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Clip-kd: An empirical study of clip model distillation,

C. Yang, Z. An, L. Huang, J. Bi, X. Yu, H. Yang, B. Diao, and Y . Xu, “Clip-kd: An empirical study of clip model distillation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , Jun. 2024, pp. 15 952–15 962

2024
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U-net: Convolutional networks for biomedical image segmentation,

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Med. Image Comput. Comput. Assist. Interv. (MICCAI) , Nov. 2015, pp. 234–241

2015
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Unipc: A unified predictor- corrector framework for fast sampling of diffusion models,

W. Zhao, L. Bai, Y . Rao, J. Zhou, and J. Lu, “Unipc: A unified predictor- corrector framework for fast sampling of diffusion models,” inProc. Adv. Neural Inf. Process. Syst. (NeurIPS) , vol. 36, Dec. 2023

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Deep joint source- channel coding for wireless image transmission,

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Lamosc: Large language model-driven semantic communication system for visual trans- mission,

Y . Zhao, Y . Yue, S. Hou, B. Cheng, and Y . Huang, “Lamosc: Large language model-driven semantic communication system for visual trans- mission,” IEEE Trans. Cognit. Commun. Networking , vol. 10, no. 6, pp. 2005–2018, Dec. 2024

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Witt: A wireless image transmission transformer for semantic communications,

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2023

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

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A lite distributed semantic communication system for internet of things,

H. Xie and Z. Qin, “A lite distributed semantic communication system for internet of things,” IEEE J. Sel. Areas Commun. , vol. 39, no. 1, pp. 142–153, Jan. 2021

2021

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Semantic communications: Overview, open issues, and future research directions,

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Rethinking modern communication from semantic coding to semantic communication,

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Semantic communications for digital signals via carrier images,

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Toward semantic communications: Deep learning-based image semantic coding,

D. Huang, F. Gao, X. Tao, Q. Du, and J. Lu, “Toward semantic communications: Deep learning-based image semantic coding,” IEEE J. Sel. Areas Commun. , vol. 41, no. 1, pp. 55–71, Jan. 2023

2023

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Deep learning enabled semantic communication systems,

H. Xie, Z. Qin, G. Y . Li, and B.-H. Juang, “Deep learning enabled semantic communication systems,” IEEE Trans. Signal Process., vol. 69, pp. 2663–2675, Apr. 2021

2021