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REVIEW 4 major objections 6 minor 52 references

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation

T0 review · 4 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Sequence-generated image augmentation lifts fine-grained bird recognition to a new high of 93.4 percent.

desk verdict Candid take: the sequence-augmentation idea and BTL are a legitimate empirical contribution, but the advertised CUB state-of-the-art claim is confounded by extra NABirds pretraining and the evidence is thinner than the abstract suggests. read the letter →

arxiv 2412.06138 v1 pith:JPGJIME3 submitted 2024-12-09 cs.CV

Qiyu Liao , Xin Yuan , Min Xu , Dadong Wang This is my paper · ORCID
classification cs.CV
keywords fine-grainedvisualclassificationgenerativeimageaugmentationlatentdiffusionmodelsequencegenerationbridgingtransferlearningCUB-200-2011few-shotdata
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 argues that fine-grained classifiers improve most when data augmentation generates, for each training image, a short sequence of images that change pose, viewpoint, background, and lighting while keeping the same fine-grained label. To make those synthetic images useful despite the domain gap they introduce, the paper proposes Bridging Transfer Learning (BTL), a two-stage schedule that first trains on a balanced mix of real and generated images and then fine-tunes on real images only. The authors report that this recipe, called Sequence Generative Image Augmentation (SGIA), beats both the no-augmentation baseline and image-based generative augmentation across CUB-200-2011, FGVC-Aircraft, and Stanford Cars, with the largest gains in few-shot settings. On CUB-200-2011 it reaches 93.4% accuracy, surpassing the previous state of the art by 0.5%. The stakes are practical: fine-grained annotation is expensive, so a reliable way to manufacture visual variability from a single labeled image lowers the cost of building such datasets.

What carries the argument

The engine is the Sequence Latent Diffusion Model (SLDM): for an input image $x$, it computes a VQGAN encoding $E_{VQ}(x)$, a global encoding $E_G(E_{VQ}(x))$, and a CLIP semantic encoding $E_{CLIP}(x)$, adds noise to the VQGAN encoding, and denoises under the combined guidance to emit $K$ frames $\tilde{x} = \mathrm{LDM}(\epsilon + E_{VQ}(x), E_{CLIP}(x) + E_G(E_{VQ}(x)))$, with $K=32$. The second load-bearing object is Bridging Transfer Learning (BTL): a model is first fine-tuned on a loader $L_\alpha$ that mixes real and synthetic images with probability $\alpha$, then fine-tuned again on real images only. This two-stage schedule is what lets the classifier absorb the extra variability of the generated sequences while snapping back to the true data distribution, and it is the component the ablation study credits for the largest gains.

What would settle it

Train a classifier on SGIA-generated frames only, then test it on the real test split: if the fine-grained labels were preserved, synthetic-only training should transfer at near-baseline accuracy, whereas a large drop would show the generator is losing the discriminative features. A second check is to have human annotators label a sample of generated frames; if a substantial fraction are assigned the wrong fine-grained class, the augmentation is injecting label noise rather than label-preserving diversity.

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Extended reading notes

Core claim

The central claim is that a sequence latent diffusion model, built on the I2VGen-XL image-to-video front end, can generate augmented frames that vary the object's pose, position, angle, lighting, and surrounding interactions without erasing the fine-grained category, and that these frames improve real classifiers when training is bridged. Concretely, in controlled experiments SGIA improves accuracy over the baseline in 94% of configurations and exceeds the prior real-guidance generative augmentation in 98% of configurations. On the full CUB-200-2011 benchmark, with a ConvNeXtV2-H backbone and additional NABirds pretraining, the paper reports 93.4%, which is 0.5% above the previous state-of-the-art MetaFormer result while using less pretraining data and no extra annotations. The authors frame this as the first generative augmentation technique to outperform training on pure real data for large-scale fine-grained classification.

Load-bearing premise

The generator preserves the fine-grained label of every source image in every generated frame, so a sparrow stays a sparrow, an aircraft model stays that model, and a car make stays that make even as pose and background change.

Editorial extensions

If this is right

  • If SGIA works as claimed, the main bottleneck for fine-grained recognition shifts from collecting rare viewpoints to verifying that generated sequences keep the class label.
  • The reported few-shot gains are larger than full-data gains, so the method is most valuable when annotated examples are scarce.
  • Deformable, color-variable categories such as birds and cars benefit more than rigid categories such as aircraft, suggesting the method is best applied where within-class variation is pose- and environment-driven.
  • Bridging Transfer Learning is a reusable schedule: any generative augmentation that mixes real and synthetic data can adopt the train-on-mixed-then-fine-tune-on-real strategy.
  • Because the generator is a pretrained video model, future improvements in image-to-video synthesis should translate directly into better augmentation without changing the training recipe.

Reading between the lines

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

  • I would expect SGIA to combine with test-time augmentation over the generated sequences, since the sequences already provide multiple views of the same instance; the paper does not test this.
  • The paper does not measure label preservation quantitatively, so a perceptual study or a cross-frame identity-retrieval metric would tell whether the 0.5% gain comes from diversity or from label-preserving realism.
  • The same sequence generator could serve other instance-level tasks such as fine-grained retrieval, re-identification, or pose estimation, where the generated frames supply free multi-view supervision; this is an extension the authors do not discuss.
  • A controlled study varying the generator's video pretraining corpus would map when synthetic augmentation starts to hurt, since the method's ceiling is set by the generator's prior rather than by the classifier.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper proposes SGIA (Sequence Generative Image Augmentation), a data-augmentation method for fine-grained visual classification that uses a sequence latent diffusion model built on I2VGen-XL to generate multiple transformed versions of each training image, together with a two-stage Bridging Transfer Learning (BTL) procedure to reduce the domain gap between real and synthetic data. The method is evaluated on CUB-200-2011, FGVC-Aircraft, and Stanford Cars across multiple backbones, input resolutions, and base augmentation settings, including few-shot regimes. The paper reports consistent accuracy gains over a baseline and over the prior GIA method, and claims a new CUB-200-2011 state of the art of 93.4%, surpassing MetaFormer by 0.5%.

Significance. If the reported results are robust, SGIA would be a useful demonstration that sequence-level generative augmentation can improve fine-grained classification, particularly in few-shot settings, and the controlled-variable comparisons across backbones and image sizes are a useful contribution. The paper itself is empirical; it provides no theoretical guarantees, code, or released model weights, and its central claims rest on small accuracy differences. The most notable strengths are the breadth of the evaluation (four backbones, two augmentation settings, two resolutions, three datasets) and the explicit comparison to the prior GIA method. However, the current empirical support is weakened by test-set hyperparameter selection, a confounded headline result, and the absence of variance estimates, so the significance of the claimed state-of-the-art result is not yet established.

major comments (4)
  1. [Section 4.4, Table 2] The headline claim of a new CUB-200-2011 state of the art (93.4%, surpassing MetaFormer by 0.5%) rests on the last row of Table 2, which uses ConvNextV2-H pretrained on NABirds, while every comparison row, including the direct ConvNextV2-H baseline (92.8%) and GIA (92.6%), uses ImageNet-21k pretraining. Because NABirds is a fine-grained bird dataset closely related to CUB, additional pretraining on NABirds could improve CUB accuracy on its own, and the paper provides no NABirds-pretrained ConvNextV2-H baseline without SGIA. This confound means the 0.5% gain over MetaFormer cannot be attributed to the augmentation method. A matched-pretraining baseline is required before the headline SOTA claim is supported.
  2. [Section 4.2, Fig. 3] The hyperparameters α and M are chosen by maximizing accuracy on the CUB-200-2011 test set (the curves in Fig. 3 are test accuracies), and these same values (α=0.5, M=1/3/4 etc.) are then used to report CUB results in Tables 1 and 2. This is a test-set selection bias that inflates the apparent gain of SGIA on CUB. The values are also transferred to FGVC-Aircraft and Stanford Cars without a separate validation procedure, weakening the cross-dataset generalization claim. The paper should select hyperparameters on a validation split or use a nested evaluation, and should report the sensitivity of the final results to this choice.
  3. [Sections 4.2-4.4, Tables 1-2] Many of the reported improvements are small (about 0.2 to 0.5 percentage points for the main comparisons), and the paper reports no error bars, standard deviations over random seeds, or significance tests. Given that single training runs of deep networks typically vary by well over 0.5 points, the evidence that SGIA outperforms GIA or the baseline in the full-dataset setting is not yet convincing. Please report mean and standard deviation over at least three to five seeds, and state the statistical significance of the key comparisons, especially the Table 2 differences and the Table 1 'Average Improvement' rows.
  4. [Section 3.1 and Section 4.5] The method assumes that each generated sequence preserves the fine-grained label of the source image, but Eq. (1) provides no identity-preservation mechanism, and Section 4.5 explicitly shows negative samples where the major discriminative feature is missing. The paper states that such negative samples 'can impact the representational capability' of trained models, yet it does not quantify their frequency, study their effect on accuracy, or filter them before training. This is load-bearing because if a substantial fraction of augmentations drop the label-defining features, the BTL training could be harmed rather than helped. Please add a quantitative analysis of label-preservation failures and, if possible, a filtering or quality-control step.
minor comments (6)
  1. [Throughout] The notation '2242', '4482', and '5122' appears to mean 224², 448², and 512², but the superscript is missing; please use a consistent and unambiguous notation, e.g., 224×224 or 224².
  2. [Fig. 4 caption] The caption contains a typo: 'Generatied' should be 'Generated'.
  3. [Section 4.1] The text says 'Pytorch' and later 'Pytorch' appears in the implementation description; the correct spelling is 'PyTorch'.
  4. [Section 4.1] The scheduler parameters 't0 = 1 and tmultiply = 2' are not defined; please explain what these parameters refer to (e.g., cosine annealing with warm restarts).
  5. [Table 2] The row 'Mix+ [44])' has an unbalanced parenthesis; also, the name 'NABird' in the last row and the text is inconsistent with the dataset name 'NABirds' used in the references.
  6. [Section 3.2] In Eq. (2), the notation Lα(i) is used both for the loader and for a sampled training sample; clarifying the distinction would improve readability.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: SGIA is an empirical augmentation pipeline whose claims rest on controlled experiments, not on definitions, fitted predictions, or load-bearing self-citations.

full rationale

The paper's central claim is an empirical comparison: SGIA with Bridging Transfer Learning improves FGVC accuracy across many configurations. The methodology is specified by well-defined external components: Eq. (1) generates sequences with a pretrained latent diffusion model, Eq. (2) mixes real and synthetic samples with probability alpha, and Eq. (3) defines a two-stage fine-tuning procedure. None of these equations defines the reported accuracy in terms of the input data in a way that forces the result. No fitted parameter is renamed as a prediction: alpha and M are explicitly tuned and reported as hyperparameters, which is a potential selection-bias issue but not circularity. The paper's self-citations ([10], [15]) are ordinary related-work references and are not load-bearing for the main claim. The strongest headline result, 93.4% on CUB-200-2011, uses NABirds pretraining in the last row of Table 2 while comparison rows use ImageNet-21k or iNaturalist pretraining; this is an evaluation confound rather than a circular derivation. Therefore the derivation chain contains no circular step.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The method is empirical and depends on pretrained components and tuned hyperparameters; no new physical or conceptual entities are introduced.

free parameters (3)
  • alpha (augmenting probability) = 0.5 (selected on CUB test set)
    Controls the fraction of synthetic images in the training loader (Eq. 2); tuned in Fig. 3 and then fixed for all other experiments.
  • M (augmentations per sample) = 3 for the main Table 2 results; varied 1-32 in Fig. 3
    Number of generated sequences per real image; selected based on CUB performance.
  • K (sequence length) = 32
    Number of frames generated per sequence; taken from I2VGen-XL [32], not tuned.
assumptions (3)
  • domain assumption Pretrained I2VGen-XL, CLIP, and VQGAN encoders provide reliable conditioning for FGVC images without adaptation
    Section 3.1 uses these pretrained components as-is; failure would degrade generated samples.
  • domain assumption Generated sequences preserve the fine-grained label of the input image
    Section 4.5 shows negative samples with missing features; label preservation is assumed rather than verified.
  • ad hoc to paper Hyperparameters tuned on CUB transfer to Aircraft and Cars
    Section 4.2 fixes alpha=0.5 on CUB and then applies it to the other datasets.

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Cite this review

Pith. "Pith review of SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation." pith.science (2026). https://pith.science/paper/JPGJIME3

@misc{pith2026241206138,
  author       = {Pith},
  title        = {Pith review of: SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JPGJIME3}},
  note         = {Machine review of arXiv:2412.06138}
}
read the original abstract

In Fine-Grained Visual Classification (FGVC), distinguishing highly similar subcategories remains a formidable challenge, often necessitating datasets with extensive variability. The acquisition and annotation of such FGVC datasets are notably difficult and costly, demanding specialized knowledge to identify subtle distinctions among closely related categories. Our study introduces a novel approach employing the Sequence Latent Diffusion Model (SLDM) for augmenting FGVC datasets, called Sequence Generative Image Augmentation (SGIA). Our method features a unique Bridging Transfer Learning (BTL) process, designed to minimize the domain gap between real and synthetically augmented data. This approach notably surpasses existing methods in generating more realistic image samples, providing a diverse range of pose transformations that extend beyond the traditional rigid transformations and style changes in generative augmentation. We demonstrate the effectiveness of our augmented dataset with substantial improvements in FGVC tasks on various datasets, models, and training strategies, especially in few-shot learning scenarios. Our method outperforms conventional image augmentation techniques in benchmark tests on three FGVC datasets, showcasing superior realism, variability, and representational quality. Our work sets a new benchmark and outperforms the previous state-of-the-art models in classification accuracy by 0.5% for the CUB-200-2011 dataset and advances the application of generative models in FGVC data augmentation.

Figures

Figures reproduced from arXiv: 2412.06138 by the authors.

Figure 1
Figure 1. Illustration of synthetic image quality. The left image is from the CUB-2011-200 dataset. The four images on [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Together, these mechanisms work in tandem to enrich FGVC datasets with the enhanced diversity and robustness [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 2
Figure 2. Two-phase neural network training framework. The process begins with encoding images with video motion [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figures from the paper (3 more)
Figure 3
Figure 3. Figure 3: FGVC accuracies on CUB-200-2011 dataset [ [PITH_FULL_IMAGE:figures/full_fig_p006_3.png]
Figure 4
Figure 4. Figure 4: Generatied samples from GIA and SGIA. 9 [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: Negative samples from SGIA. The "Original" column displays real images from the three FGVC datasets. [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]

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Reference graph

Works this paper leans on

52 extracted references · 28 canonical work pages

  1. [1]

    The caltech-ucsd birds-200- 2011 dataset

    Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie. The caltech-ucsd birds-200- 2011 dataset. 2011

    work page 2011

  2. [2]

    3d object representations for fine-grained categorization

    Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 3d object representations for fine-grained categorization. In Proceedings of the IEEE International Conference on Computer Vision Workshops, pages 554–561, 2013

    work page 2013

  3. [3]

    Bilinear cnn models for fine-grained visual recognition

    Tsung-Yu Lin, Aruni RoyChowdhury, and Subhransu Maji. Bilinear cnn models for fine-grained visual recognition. In Proceedings of the IEEE International Conference on Computer Vision, pages 1449–1457, 2015

    work page 2015

  4. [4]

    Compact bilinear pooling

    Yang Gao, Oscar Beijbom, Ning Zhang, and Trevor Darrell. Compact bilinear pooling. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 317–326, 2016

    work page 2016

  5. [5]

    Learning partial correlation based deep visual representation for image classification

    Saimunur Rahman, Piotr Koniusz, Lei Wang, Luping Zhou, Peyman Moghadam, and Changming Sun. Learning partial correlation based deep visual representation for image classification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6231–6240, 2023

    work page 2023

  6. [6]

    Learning multi-attention convolutional neural network for fine-grained image recognition

    Heliang Zheng, Jianlong Fu, Tao Mei, and Jiebo Luo. Learning multi-attention convolutional neural network for fine-grained image recognition. In Proceedings of the IEEE international conference on computer vision, pages 5209–5217, 2017

    work page 2017

  7. [7]

    Selective sparse sampling for fine-grained image recognition

    Yao Ding, Yanzhao Zhou, Yi Zhu, Qixiang Ye, and Jianbin Jiao. Selective sparse sampling for fine-grained image recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6599–6608, 2019

    work page 2019

  8. [8]

    See better before looking closer: Weakly supervised data augmentation network for fine-grained visual classification, 2019

    Tao Hu and Honggang Qi. See better before looking closer: Weakly supervised data augmentation network for fine-grained visual classification, 2019

    work page 2019

Show all 52 references
  1. [9]

    Counterfactual attention learning for fine-grained visual categorization and re-identification

    Yongming Rao, Guangyi Chen, Jiwen Lu, and Jie Zhou. Counterfactual attention learning for fine-grained visual categorization and re-identification. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1025–1034, 2021

    2021

  2. [10]

    Category attention transfer for efficient fine-grained visual categorization

    Qiyu Liao, Dadong Wang, and Min Xu. Category attention transfer for efficient fine-grained visual categorization. Pattern Recognition Letters, 153:10–15, 2022

    2022

  3. [11]

    Sr-gnn: Spatial relation- aware graph neural network for fine-grained image categorization

    Asish Bera, Zachary Wharton, Yonghuai Liu, Nik Bessis, and Ardhendu Behera. Sr-gnn: Spatial relation- aware graph neural network for fine-grained image categorization. IEEE Transactions on Image Processing, 31:6017–6031, 2022

    2022

  4. [12]

    Image data augmenta- tion for deep learning: A survey

    Suorong Yang, Weikang Xiao, Mengcheng Zhang, Suhan Guo, Jian Zhao, and Furao Shen. Image data augmenta- tion for deep learning: A survey. arXiv preprint arXiv:2204.08610, 2022

    2022 arXiv

  5. [13]

    Denoising diffusion probabilistic models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840–6851, 2020

    2020

  6. [14]

    Is synthetic data from generative models ready for image recognition? In The Eleventh International Conference on Learning Representations, 2023

    Ruifei He, Shuyang Sun, Xin Yu, Chuhui Xue, Wenqing Zhang, Philip Torr, Song Bai, and XIAOJUAN QI. Is synthetic data from generative models ready for image recognition? In The Eleventh International Conference on Learning Representations, 2023

    2023

  7. [15]

    Squeezed bilinear pooling for fine-grained visual categorization

    Qiyu Liao, Dadong Wang, Hamish Holewa, and Min Xu. Squeezed bilinear pooling for fine-grained visual categorization. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pages 0–0, 2019

    2019

  8. [16]

    Multi-attention multi-class constraint for fine-grained image recognition

    Ming Sun, Yuchen Yuan, Feng Zhou, and Errui Ding. Multi-attention multi-class constraint for fine-grained image recognition. In Proceedings of the european conference on computer vision (ECCV), pages 805–821, 2018

    2018

  9. [17]

    Learning attentive pairwise interaction for fine-grained classification

    Peiqin Zhuang, Yali Wang, and Yu Qiao. Learning attentive pairwise interaction for fine-grained classification. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 13130–13137, 2020

    2020

  10. [18]

    Attention is all you need

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017

    2017

  11. [19]

    Feature fusion vision transformer for fine-grained visual categoriza- tion

    Jun Wang, Xiaohan Yu, and Yongsheng Gao. Feature fusion vision transformer for fine-grained visual categoriza- tion. arXiv preprint arXiv:2107.02341, 2021

    2021 arXiv

  12. [20]

    Sim-trans: Structure information modeling transformer for fine- grained visual categorization

    Hongbo Sun, Xiangteng He, and Yuxin Peng. Sim-trans: Structure information modeling transformer for fine- grained visual categorization. In Proceedings of the 30th ACM International Conference on Multimedia, pages 5853–5861, 2022

    2022

  13. [21]

    Transfg: A transformer architecture for fine-grained recognition

    Ju He, Jie-Neng Chen, Shuai Liu, Adam Kortylewski, Cheng Yang, Yutong Bai, and Changhu Wang. Transfg: A transformer architecture for fine-grained recognition. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 852–860, 2022. 11

    2022

  14. [22]

    Metaformer: A unified meta framework for fine-grained recognition

    Qishuai Diao, Yi Jiang, Bin Wen, Jia Sun, and Zehuan Yuan. Metaformer: A unified meta framework for fine-grained recognition. arXiv preprint arXiv:2203.02751, 2022

    2022 arXiv

  15. [23]

    A free lunch from vit: Adaptive attention multi-scale fusion transformer for fine-grained visual recognition

    Yuan Zhang, Jian Cao, Ling Zhang, Xiangcheng Liu, Zhiyi Wang, Feng Ling, and Weiqian Chen. A free lunch from vit: Adaptive attention multi-scale fusion transformer for fine-grained visual recognition. In ICASSP 2022- 2022 IEEE International Conference on Acoustics, Speech and ...

    2022

  16. [24]

    Flownet: Learning optical flow with convolutional networks

    Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Hausser, Caner Hazirbas, Vladimir Golkov, Patrick Van Der Smagt, Daniel Cremers, and Thomas Brox. Flownet: Learning optical flow with convolutional networks. In Proceedings of the IEEE international conference on computer v...

    2015

  17. [25]

    Playing for data: Ground truth from computer games

    Stephan R Richter, Vibhav Vineet, Stefan Roth, and Vladlen Koltun. Playing for data: Ground truth from computer games. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part II 14, pages 102–118. Springer, 2016

    2016

  18. [26]

    Visda: The visual domain adaptation challenge

    Xingchao Peng, Ben Usman, Neela Kaushik, Judy Hoffman, Dequan Wang, and Kate Saenko. Visda: The visual domain adaptation challenge. arXiv preprint arXiv:1710.06924, 2017

    2017 arXiv

  19. [27]

    This dataset does not exist: training models from generated images

    Victor Besnier, Himalaya Jain, Andrei Bursuc, Matthieu Cord, and Patrick Pérez. This dataset does not exist: training models from generated images. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1–5. IEEE, 2020

    2020

  20. [28]

    A style-based generator architecture for generative adversarial networks

    Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4401–4410, 2019

    2019

  21. [29]

    Generative models as a data source for multiview representation learning

    Ali Jahanian, Xavier Puig, Yonglong Tian, and Phillip Isola. Generative models as a data source for multiview representation learning. arXiv preprint arXiv:2106.05258, 2021

    2021 arXiv

  22. [30]

    Videocomposer: Compositional video synthesis with motion controllability

    Xiang Wang, Hangjie Yuan, Shiwei Zhang, Dayou Chen, Jiuniu Wang, Yingya Zhang, Yujun Shen, Deli Zhao, and Jingren Zhou. Videocomposer: Compositional video synthesis with motion controllability. arXiv preprint arXiv:2306.02018, 2023

    2023 arXiv

  23. [31]

    High-resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695, 2022

    2022

  24. [32]

    I2vgen-xl: High-quality image-to-video synthesis via cascaded diffusion models

    Shiwei Zhang, Jiayu Wang, Yingya Zhang, Kang Zhao, Hangjie Yuan, Zhiwu Qin, Xiang Wang, Deli Zhao, and Jingren Zhou. I2vgen-xl: High-quality image-to-video synthesis via cascaded diffusion models. arXiv preprint arXiv:2311.04145, 2023

    2023 arXiv

  25. [33]

    Learning transferable visual models from natural language supervision

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pa...

    2021

  26. [34]

    Taming transformers for high-resolution image synthesis

    Patrick Esser, Robin Rombach, and Bjorn Ommer. Taming transformers for high-resolution image synthesis. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12873–12883, 2021

    2021

  27. [35]

    Imagenet: A large-scale hierarchical image database

    Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. Ieee, 2009

    2009

  28. [36]

    Fine-grained visual classifica- tion of aircraft

    Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi. Fine-grained visual classifica- tion of aircraft. arXiv preprint arXiv:1306.5151, 2013

    2013 arXiv

  29. [37]

    Generative latent implicit conditional optimization when learning from small sample

    Idan Azuri and Daphna Weinshall. Generative latent implicit conditional optimization when learning from small sample. In 2020 25th International Conference on Pattern Recognition (ICPR), pages 8584–8591. IEEE, 2021

    2020

  30. [38]

    Efficientnet: Rethinking model scaling for convolutional neural networks

    Mingxing Tan and Quoc V Le. Efficientnet: Rethinking model scaling for convolutional neural networks. arXiv preprint arXiv:1905.11946, 2019

    1905 arXiv

  31. [39]

    Deep residual learning for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016

    2016

  32. [40]

    Rethinking the inception architecture for computer vision

    Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818–2826, 2016

    2016

  33. [41]

    Api-net: Robust generative classifier via a single discriminator

    Xinshuai Dong, Hong Liu, Rongrong Ji, Liujuan Cao, Qixiang Ye, Jianzhuang Liu, and Qi Tian. Api-net: Robust generative classifier via a single discriminator. In European Conference on Computer Vision, pages 379–394. Springer, 2020. 12

    2020

  34. [42]

    Densely connected convolutional networks

    Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4700–4708, 2017

    2017

  35. [43]

    Elope: Fine-grained visual classification with efficient localization, pooling and embedding

    Harald Hanselmann and Hermann Ney. Elope: Fine-grained visual classification with efficient localization, pooling and embedding. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1247–1256, 2020

    2020

  36. [44]

    Attribute mix: semantic data augmentation for fine grained recognition

    Hao Li, Xiaopeng Zhang, Qi Tian, and Hongkai Xiong. Attribute mix: semantic data augmentation for fine grained recognition. In 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP), pages 243–246. IEEE, 2020

    2020

  37. [45]

    Three-branch and mutil-scale learning for fine-grained image recognition (tbmsl-net)

    Fan Zhang, Guisheng Zhai, Meng Li, and Yizhao Liu. Three-branch and mutil-scale learning for fine-grained image recognition (tbmsl-net). arXiv preprint arXiv:2003.09150, 2020

    2003 arXiv

  38. [46]

    An image is worth 16x16 words: Transformers for image recognition at scale

    Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv...

    2010 arXiv

  39. [47]

    Context-aware attentional pooling (cap) for fine-grained visual classification

    Ardhendu Behera, Zachary Wharton, Pradeep RPG Hewage, and Asish Bera. Context-aware attentional pooling (cap) for fine-grained visual classification. In Proceedings of the AAAI conference on artificial intelligence , volume 35, pages 929–937, 2021

    2021

  40. [48]

    Xception: Deep learning with depthwise separable convolutions

    François Chollet. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1251–1258, 2017

    2017

  41. [49]

    10,000 species recognition challenge with inaturalist data

    Oisin Mac Aodha Grant Van Horn. 10,000 species recognition challenge with inaturalist data. fgvc8, 2021

    2021

  42. [50]

    Convnext v2: Co-designing and scaling convnets with masked autoencoders

    Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, and Saining Xie. Convnext v2: Co-designing and scaling convnets with masked autoencoders. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16133–16142, 2023

    2023

  43. [51]

    Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection

    Grant Van Horn, Steve Branson, Ryan Farrell, Scott Haber, Jessie Barry, Panos Ipeirotis, Pietro Perona, and Serge Belongie. Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection. In Proceedings of the...

    2015

  44. [52]

    Random erasing data augmentation

    Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, and Yi Yang. Random erasing data augmentation. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 13001–13008, 2020. 13

    2020

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.