SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation | Paper Citation Record

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This paper cites The caltech-ucsd birds-200- 2011 dataset.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation The caltech-ucsd birds-200- 2011 dataset

Reference 1

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Observation a5c29d7d-04a8-402d-90a6-42521e1cf5b2 · outbound

This paper cites 3d object representations for fine-grained categorization.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation 3d object representations for fine-grained categorization

Reference 2

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This paper cites Bilinear cnn models for fine-grained visual recognition.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Bilinear cnn models for fine-grained visual recognition

Reference 3

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This paper cites Compact bilinear pooling.

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

Reference 4

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Observation 91b9d425-d16a-47d3-b3a6-f92dc5398c06 · outbound

This paper cites Learning partial correlation based deep visual representation for image classification.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Learning partial correlation based deep visual representation for image classification

Reference 5

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Observation 88f9e222-5613-412e-87c3-8547d8bb48a9 · outbound

This paper cites Learning multi-attention convolutional neural network for fine-grained image recognition.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Learning multi-attention convolutional neural network for fine-grained image recognition

Reference 6

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This paper cites Selective sparse sampling for fine-grained image recognition.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Selective sparse sampling for fine-grained image recognition

Reference 7

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Observation da2535c6-a20b-4460-be7c-11a976929f3e · outbound

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

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation See better before looking closer: Weakly supervised data augmentation network for fine-grained visual classification, 2019

Reference 8

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Observation edcabf0b-9b67-4bb3-9002-98767251765d · outbound

This paper cites Counterfactual attention learning for fine-grained visual categorization and re-identification.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Counterfactual attention learning for fine-grained visual categorization and re-identification

Reference 9

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This paper cites Category attention transfer for efficient fine-grained visual categorization.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Category attention transfer for efficient fine-grained visual categorization

Reference 10

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This paper cites Sr-gnn: Spatial relation- aware graph neural network for fine-grained image categorization.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Sr-gnn: Spatial relation- aware graph neural network for fine-grained image categorization

Reference 11

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Observation 67d2fa11-9846-43e4-aad7-b2b8bb87702e · outbound

This paper cites Image Data Augmentation for Deep Learning: A Survey.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Image Data Augmentation for Deep Learning: A Survey

Reference 12

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This paper cites Denoising diffusion probabilistic models.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Denoising diffusion probabilistic models

Reference 13

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

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Is synthetic data from generative models ready for image recognition? In The Eleventh International Conference on Learning Representations, 2023

Reference 14

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This paper cites Squeezed bilinear pooling for fine-grained visual categorization.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Squeezed bilinear pooling for fine-grained visual categorization

Reference 15

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This paper cites Multi-attention multi-class constraint for fine-grained image recognition.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Multi-attention multi-class constraint for fine-grained image recognition

Reference 16

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Observation e24778e6-2e90-4407-ba28-69f984c98ddb · outbound

This paper cites Learning attentive pairwise interaction for fine-grained classification.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Learning attentive pairwise interaction for fine-grained classification

Reference 17

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This paper cites Attention is all you need.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Attention is all you need

Reference 18

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Observation 531ec3ed-45dd-4759-92ea-bd6d689bb512 · outbound

This paper cites Feature Fusion Vision Transformer for Fine-Grained Visual Categorization.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Feature Fusion Vision Transformer for Fine-Grained Visual Categorization

Reference 19

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Observation a6fcf440-4715-4f63-a458-123b43f5a233 · outbound

This paper cites Sim-trans: Structure information modeling transformer for fine- grained visual categorization.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Sim-trans: Structure information modeling transformer for fine- grained visual categorization

Reference 20

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Observation b2d8b845-c40a-40f1-8c61-9661fcaf67a5 · outbound

This paper cites Transfg: A transformer architecture for fine-grained recognition.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Transfg: A transformer architecture for fine-grained recognition

Reference 21

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Observation 4aee11a0-378e-427d-8c87-2e7778edba08 · outbound

This paper cites MetaFormer: A Unified Meta Framework for Fine-Grained Recognition.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation MetaFormer: A Unified Meta Framework for Fine-Grained Recognition

Reference 22

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

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation A free lunch from vit: Adaptive attention multi-scale fusion transformer for fine-grained visual recognition

Reference 23

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Observation 4b00e296-62b2-4078-b15a-b4be6c48759a · outbound

This paper cites Flownet: Learning optical flow with convolutional networks.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Flownet: Learning optical flow with convolutional networks

Reference 24

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Observation 11fd473e-74ba-4d3c-afcb-791b5dc48a5b · outbound

This paper cites Playing for data: Ground truth from computer games.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Playing for data: Ground truth from computer games

Reference 25

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Observation 2aa22d90-cf8c-43cd-a361-15d201a7cd29 · outbound

This paper cites VisDA: The Visual Domain Adaptation Challenge.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation VisDA: The Visual Domain Adaptation Challenge

Reference 26

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This paper cites This dataset does not exist: training models from generated images.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation This dataset does not exist: training models from generated images

Reference 27

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Observation f6e9b55e-5f40-40a5-a242-eb27bd10df57 · outbound

This paper cites A style-based generator architecture for generative adversarial networks.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation A style-based generator architecture for generative adversarial networks

Reference 28

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This paper cites Generative Models as a Data Source for Multiview Representation Learning.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Generative Models as a Data Source for Multiview Representation Learning

Reference 29

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This paper cites VideoComposer: Compositional Video Synthesis with Motion Controllability.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation VideoComposer: Compositional Video Synthesis with Motion Controllability

Reference 30

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This paper cites High-resolution image synthesis with latent diffusion models.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation High-resolution image synthesis with latent diffusion models

Reference 31

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This paper cites I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models

Reference 32

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Observation 447b452d-aeb6-4323-a4e0-66417bd82e5f · outbound

This paper cites Learning transferable visual models from natural language supervision.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Learning transferable visual models from natural language supervision

Reference 33

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This paper cites Taming transformers for high-resolution image synthesis.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Taming transformers for high-resolution image synthesis

Reference 34

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This paper cites Imagenet: A large-scale hierarchical image database.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Imagenet: A large-scale hierarchical image database

Reference 35

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This paper cites Fine-Grained Visual Classification of Aircraft.

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

Reference 36

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Observation dcb50cce-0eed-4d25-8e9a-558386a6c175 · outbound

This paper cites Generative latent implicit conditional optimization when learning from small sample.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Generative latent implicit conditional optimization when learning from small sample

Reference 37

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Observation b16201ad-4bb4-42cb-8395-659ca3a1aac7 · outbound

This paper cites EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Reference 38

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This paper cites Deep residual learning for image recognition.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Deep residual learning for image recognition

Reference 39

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This paper cites Rethinking the inception architecture for computer vision.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Rethinking the inception architecture for computer vision

Reference 40

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This paper cites Api-net: Robust generative classifier via a single discriminator.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Api-net: Robust generative classifier via a single discriminator

Reference 41

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This paper cites Densely connected convolutional networks.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Densely connected convolutional networks

Reference 42

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Observation f9f60e75-dca0-40be-8cbb-f4366834eed3 · outbound

This paper cites Elope: Fine-grained visual classification with efficient localization, pooling and embedding.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Elope: Fine-grained visual classification with efficient localization, pooling and embedding

Reference 43

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This paper cites Attribute mix: semantic data augmentation for fine grained recognition.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Attribute mix: semantic data augmentation for fine grained recognition

Reference 44

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Observation ba024a9b-cbe2-4bbc-ba67-3bd2a6aa13e4 · outbound

This paper cites Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization

Reference 45

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Observation 2224ac25-308d-45d1-9ac1-9b242a3fd9ba · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 46

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Observation 89fbdc76-5e6b-4c3c-bd0b-114036e24d17 · outbound

This paper cites Context-aware attentional pooling (cap) for fine-grained visual classification.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Context-aware attentional pooling (cap) for fine-grained visual classification

Reference 47

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Observation ddd1c9ae-0dcc-4d9b-9a1b-275672dbdbe3 · outbound

This paper cites Xception: Deep learning with depthwise separable convolutions.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Xception: Deep learning with depthwise separable convolutions

Reference 48

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This paper cites 10,000 species recognition challenge with inaturalist data.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation 10,000 species recognition challenge with inaturalist data

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:01:22.754799Z

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This paper cites Convnext v2: Co-designing and scaling convnets with masked autoencoders.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Convnext v2: Co-designing and scaling convnets with masked autoencoders

Reference 50

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

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection

Reference 51

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Observation bb7a3b3e-07bd-49e3-a428-d7b8cc0a3ab0 · outbound

This paper cites Random erasing data augmentation.

SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation Random erasing data augmentation

Reference 52

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