Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval | Paper Citation Record

Observation 4b5ebea1-ef3d-46e1-bbcc-a6a377e97285 · outbound

This paper cites Fine-grained image analysis with deep learning: A sur- vey,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Fine-grained image analysis with deep learning: A sur- vey,

Reference 1

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Observation 02a20c97-c456-48f5-9237-adb29cdc1955 · outbound

This paper cites Deep collaborative embedding for social image understanding,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Deep collaborative embedding for social image understanding,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:03:18.889116Z

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Observation 1fe470c4-f550-4cd5-a152-781f86c5fb3f · outbound

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

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval 3d object representations for fine-grained categorization,

Reference 3

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

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval The caltech-ucsd birds-200-2011 dataset,

Reference 4

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This paper cites Dvf: Advancing robust and accurate fine-grained image retrieval with retrieval guidelines,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Dvf: Advancing robust and accurate fine-grained image retrieval with retrieval guidelines,

Reference 5

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This paper cites Hypergraph-induced semantic tuplet loss for deep metric learning,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Hypergraph-induced semantic tuplet loss for deep metric learning,

Reference 6

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This paper cites No fuss distance metric learning using proxies,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval No fuss distance metric learning using proxies,

Reference 7

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This paper cites One loss for all: Deep hashing with a single cosine similarity based learning objective,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval One loss for all: Deep hashing with a single cosine similarity based learning objective,

Reference 8

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Observation 2ca8b32f-5ce8-4a14-b158-cd441f73c399 · outbound

This paper cites Global meets local: Dual activation hashing network for large-scale fine-grained image retrieval,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Global meets local: Dual activation hashing network for large-scale fine-grained image retrieval,

Reference 9

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This paper cites Deep polarized network for supervised learning of accurate binary hashing codes,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Deep polarized network for supervised learning of accurate binary hashing codes,

Reference 10

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This paper cites Central similarity quantization for efficient image and video retrieval,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Central similarity quantization for efficient image and video retrieval,

Reference 11

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Observation b2081cd2-98ff-4781-8cad-e0e2aa56bf57 · outbound

This paper cites SEMICON: A learning-to-hash solution for large-scale fine-grained image retrieval,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval SEMICON: A learning-to-hash solution for large-scale fine-grained image retrieval,

Reference 12

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This paper cites Attribute-aware deep hashing with self-consistency for large-scale fine-grained image retrieval,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Attribute-aware deep hashing with self-consistency for large-scale fine-grained image retrieval,

Reference 13

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 14

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This paper cites Training data-efficient image transformers & distillation through attention,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Training data-efficient image transformers & distillation through attention,

Reference 15

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Observation b6e84376-5ff2-49a7-a0ff-82f2d8091d07 · outbound

This paper cites Msvit: training multiscale vision transformers for image retrieval,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Msvit: training multiscale vision transformers for image retrieval,

Reference 16

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Observation d72ef937-6e70-4267-a3bd-e4d367a13bd7 · outbound

This paper cites Swinfghash: Fine-grained image retrieval via transformer-based hashing network.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Swinfghash: Fine-grained image retrieval via transformer-based hashing network

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:03:18.665372Z

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This paper cites No matter how: Top-down effects of verbal and semantic category knowledge on early visual perception,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval No matter how: Top-down effects of verbal and semantic category knowledge on early visual perception,

Reference 18

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This paper cites Learning attention-guided pyrami- dal features for few-shot fine-grained recognition,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Learning attention-guided pyrami- dal features for few-shot fine-grained recognition,

Reference 19

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Observation 75cab98d-bdb3-47d7-aad5-1d76f4bb6fd7 · outbound

This paper cites Divide-and-conquer: Confluent triple-flow network for rgb-t salient object detection,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Divide-and-conquer: Confluent triple-flow network for rgb-t salient object detection,

Reference 20

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This paper cites Adaptive token sampling for efficient vision transformers,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Adaptive token sampling for efficient vision transformers,

Reference 21

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This paper cites Token merging: Your vit but faster,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Token merging: Your vit but faster,

Reference 22

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This paper cites Dynamicvit: Efficient vision transformers with dynamic token sparsification,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Dynamicvit: Efficient vision transformers with dynamic token sparsification,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:03:18.574935Z

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Observation cdfa8a4f-254a-42f8-b136-6815ae8fcd5f · outbound

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

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Bilinear CNN models for fine- grained visual recognition,

Reference 24

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Observation b1774ac6-019c-4c4a-8fb7-0271b2e7b65e · outbound

This paper cites Compact bilinear pooling,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Compact bilinear pooling,

Reference 25

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This paper cites Hierarchical bilinear pooling for fine-grained visual recognition,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Hierarchical bilinear pooling for fine-grained visual recognition,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:03:18.530300Z

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This paper cites Deep LAC: deep localization, alignment and classification for fine-grained recognition,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Deep LAC: deep localization, alignment and classification for fine-grained recognition,

Reference 27

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This paper cites Boosting few-shot fine-grained recognition with background suppression and foreground alignment,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Boosting few-shot fine-grained recognition with background suppression and foreground alignment,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:03:18.499742Z

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This paper cites Fine-grained visual classification via internal ensemble learning transformer,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Fine-grained visual classification via internal ensemble learning transformer,

Reference 29

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This paper cites Delving into multi- modal prompting for fine-grained visual classification,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Delving into multi- modal prompting for fine-grained visual classification,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:03:18.468989Z

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This paper cites Part-based r-cnns for fine-grained category detection,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Part-based r-cnns for fine-grained category detection,

Reference 31

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Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval P-CNN: part-based con- volutional neural networks for fine-grained visual categorization,

Reference 32

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This paper cites Cross-part learning for fine-grained image classification,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Cross-part learning for fine-grained image classification,

Reference 33

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This paper cites IMAGDressing-v1: Customizable Virtual Dressing.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval IMAGDressing-v1: Customizable Virtual Dressing

Reference 34

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This paper cites Hyperbolic vision transformers: Combining improvements in metric learning,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Hyperbolic vision transformers: Combining improvements in metric learning,

Reference 35

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This paper cites Proxynca++: Revisiting and revitalizing proxy neighborhood component analysis,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Proxynca++: Revisiting and revitalizing proxy neighborhood component analysis,

Reference 36

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This paper cites Boosting vision transformers for image retrieval,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Boosting vision transformers for image retrieval,

Reference 37

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This paper cites Re-id- leak: Membership inference attacks against person re-identification,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Re-id- leak: Membership inference attacks against person re-identification,

Reference 38

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This paper cites Git: Graph interactive transformer for vehicle re-identification,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Git: Graph interactive transformer for vehicle re-identification,

Reference 39

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This paper cites Deep saliency hashing for fine-grained retrieval,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Deep saliency hashing for fine-grained retrieval,

Reference 40

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This paper cites A 2-net: Learning attribute-aware hash codes for large-scale fine-grained image retrieval,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval A 2-net: Learning attribute-aware hash codes for large-scale fine-grained image retrieval,

Reference 41

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This paper cites Fine-grained hashing with double filtering,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Fine-grained hashing with double filtering,

Reference 42

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Observation 276a0e0d-42d0-4f59-8295-1f83af87176e · outbound

This paper cites Deep progressive asymmetric quantization based on causal intervention for fine-grained image retrieval,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Deep progressive asymmetric quantization based on causal intervention for fine-grained image retrieval,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:03:18.279877Z

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This paper cites Deep neighbor- hood structure-preserving hashing for large-scale image retrieval,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Deep neighbor- hood structure-preserving hashing for large-scale image retrieval,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:03:18.263340Z

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This paper cites Supervised deep hashing for scalable face image retrieval,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Supervised deep hashing for scalable face image retrieval,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:03:18.248036Z

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Observation c1a59fcf-71c2-4613-9006-05fd2d4b68d7 · outbound

This paper cites Weakly-supervised semantic guided hashing for social image retrieval,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Weakly-supervised semantic guided hashing for social image retrieval,

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-16T11:03:17.701437Z

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This paper cites Fast locality-sensitive hashing,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Fast locality-sensitive hashing,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:03:18.222052Z

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Observation 99596cb4-eecd-4eae-a4d2-c4ae2384cdd8 · outbound

This paper cites HHF: hashing-guided hinge function for deep hashing retrieval,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval HHF: hashing-guided hinge function for deep hashing retrieval,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:03:18.206682Z

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Observation 2fcd874a-9ba9-486f-acf0-a495820ae834 · outbound

This paper cites Iterative quantiza- tion: A procrustean approach to learning binary codes for large-scale image retrieval,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Iterative quantiza- tion: A procrustean approach to learning binary codes for large-scale image retrieval,

Reference 49

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Observation 6dd415e7-adf2-401c-9d95-a253bd6a864f · outbound

This paper cites Asymmetric deep supervised hashing,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Asymmetric deep supervised hashing,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:03:18.179962Z

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Observation b7c358e6-e337-4038-9b71-31612d940813 · outbound

This paper cites Self-paced relational contrastive hash- ing for large-scale image retrieval,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Self-paced relational contrastive hash- ing for large-scale image retrieval,

Reference 51

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This paper cites Alleviating over-fitting in hashing-based fine-grained image retrieval: From causal feature learning to binary-injected hash learning,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Alleviating over-fitting in hashing-based fine-grained image retrieval: From causal feature learning to binary-injected hash learning,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:03:18.146521Z

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This paper cites Densifying one permutation hashing via rota- tion for fast near neighbor search,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Densifying one permutation hashing via rota- tion for fast near neighbor search,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:03:18.129871Z

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This paper cites Global Vision Transformer Pruning with Hessian-Aware Saliency.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Global Vision Transformer Pruning with Hessian-Aware Saliency

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-08-16T11:03:17.894072Z

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This paper cites Chasing sparsity in vision transformers: An end-to-end exploration,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Chasing sparsity in vision transformers: An end-to-end exploration,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:03:18.114277Z

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This paper cites IA-RED$^2$: Interpretability-Aware Redundancy Reduction for Vision Transformers.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval IA-RED$^2$: Interpretability-Aware Redundancy Reduction for Vision Transformers

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-08-16T11:03:17.872224Z

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Observation f4ff2d2c-f556-4f96-ad0d-9f2971db04ea · outbound

This paper cites Evo-vit: Slow-fast token evolution for dynamic vision transformer,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Evo-vit: Slow-fast token evolution for dynamic vision transformer,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:03:18.083766Z

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This paper cites Evit: Expediting vision transformers via token reorganizations,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Evit: Expediting vision transformers via token reorganizations,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:03:18.098944Z

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This paper cites Squeeze-and-excitation networks,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Squeeze-and-excitation networks,

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-16T11:03:17.763945Z

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This paper cites Vitkd: Feature- based knowledge distillation for vision transformers,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Vitkd: Feature- based knowledge distillation for vision transformers,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:03:18.057553Z

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This paper cites Transformer- based distillation hash learning for image retrieval,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Transformer- based distillation hash learning for image retrieval,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:03:18.042547Z

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This paper cites Distilling the Knowledge in a Neural Network.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Distilling the Knowledge in a Neural Network

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-16T11:03:17.777348Z

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This paper cites Deep listwise triplet hashing for fine-grained image retrieval,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Deep listwise triplet hashing for fine-grained image retrieval,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:03:18.027133Z

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This paper cites Sub-region localized hashing for fine-grained image retrieval,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Sub-region localized hashing for fine-grained image retrieval,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:03:18.009858Z

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This paper cites Exchnet: A unified hashing network for large-scale fine-grained image retrieval,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Exchnet: A unified hashing network for large-scale fine-grained image retrieval,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:03:17.994907Z

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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,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:03:17.980175Z

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This paper cites Vegfru: A domain-specific dataset for fine-grained visual categorization,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Vegfru: A domain-specific dataset for fine-grained visual categorization,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:03:17.964810Z

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Observation 4d857f82-f2f4-4d23-9d6d-ff1e7c252cc8 · outbound

This paper cites Food-101 - mining discriminative components with random forests,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Food-101 - mining discriminative components with random forests,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:03:17.949248Z

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This paper cites The inaturalist species classifi- cation and detection dataset,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval The inaturalist species classifi- cation and detection dataset,

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-16T11:03:17.809074Z

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This paper cites Char- acteristics matching based hash codes generation for efficient fine- grained image retrieval,.

Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval Char- acteristics matching based hash codes generation for efficient fine- grained image retrieval,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:03:17.924653Z

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