Paper Citation Record · LEDGER

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts

As of 8 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2506.04673.

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pith.paper-citation-record.v1
2506.04673 v1

Coverage vector

measured 46 of 46 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

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External citation measurements

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Outbound references

Observation 85c08559-0ce4-4aa3-800a-b11ad1901db7 · outbound

This paper cites This looks like that: deep learning for interpretable image recognition,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts This looks like that: deep learning for interpretable image recognition,

Reference 1

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This paper cites Learning transferable conceptual prototypes for interpretable unsupervised domain adaptation,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Learning transferable conceptual prototypes for interpretable unsupervised domain adaptation,

Reference 2

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This paper cites Describe, spot and explain: Interpretable representation learning for discriminative visual reasoning,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Describe, spot and explain: Interpretable representation learning for discriminative visual reasoning,

Reference 3

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This paper cites An interpretable deep architecture for similarity learning built upon hierarchical concepts,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts An interpretable deep architecture for similarity learning built upon hierarchical concepts,

Reference 4

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This paper cites Explainability enhanced object detection transformer with feature disentanglement,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Explainability enhanced object detection transformer with feature disentanglement,

Reference 5

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This paper cites Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai,

Reference 6

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This paper cites Prediction with visual evidence: Sketch classification explanation via stroke- level attributions,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Prediction with visual evidence: Sketch classification explanation via stroke- level attributions,

Reference 7

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This paper cites Joint answering and explanation for visual commonsense reasoning,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Joint answering and explanation for visual commonsense reasoning,

Reference 8

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This paper cites Interpretable image recognition by constructing transparent embedding space,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Interpretable image recognition by constructing transparent embedding space,

Reference 9

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This paper cites A grass- mann manifold handbook: Basic geometry and computational aspects,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts A grass- mann manifold handbook: Basic geometry and computational aspects,

Reference 10

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This paper cites Pip- net: Patch-based intuitive prototypes for interpretable image classification,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Pip- net: Patch-based intuitive prototypes for interpretable image classification,

Reference 11

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This paper cites ProtoS-ViT: Visual foundation models for sparse self-explainable classifications.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts ProtoS-ViT: Visual foundation models for sparse self-explainable classifications

Reference 12

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

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 13

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This paper cites Learning to prompt for vision-language models,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Learning to prompt for vision-language models,

Reference 14

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This paper cites Conditional prompt learning for vision-language models,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Conditional prompt learning for vision-language models,

Reference 15

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This paper cites Visual-language prompt tuning with knowledge-guided context optimization,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Visual-language prompt tuning with knowledge-guided context optimization,

Reference 16

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Prompt- aligned gradient for prompt tuning,

Reference 17

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Clip-adapter: Better vision-language models with feature adapters,

Reference 18

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling

Reference 19

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Task resid- ual for tuning vision-language models,

Reference 20

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This paper cites Adaptive mixtures of local experts,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Adaptive mixtures of local experts,

Reference 21

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Hierarchical mixtures of experts and the em algorithm,

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Nonlinear models using dirichlet process mixtures,

Reference 23

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts A parallel mixture of svms for very large scale problems,

Reference 24

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 25

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

Reference 26

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This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Adap- tive gating in mixture-of-experts based language models,

Reference 28

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This paper cites DeepSeekMoE: Towards ultimate expert specialization in mixture-of-experts language models,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts DeepSeekMoE: Towards ultimate expert specialization in mixture-of-experts language models,

Reference 29

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Emerging properties in self-supervised vision transformers,

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts DINOv2: Learning Robust Visual Features without Supervision

Reference 31

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Lora: Low-rank adaptation of large language models

Reference 32

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

Reference 33

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts The power of scale for parameter-efficient prompt tuning,

Reference 34

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Prefix-tuning: Optimizing continuous prompts for generation,

Reference 35

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Sparse low-rank adaptation of pre-trained language models,

Reference 36

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This paper cites Enhancing few-shot out-of-distribution detection with pre- trained model features,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Enhancing few-shot out-of-distribution detection with pre- trained model features,

Reference 37

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Representation Learning with Contrastive Predictive Coding

Reference 38

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts The Caltech-UCSD Birds-200-2011 Dataset,

Reference 39

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Fine-Grained Visual Classification of Aircraft

Reference 40

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Matching networks for one shot learning,

Reference 41

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Meta-learning with differentiable closed-form solvers

Reference 42

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This paper cites Few-Shot Learning with Metric-Agnostic Conditional Embeddings.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Few-Shot Learning with Metric-Agnostic Conditional Embeddings

Reference 43

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Describing textures in the wild,

Reference 44

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Evaluation and improvement of interpretability for self-explainable part-prototype networks,

Reference 45

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Observation b0b9cfe8-787b-48a7-bf65-2822cde8bcdc · outbound

This paper cites Learning support and trivial prototypes for interpretable image classification,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Learning support and trivial prototypes for interpretable image classification,

Reference 46

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