Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation | Paper Citation Record

Observation dfd3ef15-033a-4865-aabe-f6dd70d9f49a · outbound

This paper cites GPT-4 Technical Report.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation GPT-4 Technical Report

Reference 1

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This paper cites Deep vit features as dense visual descriptors.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Deep vit features as dense visual descriptors

Reference 2

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This paper cites Foundation models defining a new era in vision: a sur- vey and outlook.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Foundation models defining a new era in vision: a sur- vey and outlook

Reference 3

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This paper cites Explaining neural scaling laws.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Explaining neural scaling laws

Reference 4

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This paper cites Knowledge distillation: A good teacher is patient and consistent.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Knowledge distillation: A good teacher is patient and consistent

Reference 5

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This paper cites On the Opportunities and Risks of Foundation Models.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation On the Opportunities and Risks of Foundation Models

Reference 6

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This paper cites Language models are few-shot learners.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Language models are few-shot learners

Reference 7

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This paper cites Coco-stuff: Thing and stuff classes in context.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Coco-stuff: Thing and stuff classes in context

Reference 8

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This paper cites All about structure: Adapting struc- tural information across domains for boosting seman- tic segmentation.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation All about structure: Adapting struc- tural information across domains for boosting seman- tic segmentation

Reference 9

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This paper cites Zero-shot Domain Generalization of Foundational Models for 3D Medical Image Segmentation: An Experimental Study.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Zero-shot Domain Generalization of Foundational Models for 3D Medical Image Segmentation: An Experimental Study

Reference 10

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This paper cites FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance

Reference 11

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This paper cites Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs

Reference 12

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This paper cites Reproducible scaling laws for contrastive language-image learning.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Reproducible scaling laws for contrastive language-image learning

Reference 13

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This paper cites Cat-seg: Cost aggregation for open-vocabulary semantic segmentation.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Cat-seg: Cost aggregation for open-vocabulary semantic segmentation

Reference 14

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This paper cites Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Reference 15

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This paper cites The cityscapes dataset for semantic urban scene under- standing.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation The cityscapes dataset for semantic urban scene under- standing

Reference 16

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This paper cites Semantic image segmentation: Two decades of research.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Semantic image segmentation: Two decades of research

Reference 17

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This paper cites Cross-domain transfer learning with corte: Consistent and reliable transfer from black-box to lightweight segmentation model.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Cross-domain transfer learning with corte: Consistent and reliable transfer from black-box to lightweight segmentation model

Reference 18

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 19

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This paper cites Uncertainty reduction for model adaptation in semantic segmentation.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Uncertainty reduction for model adaptation in semantic segmentation

Reference 20

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Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Unresolved cited work

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This paper cites Distilling the Knowledge in a Neural Network.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Distilling the Knowledge in a Neural Network

Reference 22

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This paper cites Dis- tilling the knowledge in a neural network, 2015.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Dis- tilling the knowledge in a neural network, 2015

Reference 23

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This paper cites Large Language Models Are Reasoning Teachers.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Large Language Models Are Reasoning Teachers

Reference 24

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This paper cites Cycada: Cycle-consistent adversarial domain adaptation.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Cycada: Cycle-consistent adversarial domain adaptation

Reference 25

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This paper cites FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation

Reference 26

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This paper cites Conditional generative adversarial net- work for structured domain adaptation.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Conditional generative adversarial net- work for structured domain adaptation

Reference 27

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This paper cites Daformer: Improving network architectures and train- ing strategies for domain-adaptive semantic segmen- tation.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Daformer: Improving network architectures and train- ing strategies for domain-adaptive semantic segmen- tation

Reference 28

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Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Hrda: Context-aware high-resolution domain-adaptive se- mantic segmentation

Reference 29

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This paper cites The Platonic Representation Hypothesis.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation The Platonic Representation Hypothesis

Reference 30

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This paper cites Adaptive mixtures of local experts.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Adaptive mixtures of local experts

Reference 31

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This paper cites Tiny- BERT: Distilling BERT for natural language under- standing.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Tiny- BERT: Distilling BERT for natural language under- standing

Reference 32

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This paper cites Dinov2 meets text: A unified framework for image-and pixel-level vision-language alignment.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Dinov2 meets text: A unified framework for image-and pixel-level vision-language alignment

Reference 33

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Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Adam: A method for stochastic gradient descent

Reference 34

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This paper cites Berg, Wan-Yen Lo, Piotr Doll´ar, and Ross B.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Berg, Wan-Yen Lo, Piotr Doll´ar, and Ross B

Reference 35

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This paper cites General- ize then adapt: Source-free domain adaptive semantic segmentation.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation General- ize then adapt: Source-free domain adaptive semantic segmentation

Reference 36

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This paper cites Testing knowledge distilla- tion theories with dataset size.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Testing knowledge distilla- tion theories with dataset size

Reference 37

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Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Dine: Domain adaptation from single and multiple black-box predictors

Reference 38

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Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation TinyGSM: achieving >80% on GSM8k with small language models

Reference 39

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Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Visual instruction tuning

Reference 40

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Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Early-learning reg- ularization prevents memorization of noisy labels

Reference 41

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Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Adaptive multi-teacher multi-level knowledge distillation

Reference 42

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Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Source-free domain adaptation for semantic segmentation

Reference 43

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Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Improved knowledge distillation via teacher assistant

Reference 44

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This paper cites Orca: Progressive Learning from Complex Explanation Traces of GPT-4.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Orca: Progressive Learning from Complex Explanation Traces of GPT-4

Reference 45

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Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Unresolved cited work

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This paper cites Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision

Reference 47

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This paper cites Learning transferable visual models from natu- ral language supervision.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Learning transferable visual models from natu- ral language supervision

Reference 48

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This paper cites Am-radio: Agglomerative vision foun- dation model reduce all domains into one.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Am-radio: Agglomerative vision foun- dation model reduce all domains into one

Reference 49

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This paper cites Raspberry Pi 4 Model B.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Raspberry Pi 4 Model B

Reference 50

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Observation 1fce5f35-071f-41d7-858d-d133b1e71ef2 · outbound

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

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Playing for data: Ground truth from computer games

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:35:45.392553Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c320ea18-79d8-4695-b57f-17ed4bf5b53d · outbound

This paper cites Curriculum graph co- teaching for multi-target domain adaptation.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Curriculum graph co- teaching for multi-target domain adaptation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:35:45.250890Z

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Observation 4563e566-5c9d-462d-bde5-93cc6d2d6b7f · outbound

This paper cites Acdc: The adverse conditions dataset with correspon- dences for semantic driving scene understanding.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Acdc: The adverse conditions dataset with correspon- dences for semantic driving scene understanding

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:35:45.034236Z

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Observation 29ce56d1-e14f-4b50-8844-30de6a593483 · outbound

This paper cites Unic: Universal classification models via multi-teacher dis- tillation.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Unic: Universal classification models via multi-teacher dis- tillation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:35:44.781820Z

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Observation 0cd5722b-26e3-4fb9-9c32-f7d9031215d8 · outbound

This paper cites BBox-Adapter: Lightweight Adapting for Black-Box Large Language Models.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation BBox-Adapter: Lightweight Adapting for Black-Box Large Language Models

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-08-05T13:35:41.386393Z

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Observation 522d411f-122d-4a3e-97e9-82568c520394 · outbound

This paper cites Dime-fm: Distilling multimodal and efficient foundation models.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Dime-fm: Distilling multimodal and efficient foundation models

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:35:44.545248Z

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Observation 47d4f511-0fd4-4495-bf8f-2764fbd6645a · outbound

This paper cites Scal- ing laws vs model architectures: How does inductive bias influence scaling? In Findings of the Association for Computational Linguistics: EMNLP 2023 , 2023.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Scal- ing laws vs model architectures: How does inductive bias influence scaling? In Findings of the Association for Computational Linguistics: EMNLP 2023 , 2023

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:35:44.217049Z

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Observation b600055e-0eeb-4171-bb5e-6eb9dd727339 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-05T13:35:39.185035Z

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Observation 8af4d71c-6a3a-4efd-85c6-4a45efedc56e · outbound

This paper cites Ensemble Adversarial Training: Attacks and Defenses.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Ensemble Adversarial Training: Attacks and Defenses

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-05T13:35:39.282015Z

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Observation fd7a6fb7-1644-4ea3-b03a-951a02d797fc · outbound

This paper cites Contrastive Learning Rivals Masked Image Modeling in Fine-tuning via Feature Distillation.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Contrastive Learning Rivals Masked Image Modeling in Fine-tuning via Feature Distillation

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-05T13:35:39.417944Z

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Observation 58b81203-636c-4b6d-8a9b-649d2e3499e6 · outbound

This paper cites Stronger fewer & superior: Harness- ing vision foundation models for domain generalized semantic segmentation.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Stronger fewer & superior: Harness- ing vision foundation models for domain generalized semantic segmentation

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:35:43.986558Z

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Observation f2011348-aeb6-43b0-b1a9-25b974d6b970 · outbound

This paper cites Clip-dinoiser: Teaching clip a few dino tricks for open-vocabulary semantic segmentation.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Clip-dinoiser: Teaching clip a few dino tricks for open-vocabulary semantic segmentation

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:35:43.767196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 53ca83a2-7d34-4a9c-bd4b-9a257b6b2837 · outbound

This paper cites Open- vocabulary panoptic segmentation with text-to-image diffusion models.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Open- vocabulary panoptic segmentation with text-to-image diffusion models

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:35:43.525264Z

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Observation 1dc8d313-9c4d-422b-820b-14975eb9a42b · outbound

This paper cites Side adapter network for open- vocabulary semantic segmentation.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Side adapter network for open- vocabulary semantic segmentation

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:35:43.360661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7b0070ba-ef3b-4ed6-a736-5c34ed621383 · outbound

This paper cites Knowledge distillation using hierar- chical self-supervision augmented distribution.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Knowledge distillation using hierar- chical self-supervision augmented distribution

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:35:43.108991Z

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Observation c84a0275-0718-4baa-8be1-4fc1e0c26e4c · outbound

This paper cites A gift from knowledge distillation: Fast opti- mization, network minimization and transfer learning.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation A gift from knowledge distillation: Fast opti- mization, network minimization and transfer learning

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:35:42.814759Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 31705a7a-6292-4142-a65a-25910d8e0ef1 · outbound

This paper cites Learning from multiple teacher networks.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Learning from multiple teacher networks

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:35:42.563701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0a75b93c-4ba1-42df-b1e5-43e82c357f0b · outbound

This paper cites Os- prey: Pixel understanding with visual instruction tun- ing.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Os- prey: Pixel understanding with visual instruction tun- ing

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:35:42.367136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 13210f4e-da50-48d9-8b07-21105deac2ba · outbound

This paper cites Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-05T13:35:40.386891Z

Source-reported events for the cited work

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Observation e3ebf2b1-58e1-420d-a259-54f1bcc574d6 · outbound

This paper cites Unsupervised Domain Adaptation of Black-Box Source Models.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Unsupervised Domain Adaptation of Black-Box Source Models

Reference 70

Resolution: verified exact; local_arxiv, observed 2026-08-05T13:35:41.063886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1148481a-63fe-4115-b302-50f983bf862d · outbound

This paper cites Black-box unsupervised domain adapta- tion with bi-directional atkinson-shiffrin memory.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Black-box unsupervised domain adapta- tion with bi-directional atkinson-shiffrin memory

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:35:42.154754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 97d4bb45-bf4c-4562-bb02-ff0994b71a15 · outbound

This paper cites MROVSeg: Breaking the Resolution Curse of Vision-Language Models in Open-Vocabulary Image Segmentation.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation MROVSeg: Breaking the Resolution Curse of Vision-Language Models in Open-Vocabulary Image Segmentation

Reference 72

Resolution: malformed identifier; local_arxiv, observed 2026-08-05T13:35:40.823109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d0b4a9ab-d9ed-471d-be2f-c35e203921d8 · outbound

This paper cites an unresolved cited work.

Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation Unresolved cited work

Reference 2019

Resolution: parse uncertain; raw_fallback, observed 2026-08-05T13:35:45.663246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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