Paper Citation Record · LEDGER

Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models

As of 20 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 3 inbound Pith citation observations for arXiv:2411.17767.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.17767 v1

Coverage vector

measured 57 of 57 reference resolution

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measured 3 of 3 inbound itemization

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Source: arxiv_reference, observed 2026-07-04T16:19:57.744872Z

Reference resolution

57 of 57 outbound references displayed

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External citation measurements

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Outbound references

Observation 52a95491-c158-4d6e-a5b4-df770b86a88f · outbound

This paper cites Segment Any Anomaly without Training via Hybrid Prompt Regularization.

Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Segment Any Anomaly without Training via Hybrid Prompt Regularization

Reference 1

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Observation 17f7ecd5-1a0a-4944-85bd-a714b98f0aeb · outbound

This paper cites End-to- end object detection with transformers.

Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models End-to- end object detection with transformers

Reference 2

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Observation 19126069-877b-4920-9619-0c516c3f3f7e · outbound

This paper cites Data uncertainty learning in face recognition.

Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Data uncertainty learning in face recognition

Reference 3

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Observation b783644e-434b-4d27-b0c3-6c6aa05a4f30 · outbound

This paper cites Confidence- based reliable learning under dual noises.

Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Confidence- based reliable learning under dual noises

Reference 4

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Observation 38bd727e-9fbd-4168-b2bf-042efad2f9d8 · outbound

This paper cites Learning sample difficulty from pre-trained models for reliable prediction.

Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Learning sample difficulty from pre-trained models for reliable prediction

Reference 5

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Observation 33722fe9-2f77-4e44-9e79-c71e14c3c542 · outbound

This paper cites Decomposition of uncer- tainty in bayesian deep learning for efficient and risk-sensitive learning.

Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Decomposition of uncer- tainty in bayesian deep learning for efficient and risk-sensitive learning

Reference 6

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Observation 17941345-7a04-43c8-85b1-aeed0adc38e7 · outbound

This paper cites Aleatory or epis- temic? does it matter? Structural safety, 31(2):105–112,.

Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Aleatory or epis- temic? does it matter? Structural safety, 31(2):105–112,

Reference 7

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Observation 0f5f05a1-fbbf-4ec2-9f14-e46be1d6ff54 · outbound

This paper cites Dietterich and Alex Guyer.

Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Dietterich and Alex Guyer

Reference 8

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

Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models An image is worth 16x16 words: Transformers for image recognition at scale

Reference 9

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Observation 7000785c-0d3b-4422-b287-8445680385d8 · outbound

This paper cites Everingham, S.

Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Everingham, S

Reference 10

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Observation a2a6b4d6-f2e0-4ca3-b113-c27ef802bad1 · outbound

This paper cites Dropout as a Bayesian approximation: Representing model uncertainty in deep learn- ing.

Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Dropout as a Bayesian approximation: Representing model uncertainty in deep learn- ing

Reference 11

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This paper cites YOLOX: Exceeding YOLO Series in 2021.

Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models YOLOX: Exceeding YOLO Series in 2021

Reference 12

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This paper cites Deep residual learning for image recognition.

Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Deep residual learning for image recognition

Reference 13

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This paper cites Mask r-cnn.

Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Mask r-cnn

Reference 14

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This paper cites Open-set image tagging with multi-grained text supervision.

Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Open-set image tagging with multi-grained text supervision

Reference 15

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This paper cites Tag2Text: Guiding Vision-Language Model via Image Tagging.

Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Tag2Text: Guiding Vision-Language Model via Image Tagging

Reference 16

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This paper cites Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods.

Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods

Reference 17

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This paper cites What uncertainties do we need in bayesian deep learning for computer vision? In Advances in Neural Information Processing Systems.

Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models What uncertainties do we need in bayesian deep learning for computer vision? In Advances in Neural Information Processing Systems

Reference 18

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This paper cites Why normalizing flows fail to detect out-of-distribution data.

Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Why normalizing flows fail to detect out-of-distribution data

Reference 19

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This paper cites Berg, Wan-Yen Lo, Piotr Dollar, and Ross Girshick.

Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Berg, Wan-Yen Lo, Piotr Dollar, and Ross Girshick

Reference 20

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This paper cites Blip: Bootstrapping language-image pre-training for unified vision- language understanding and generation.

Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Blip: Bootstrapping language-image pre-training for unified vision- language understanding and generation

Reference 21

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Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models BLIP- 2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 22

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Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Clipsam: Clip and sam collaboration for zero-shot anomaly segmentation, 2024

Reference 23

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Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Evaluating object hallucination in large vision-language models

Reference 24

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Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Gmm- seg: Gaussian mixture based generative semantic segmenta- tion models

Reference 25

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Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Microsoft coco: Common objects in context

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Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Focal loss for dense object detection

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Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models

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Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Visual instruction tuning

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Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

Reference 30

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Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Towards robust adaptive object detection under noisy annotations

Reference 31

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Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Swin transformer: Hierarchical vision transformer using shifted windows

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Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Calibrating deep neural networks using focal loss

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Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Deep deterministic uncertainty: A new simple baseline

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Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Regularizing Neural Networks by Penalizing Confident Output Distributions

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Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Learning transferable visual models from natural language supervision

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Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models YOLOv3: An Incremental Improvement

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Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection

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Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Faster r-cnn: Towards real-time object detection with region proposal networks

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Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Grounded sam: Assembling open-world models for diverse visual tasks, 2024

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Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Segmenter: Transformer for semantic segmentation

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Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models FCOS: fully convolutional one-stage object detection

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Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Mlp-mixer: An all-mlp architecture for vision

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Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Uncertainty estimation using a single deep de- terministic neural network

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Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Segment and Caption Anything

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Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Segformer: Simple and efficient design for semantic segmentation with transform- ers

Reference 47

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Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models BDD100K: A diverse driving dataset for heterogeneous mul- titask learning

Reference 48

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Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models mixup: Beyond empirical risk minimization

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Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Dino: Detr with 10 improved denoising anchor boxes for end-to-end object detec- tion

Reference 50

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Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Unresolved cited work

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This paper cites Recognize Anything: A Strong Image Tagging Model.

Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Recognize Anything: A Strong Image Tagging Model

Reference 52

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This paper cites Segment any- thing model for medical image segmentation: Current ap- plications and future directions.

Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Segment any- thing model for medical image segmentation: Current ap- plications and future directions

Reference 53

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This paper cites Analyzing and mitigating object hallucination in large vision- language models.

Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Analyzing and mitigating object hallucination in large vision- language models

Reference 54

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Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Minigpt-4: Enhancing vision-language understanding with advanced large language models

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:29:19.716744Z

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This paper cites Deformable {detr}: Deformable transformers for end-to-end object detection.

Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models Deformable {detr}: Deformable transformers for end-to-end object detection

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:29:19.706904Z

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This paper cites dog” and “bird.

Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models dog” and “bird

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:29:19.696189Z

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Pith citing papers

Observation 78f2582b-d383-4c68-959b-63e97d64830b · inbound

Are vision language models robust to uncertain inputs? cites this paper.

Are vision language models robust to uncertain inputs? Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models

Reference 9

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Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models cites this paper.

Delving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models

Reference 5

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Observation 900b8d08-ad12-4449-8194-a097879fc0e7 · inbound

PointVG-R: Internalizing Geometric Reasoning in MLLMs for Precise Pointing Localization via Visual Chain of Thought cites this paper.

PointVG-R: Internalizing Geometric Reasoning in MLLMs for Precise Pointing Localization via Visual Chain of Thought Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models

Reference 20

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