Paper Citation Record · LEDGER

Interpreting Object-level Foundation Models via Visual Precision Search

As of 14 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 1 inbound Pith citation observation for arXiv:2411.16198.

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

pith.paper-citation-record.v1
2411.16198 v4

Coverage vector

measured 62 of 62 reference resolution

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measured 63 of 63 standing notices

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-22T21:32:10.246997Z

Reference resolution

62 of 62 outbound references displayed

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External citation measurements

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Outbound references

Observation 02375991-57e8-4663-8881-038582551b4f · outbound

This paper cites SLIC superpix- els compared to state-of-the-art superpixel methods.

Interpreting Object-level Foundation Models via Visual Precision Search SLIC superpix- els compared to state-of-the-art superpixel methods

Reference 1

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Observation 0a6f1f55-562f-4d0b-9ded-2793ab4afd55 · outbound

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

Interpreting Object-level Foundation Models via Visual Precision Search End-to- end object detection with transformers

Reference 2

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Observation 3669d7c9-ad23-4b44-95fc-369a40b4cbc0 · outbound

This paper cites Grad-CAM++: General- ized gradient-based visual explanations for deep convolu- tional networks.

Interpreting Object-level Foundation Models via Visual Precision Search Grad-CAM++: General- ized gradient-based visual explanations for deep convolu- tional networks

Reference 3

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Observation 2c4adc72-412b-4f66-ba00-5c399257db93 · outbound

This paper cites End-to-end autonomous driving: Challenges and frontiers.

Interpreting Object-level Foundation Models via Visual Precision Search End-to-end autonomous driving: Challenges and frontiers

Reference 4

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Observation eb97fe33-2c49-4207-a243-dfe2e136151e · outbound

This paper cites Sim2Word: Explaining similarity with representative attribute words via counterfactual expla- nations.

Interpreting Object-level Foundation Models via Visual Precision Search Sim2Word: Explaining similarity with representative attribute words via counterfactual expla- nations

Reference 5

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Observation 926f83f9-0ddc-41e8-96f6-4eaec434d3cd · outbound

This paper cites Less is more: Fewer interpretable region via submodular subset selection.

Interpreting Object-level Foundation Models via Visual Precision Search Less is more: Fewer interpretable region via submodular subset selection

Reference 6

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Observation f2af0a76-3475-4f34-9e2a-726e7a98a267 · outbound

This paper cites Yolo-world: Real-time open- vocabulary object detection.

Interpreting Object-level Foundation Models via Visual Precision Search Yolo-world: Real-time open- vocabulary object detection

Reference 7

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Observation 6cfae6ae-7578-4531-8107-c298858dc6c0 · outbound

This paper cites Submodular functions, matroids, and cer- tain polyhedra.

Interpreting Object-level Foundation Models via Visual Precision Search Submodular functions, matroids, and cer- tain polyhedra

Reference 8

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Observation 591debad-9020-4581-afe4-bcc5d20b0c6f · outbound

This paper cites A review and comparative study on probabilistic ob- ject detection in autonomous driving.

Interpreting Object-level Foundation Models via Visual Precision Search A review and comparative study on probabilistic ob- ject detection in autonomous driving

Reference 9

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Observation 13fcac8e-19fb-431b-a119-02df0b429229 · outbound

This paper cites Submodular functions and optimization.

Interpreting Object-level Foundation Models via Visual Precision Search Submodular functions and optimization

Reference 10

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Observation b7760aa8-084f-4bd0-ac5a-06784d71712d · outbound

This paper cites In- terpreting clip’s image representation via text-based decom- position.

Interpreting Object-level Foundation Models via Visual Precision Search In- terpreting clip’s image representation via text-based decom- position

Reference 11

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Observation a18a1483-b824-4324-828f-6e62d284548a · outbound

This paper cites Going beyond xai: A system- atic survey for explanation-guided learning.

Interpreting Object-level Foundation Models via Visual Precision Search Going beyond xai: A system- atic survey for explanation-guided learning

Reference 12

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Observation 714027f7-63ab-45c7-a4ff-790be986ffe3 · outbound

This paper cites Explain to Fix: A Framework to Interpret and Correct DNN Object Detector Predictions.

Interpreting Object-level Foundation Models via Visual Precision Search Explain to Fix: A Framework to Interpret and Correct DNN Object Detector Predictions

Reference 13

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Observation 0124071e-e29b-4097-8553-c4e06cfabf02 · outbound

This paper cites LVIS: A dataset for large vocabulary instance segmentation.

Interpreting Object-level Foundation Models via Visual Precision Search LVIS: A dataset for large vocabulary instance segmentation

Reference 14

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Observation 3e6ad84f-1cf1-4673-b19b-e533abaef9c3 · outbound

This paper cites Mask R-CNN.

Interpreting Object-level Foundation Models via Visual Precision Search Mask R-CNN

Reference 15

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Observation 2f4138cf-4372-4078-a8e8-fb02958f7959 · outbound

This paper cites Planning-oriented autonomous driving.

Interpreting Object-level Foundation Models via Visual Precision Search Planning-oriented autonomous driving

Reference 16

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Observation c0b40938-787b-4b94-a325-d3487a702ac3 · outbound

This paper cites Diverse explanations for object detectors with nesterov-accelerated igos++.

Interpreting Object-level Foundation Models via Visual Precision Search Diverse explanations for object detectors with nesterov-accelerated igos++

Reference 17

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Observation f3711944-088f-42c5-a45d-b2e668a7a9fa · outbound

This paper cites Comparing the decision-making mechanisms by transformers and cnns via explanation methods.

Interpreting Object-level Foundation Models via Visual Precision Search Comparing the decision-making mechanisms by transformers and cnns via explanation methods

Reference 18

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Observation 8219757b-9b9d-4cbc-9462-7ed287655a2b · outbound

This paper cites ReferItGame: Referring to objects in pho- tographs of natural scenes.

Interpreting Object-level Foundation Models via Visual Precision Search ReferItGame: Referring to objects in pho- tographs of natural scenes

Reference 19

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Observation 14ee344d-da5e-4a0e-9ae8-cf446acc1398 · outbound

This paper cites BBAM: Bounding box attribution map for weakly super- vised semantic and instance segmentation.

Interpreting Object-level Foundation Models via Visual Precision Search BBAM: Bounding box attribution map for weakly super- vised semantic and instance segmentation

Reference 20

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Observation 0792dbad-2dc2-482c-b4fb-d3db2ce88366 · outbound

This paper cites Grounded language-image pre-training.

Interpreting Object-level Foundation Models via Visual Precision Search Grounded language-image pre-training

Reference 21

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Observation 5dc630d4-95fd-4cf3-a8eb-18adfd83941a · outbound

This paper cites Parallel rectangle flip attack: A query-based black-box attack against object detection.

Interpreting Object-level Foundation Models via Visual Precision Search Parallel rectangle flip attack: A query-based black-box attack against object detection

Reference 22

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Observation 69566c4b-5889-4f60-9de9-014e83fdb004 · outbound

This paper cites A large-scale multiple- objective method for black-box attack against object detec- tion.

Interpreting Object-level Foundation Models via Visual Precision Search A large-scale multiple- objective method for black-box attack against object detec- tion

Reference 23

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Observation 17aaf305-bb1d-4d41-abe9-0b0cf6f975a3 · outbound

This paper cites Imitated detectors: Stealing knowl- edge of black-box object detectors.

Interpreting Object-level Foundation Models via Visual Precision Search Imitated detectors: Stealing knowl- edge of black-box object detectors

Reference 24

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Observation e2d484b2-102d-4ae2-8fb5-5d3b6f151777 · outbound

This paper cites Object Detectors in the Open Environment: Challenges, Solutions, and Outlook.

Interpreting Object-level Foundation Models via Visual Precision Search Object Detectors in the Open Environment: Challenges, Solutions, and Outlook

Reference 25

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Observation b81e6fd5-451f-4b07-b7e4-efba6b9a22ee · outbound

This paper cites Microsoft COCO: Common objects in context.

Interpreting Object-level Foundation Models via Visual Precision Search Microsoft COCO: Common objects in context

Reference 26

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This paper cites {X-Adv}: Physical adversarial object attacks against x-ray prohibited item detection.

Interpreting Object-level Foundation Models via Visual Precision Search {X-Adv}: Physical adversarial object attacks against x-ray prohibited item detection

Reference 27

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Observation 49491f6e-8c9e-4c69-b36a-4f3cb40bec03 · outbound

This paper cites Referring expression generation and comprehension via attributes.

Interpreting Object-level Foundation Models via Visual Precision Search Referring expression generation and comprehension via attributes

Reference 28

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This paper cites Grounding DINO: Marrying dino with grounded pre-training for open-set object detection.

Interpreting Object-level Foundation Models via Visual Precision Search Grounding DINO: Marrying dino with grounded pre-training for open-set object detection

Reference 29

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Observation 0fda12b3-6a35-471e-9daa-2287c49d7d8b · outbound

This paper cites SSD: Single shot multibox detector.

Interpreting Object-level Foundation Models via Visual Precision Search SSD: Single shot multibox detector

Reference 30

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Observation 38450e04-0247-4198-931b-630ad4cea417 · outbound

This paper cites Evaluating merging strategies for sampling- based uncertainty techniques in object detection.

Interpreting Object-level Foundation Models via Visual Precision Search Evaluating merging strategies for sampling- based uncertainty techniques in object detection

Reference 31

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Observation faa303d6-ce4e-4172-992b-579d49082f0c · outbound

This paper cites Ex- plaining nonlinear classification decisions with deep taylor decomposition.

Interpreting Object-level Foundation Models via Visual Precision Search Ex- plaining nonlinear classification decisions with deep taylor decomposition

Reference 32

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Observation 42e1fd64-ecd7-4403-afb4-67cde77cf229 · outbound

This paper cites Making sense of dependence: Efficient black-box explanations using dependence measure.

Interpreting Object-level Foundation Models via Visual Precision Search Making sense of dependence: Efficient black-box explanations using dependence measure

Reference 33

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This paper cites RISE: Random- ized input sampling for explanation of black-box models.

Interpreting Object-level Foundation Models via Visual Precision Search RISE: Random- ized input sampling for explanation of black-box models

Reference 34

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Observation 199b5708-d9c2-4a35-9ccc-8183c0c6d418 · outbound

This paper cites Black-box explanation of object detectors via saliency maps.

Interpreting Object-level Foundation Models via Visual Precision Search Black-box explanation of object detectors via saliency maps

Reference 35

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Observation 6e72f900-e776-49a4-90ae-4f29cdb18767 · outbound

This paper cites Learn- ing transferable visual models from natural language super- vision.

Interpreting Object-level Foundation Models via Visual Precision Search Learn- ing transferable visual models from natural language super- vision

Reference 36

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Observation c8100e45-ee0a-4058-a986-40033fd2f93d · outbound

This paper cites YOLOv3: An Incremental Improvement.

Interpreting Object-level Foundation Models via Visual Precision Search YOLOv3: An Incremental Improvement

Reference 37

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Interpreting Object-level Foundation Models via Visual Precision Search Faster R-CNN: Towards real-time object detection with re- gion proposal networks

Reference 38

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This paper cites A unified approach to interpret- ing model predictions.

Interpreting Object-level Foundation Models via Visual Precision Search A unified approach to interpret- ing model predictions

Reference 39

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Observation 8ecf7d93-6fd6-4ec2-b4be-09d8f2676f9f · outbound

This paper cites Grad-CAM: visual explanations from deep networks via gradient-based localization.

Interpreting Object-level Foundation Models via Visual Precision Search Grad-CAM: visual explanations from deep networks via gradient-based localization

Reference 40

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This paper cites Informer-interpretability founded monitoring of medical im- age deep learning models.

Interpreting Object-level Foundation Models via Visual Precision Search Informer-interpretability founded monitoring of medical im- age deep learning models

Reference 41

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This paper cites Thirdeye: Attention maps for safe au- tonomous driving systems.

Interpreting Object-level Foundation Models via Visual Precision Search Thirdeye: Attention maps for safe au- tonomous driving systems

Reference 42

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This paper cites Axiomatic attribution for deep networks.

Interpreting Object-level Foundation Models via Visual Precision Search Axiomatic attribution for deep networks

Reference 43

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This paper cites FCOS: A simple and strong anchor-free object detector.

Interpreting Object-level Foundation Models via Visual Precision Search FCOS: A simple and strong anchor-free object detector

Reference 44

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Observation d2debb3e-65e3-4463-8a95-720de8d87038 · outbound

This paper cites Attention is all you need.

Interpreting Object-level Foundation Models via Visual Precision Search Attention is all you need

Reference 45

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Observation 87e17fc9-c8b3-4c95-8dd7-02230e1d1fbe · outbound

This paper cites YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors.

Interpreting Object-level Foundation Models via Visual Precision Search YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

Reference 46

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Observation b9219489-630a-44a2-83ac-a843e6ba708a · outbound

This paper cites Score-cam: Score-weighted visual explanations for convolutional neural networks.

Interpreting Object-level Foundation Models via Visual Precision Search Score-cam: Score-weighted visual explanations for convolutional neural networks

Reference 47

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Observation 568b186f-c0fa-4c37-904e-8d527ef4ba66 · outbound

This paper cites Transferable adversarial attacks for image and video object detection.

Interpreting Object-level Foundation Models via Visual Precision Search Transferable adversarial attacks for image and video object detection

Reference 48

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This paper cites On the road with gpt-4v (ision): Explorations of utilizing visual-language model as autonomous driving agent.

Interpreting Object-level Foundation Models via Visual Precision Search On the road with gpt-4v (ision): Explorations of utilizing visual-language model as autonomous driving agent

Reference 49

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This paper cites Safe: Sensitivity-aware fea- tures for out-of-distribution object detection.

Interpreting Object-level Foundation Models via Visual Precision Search Safe: Sensitivity-aware fea- tures for out-of-distribution object detection

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This paper cites General object foundation model for images and videos at scale.

Interpreting Object-level Foundation Models via Visual Precision Search General object foundation model for images and videos at scale

Reference 51

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This paper cites Towards open vocabulary learning: A survey.

Interpreting Object-level Foundation Models via Visual Precision Search Towards open vocabulary learning: A survey

Reference 52

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This paper cites Florence-2: Advancing a unified representation for a variety of vision tasks.

Interpreting Object-level Foundation Models via Visual Precision Search Florence-2: Advancing a unified representation for a variety of vision tasks

Reference 53

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This paper cites Spatial Sensitive Grad-CAM++: Im- proved visual explanation for object detectors via weighted combination of gradient map.

Interpreting Object-level Foundation Models via Visual Precision Search Spatial Sensitive Grad-CAM++: Im- proved visual explanation for object detectors via weighted combination of gradient map

Reference 54

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This paper cites Explaining Object Detectors via Collective Contribution of Pixels.

Interpreting Object-level Foundation Models via Visual Precision Search Explaining Object Detectors via Collective Contribution of Pixels

Reference 55

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This paper cites Detclipv3: To- wards versatile generative open-vocabulary object detection.

Interpreting Object-level Foundation Models via Visual Precision Search Detclipv3: To- wards versatile generative open-vocabulary object detection

Reference 56

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This paper cites Contextual object detection with mul- timodal large language models.

Interpreting Object-level Foundation Models via Visual Precision Search Contextual object detection with mul- timodal large language models

Reference 57

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Interpreting Object-level Foundation Models via Visual Precision Search Top-down neural attention by excitation backprop

Reference 58

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This paper cites Gradient-based instance-specific visual explanations for ob- ject specification and object discrimination.IEEE Trans.

Interpreting Object-level Foundation Models via Visual Precision Search Gradient-based instance-specific visual explanations for ob- ject specification and object discrimination.IEEE Trans

Reference 59

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Interpreting Object-level Foundation Models via Visual Precision Search Gradient-based visual explanation for transformer-based clip

Reference 60

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This paper cites Generalized decoding for pixel, image, and lan- guage.

Interpreting Object-level Foundation Models via Visual Precision Search Generalized decoding for pixel, image, and lan- guage

Reference 61

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This paper cites Object detection in 20 years: A survey.Proceed- ings of the IEEE, 111(3):257–276, 2023.

Interpreting Object-level Foundation Models via Visual Precision Search Object detection in 20 years: A survey.Proceed- ings of the IEEE, 111(3):257–276, 2023

Reference 62

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Pith citing papers

Observation 5b29df90-fbff-41b0-850f-4e89c13f9161 · inbound

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection cites this paper.

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection Interpreting Object-level Foundation Models via Visual Precision Search

Reference 11

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