Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding | Paper Citation Record

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This paper cites Flamingo: a visual language model for few-shot learning.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Flamingo: a visual language model for few-shot learning

Reference 1

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Observation 9f9893fc-7d8e-44f3-9f3e-2cded8986c0f · outbound

This paper cites Detector-free weakly supervised grounding by separation.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Detector-free weakly supervised grounding by separation

Reference 2

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This paper cites Exploring Visual Prompts for Adapting Large-Scale Models.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Exploring Visual Prompts for Adapting Large-Scale Models

Reference 3

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This paper cites Learning to exploit temporal structure for biomedical vision-language processing.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Learning to exploit temporal structure for biomedical vision-language processing

Reference 4

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This paper cites Im- proving pneumonia localization via cross-attention on med- ical images and reports.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Im- proving pneumonia localization via cross-attention on med- ical images and reports

Reference 5

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This paper cites Making the most of text semantics to improve biomedical vision–language processing.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Making the most of text semantics to improve biomedical vision–language processing

Reference 6

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

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Coco- stuff: Thing and stuff classes in context

Reference 7

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Observation a9391a63-6744-417f-877a-50b342985715 · outbound

This paper cites Generic attention- model explainability for interpreting bi-modal and encoder- decoder transformers.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Generic attention- model explainability for interpreting bi-modal and encoder- decoder transformers

Reference 8

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Observation 67be6692-4e7d-4415-aa9a-e7e907ac3d6e · outbound

This paper cites Knowledge aided consistency for weakly supervised phrase grounding.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Knowledge aided consistency for weakly supervised phrase grounding

Reference 9

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This paper cites Querymatch: A query-based contrastive learning framework for weakly supervised visual grounding.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Querymatch: A query-based contrastive learning framework for weakly supervised visual grounding

Reference 10

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This paper cites Medical phrase ground- ing with region-phrase context contrastive alignment.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Medical phrase ground- ing with region-phrase context contrastive alignment

Reference 11

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Observation 39d4635b-8722-4f76-981a-b727d611bb67 · outbound

This paper cites A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretation.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretation

Reference 12

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This paper cites Randaugment: Practical automated data augmen- tation with a reduced search space.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Randaugment: Practical automated data augmen- tation with a reduced search space

Reference 13

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This paper cites Align2ground: Weakly supervised phrase grounding guided by image-caption align- ment.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Align2ground: Weakly supervised phrase grounding guided by image-caption align- ment

Reference 14

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This paper cites Transvg: End-to-end visual ground- ing with transformers.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Transvg: End-to-end visual ground- ing with transformers

Reference 15

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This paper cites Chest imaging representing a covid-19 positive rural us population.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Chest imaging representing a covid-19 positive rural us population

Reference 16

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This paper cites Adam: A method for stochastic opti- mization.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Adam: A method for stochastic opti- mization

Reference 17

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This paper cites Neural se- quential phrase grounding (seqground).

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Neural se- quential phrase grounding (seqground)

Reference 18

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This paper cites Learning to prompt for open-vocabulary ob- ject detection with vision-language model.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Learning to prompt for open-vocabulary ob- ject detection with vision-language model

Reference 19

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This paper cites Medrax: Medical reasoning agent for chest x-ray, 2025.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Medrax: Medical reasoning agent for chest x-ray, 2025

Reference 20

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This paper cites Contrastive learning for weakly supervised phrase grounding.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Contrastive learning for weakly supervised phrase grounding

Reference 21

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This paper cites Parameter-Efficient Fine-Tuning Medical Multimodal Large Language Models for Medical Visual Grounding.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Parameter-Efficient Fine-Tuning Medical Multimodal Large Language Models for Medical Visual Grounding

Reference 22

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This paper cites Improved visual ground- ing through self-consistent explanations.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Improved visual ground- ing through self-consistent explanations

Reference 23

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This paper cites Multi-grained attention with object-level ground- ing for visual question answering.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Multi-grained attention with object-level ground- ing for visual question answering

Reference 24

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This paper cites Verjans, Minh-Son To, and Vu Minh Hieu Phan.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Verjans, Minh-Son To, and Vu Minh Hieu Phan

Reference 25

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This paper cites Visual grounding of whole radiology reports for 3d ct images.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Visual grounding of whole radiology reports for 3d ct images

Reference 26

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This paper cites Rad- graph: Extracting clinical entities and relations from radiol- ogy reports.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Rad- graph: Extracting clinical entities and relations from radiol- ogy reports

Reference 27

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This paper cites Scaling up visual and vision-language representa- tion learning with noisy text supervision.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Scaling up visual and vision-language representa- tion learning with noisy text supervision

Reference 28

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This paper cites Vi- sual prompt tuning.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Vi- sual prompt tuning

Reference 29

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This paper cites Pseudo-q: Generating pseudo language queries for visual grounding.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Pseudo-q: Generating pseudo language queries for visual grounding

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This paper cites Refclip: A universal teacher for weakly supervised referring expression comprehension.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Refclip: A universal teacher for weakly supervised referring expression comprehension

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This paper cites Mimic-cxr, a de- identified publicly available database of chest radiographs with free-text reports.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Mimic-cxr, a de- identified publicly available database of chest radiographs with free-text reports

Reference 32

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This paper cites Deep visual-semantic align- ments for generating image descriptions.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Deep visual-semantic align- ments for generating image descriptions

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This paper cites Maple: Multi-modal prompt learning.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Maple: Multi-modal prompt learning

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Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Covlm: Compos- ing visual entities and relationships in large language models via communicative decoding

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Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Visual prompt tuning for weakly supervised phrase grounding

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This paper cites Adaptive recon- struction network for weakly supervised referring expression grounding.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Adaptive recon- struction network for weakly supervised referring expression grounding

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This paper cites Knowledge-guided pairwise recon- struction network for weakly supervised referring expression grounding.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Knowledge-guided pairwise recon- struction network for weakly supervised referring expression grounding

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This paper cites Relation- aware instance refinement for weakly supervised visual grounding.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Relation- aware instance refinement for weakly supervised visual grounding

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This paper cites Confidence-aware pseudo-label learning for weakly super- vised visual grounding.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Confidence-aware pseudo-label learning for weakly super- vised visual grounding

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This paper cites VividMed: Vision Language Model with Versatile Visual Grounding for Medicine.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding VividMed: Vision Language Model with Versatile Visual Grounding for Medicine

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This paper cites Localizing be- fore answering: A hallucination evaluation benchmark for grounded medical multimodal llms.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Localizing be- fore answering: A hallucination evaluation benchmark for grounded medical multimodal llms

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This paper cites Gpt-4 technical report, 2023.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Gpt-4 technical report, 2023

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This paper cites Decomposing disease de- scriptions for enhanced pathology detection: A multi-aspect vision-language pre-training framework.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Decomposing disease de- scriptions for enhanced pathology detection: A multi-aspect vision-language pre-training framework

Reference 44

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

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Learn- ing transferable visual models from natural language super- vision

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This paper cites Grounding of textual phrases in images by reconstruction.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Grounding of textual phrases in images by reconstruction

Reference 46

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This paper cites Similarity maps for self- training weakly-supervised phrase grounding.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Similarity maps for self- training weakly-supervised phrase grounding

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Observation 690d67a2-a5e3-4e85-9615-04bbfad034cb · outbound

This paper cites What is where by looking: Weakly-supervised open-world phrase- grounding without text inputs.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding What is where by looking: Weakly-supervised open-world phrase- grounding without text inputs

Reference 48

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Observation e5ce2b65-5e96-475b-b497-1113b6c1d868 · outbound

This paper cites Augmenting the national institutes of health chest radiograph dataset with expert annotations of possi- ble pneumonia.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Augmenting the national institutes of health chest radiograph dataset with expert annotations of possi- ble pneumonia

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:25.156053Z

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Observation e119af83-37ae-4cba-bc26-9d901ecd8ca6 · outbound

This paper cites Interactive Visual Grounding of Referring Expressions for Human-Robot Interaction.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Interactive Visual Grounding of Referring Expressions for Human-Robot Interaction

Reference 50

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This paper cites What does clip know about a red circle? vi- sual prompt engineering for vlms.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding What does clip know about a red circle? vi- sual prompt engineering for vlms

Reference 51

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This paper cites Discriminative triad matching and reconstruc- tion for weakly referring expression grounding.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Discriminative triad matching and reconstruc- tion for weakly referring expression grounding

Reference 52

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This paper cites Expert-level detection of pathologies from unannotated chest x-ray images via self- supervised learning.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Expert-level detection of pathologies from unannotated chest x-ray images via self- supervised learning

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:25.094777Z

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This paper cites Few-shot visual grounding for natural human-robot interaction.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Few-shot visual grounding for natural human-robot interaction

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:25.073288Z

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This paper cites Found a reason for me? weakly-supervised grounded visual question answering us- ing capsules.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Found a reason for me? weakly-supervised grounded visual question answering us- ing capsules

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:25.054302Z

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This paper cites A framework for interpretability in machine learn- ing for medical imaging.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding A framework for interpretability in machine learn- ing for medical imaging

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:25.028400Z

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This paper cites Phrase localization without paired training examples.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Phrase localization without paired training examples

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:25.007002Z

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Observation 37c9d7c2-2ae0-4e50-a5a8-59a3c52936ab · outbound

This paper cites Improving weakly supervised visual ground- ing by contrastive knowledge distillation.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Improving weakly supervised visual ground- ing by contrastive knowledge distillation

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:24.989415Z

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This paper cites Learning Visual Grounding from Generative Vision and Language Model.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Learning Visual Grounding from Generative Vision and Language Model

Reference 59

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This paper cites Medclip: Contrastive learning from unpaired medical images and text.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Medclip: Contrastive learning from unpaired medical images and text

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:24.968008Z

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Observation 02e86381-a97b-45f9-b934-ef04de9e313c · outbound

This paper cites MedKLIP: Medical knowledge enhanced language-image pre-training.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding MedKLIP: Medical knowledge enhanced language-image pre-training

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:24.944750Z

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Observation 33e89b3a-656d-4701-8a95-ac7aedea0b84 · outbound

This paper cites Rethinking masked image modelling for medical image representation.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Rethinking masked image modelling for medical image representation

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:24.918857Z

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This paper cites A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language Model.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language Model

Reference 63

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Observation 5e8a2ed7-223d-47bd-ad3b-74c5efaefb69 · outbound

This paper cites Florence: A New Foundation Model for Computer Vision.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Florence: A New Foundation Model for Computer Vision

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T15:27:24.456585Z

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This paper cites BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs

Reference 65

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This paper cites Conditional prompt learning for vision-language mod- els.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Conditional prompt learning for vision-language mod- els

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T15:27:24.468858Z

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Observation eab34838-7f13-4283-a369-cd5e76acf9e7 · outbound

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

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Learning to prompt for vision-language models

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-07T15:27:24.474354Z

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Observation 5b435cc1-5157-4ef7-affe-9758d5f16013 · outbound

This paper cites Scanreason: Empowering 3d visual grounding with reasoning capabilities.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Scanreason: Empowering 3d visual grounding with reasoning capabilities

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:27:24.870915Z

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Observation b85f18c6-6d44-4ca3-b5ba-e7ff031059ef · outbound

This paper cites Read before grounding: Scene knowledge visual ground- ing via multi-step parsing.

Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Read before grounding: Scene knowledge visual ground- ing via multi-step parsing

Reference 69

Resolution: verified exact; raw_fallback, observed 2026-08-07T15:27:24.680975Z

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Observation 82e14d23-428d-480b-bc92-1df061a0972d · outbound

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Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding Unresolved cited work

Reference 2021

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