DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models | Paper Citation Record

Observation a7fa3421-7e9e-4cd0-ad68-5d7b237a90b2 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:38.156625Z

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Observation b4ecf9c9-bc7b-4d08-8601-fb38be14cdfb · outbound

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

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 2

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Observation bb8a39e6-f4c2-4445-97de-695c4cf08d6d · outbound

This paper cites The Llama 3 Herd of Models.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models The Llama 3 Herd of Models

Reference 3

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Observation 4eb85102-92d4-4566-b328-cb88248aed08 · outbound

This paper cites Qwen Technical Report.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Qwen Technical Report

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:38.493697Z

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Observation d29c3809-a7bd-40d4-a555-ef565faded41 · outbound

This paper cites Qwen2.5 Technical Report.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Qwen2.5 Technical Report

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:38.629796Z

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Observation 33e50349-0dd1-4cca-b99c-0339394e082a · outbound

This paper cites DeepSeek LLM: Scaling Open-Source Language Models with Longtermism.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:38.746391Z

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Observation a067b816-b85d-497d-8084-7155d5c6def3 · outbound

This paper cites DeepSeek-VL: Towards Real-World Vision-Language Understanding.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models DeepSeek-VL: Towards Real-World Vision-Language Understanding

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:38.859878Z

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Observation cb988395-0cfc-42b3-9717-fa3fc90fd8fd · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:38.902531Z

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Observation e41cc792-baac-424c-a1fc-20f44a7e3636 · outbound

This paper cites DeepSeek-V3 Technical Report.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models DeepSeek-V3 Technical Report

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:38.970665Z

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Observation 0050adac-68c7-4a78-ad03-b15c7ce21f64 · outbound

This paper cites Visual instruction tuning.Advances in neural information processing systems, 36:34892–34916, 2023.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Visual instruction tuning.Advances in neural information processing systems, 36:34892–34916, 2023

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:39.038626Z

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Observation b9a6c6ab-b501-420e-8a4f-927aaaa92e05 · outbound

This paper cites Segment anything.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Segment anything

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:39.094947Z

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Observation e0991557-f1d0-44cd-899e-fa00cafaf8d3 · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models SAM 2: Segment Anything in Images and Videos

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:39.167283Z

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Observation 5c9bfed1-f09b-4e01-8ef0-5f4d300276f4 · outbound

This paper cites DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:39.233737Z

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Observation 66406ad9-3fa3-4b00-9c68-4f52956ca41d · outbound

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

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Grounding dino: Marrying dino with grounded pre-training for open-set object detection

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:39.298980Z

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Observation 7f08e0fa-4254-4761-b8a1-c7ffb6ac8e98 · outbound

This paper cites DINO-X: A Unified Vision Model for Open-World Object Detection and Understanding.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models DINO-X: A Unified Vision Model for Open-World Object Detection and Understanding

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:39.392644Z

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Observation c05c271e-7514-4b73-874f-6de89913f62b · outbound

This paper cites Visual in-context prompting.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Visual in-context prompting

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:46.051518Z

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Observation 9e1ad738-9420-425b-a973-25c6ab61b52d · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 17

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Observation 704b60cf-de98-437a-8e4d-430091d07f43 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Imagenet: A large-scale hierarchical image database

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:39.690023Z

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Observation 662ab28b-6bef-4529-b622-eed32a677db4 · outbound

This paper cites Microsoft coco: Common objects in context.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Microsoft coco: Common objects in context

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:39.743576Z

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Observation a4030498-18b2-404f-801d-ebf925e00178 · outbound

This paper cites Objects365: A large-scale, high-quality dataset for object detection.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Objects365: A large-scale, high-quality dataset for object detection

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:39.812688Z

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Observation ae087a71-c295-45c0-b2f2-1221703ca4cd · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models DINOv2: Learning Robust Visual Features without Supervision

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:39.891022Z

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Observation 5f9ef87f-503d-4289-b3e4-5f2af612fbb1 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models A simple framework for contrastive learning of visual representations

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:39.964947Z

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Observation b477893c-00b4-4249-93b3-d4d253260570 · outbound

This paper cites G-simclr: Self-supervised contrastive learning with guided projection via pseudo labelling.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models G-simclr: Self-supervised contrastive learning with guided projection via pseudo labelling

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:45.911668Z

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Observation d06069fb-48f5-4f1e-ab3f-1f9a3a56e64b · outbound

This paper cites T-Rex: Counting by Visual Prompting.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models T-Rex: Counting by Visual Prompting

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:40.179541Z

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Observation 73714e8e-8a42-401a-9e20-194a605f915e · outbound

This paper cites T-rex2: Towards generic object detection via text-visual prompt synergy.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models T-rex2: Towards generic object detection via text-visual prompt synergy

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:45.840820Z

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

Observation 9f2710a2-c5df-456b-9f9d-9ec81e47460f · outbound

This paper cites Cp-detr: Concept prompt guide detr toward stronger universal object detection.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Cp-detr: Concept prompt guide detr toward stronger universal object detection

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:45.729040Z

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

Observation 4a7b5899-47f8-49a3-81a4-9ccbbd8385d4 · outbound

This paper cites AutoVP: An Automated Visual Prompting Framework and Benchmark.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models AutoVP: An Automated Visual Prompting Framework and Benchmark

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:40.486017Z

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Observation a2913642-ecaf-4e01-92af-966cba330f9e · outbound

This paper cites Moka: Open-vocabulary robotic manipulation through mark-based visual prompting.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Moka: Open-vocabulary robotic manipulation through mark-based visual prompting

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:40.605490Z

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Observation d1b9627f-cdc5-49b7-b38f-e8d6f1209201 · outbound

This paper cites Multimodal prompt perceiver: Empower adaptiveness generalizability and fidelity for all-in-one image restoration.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Multimodal prompt perceiver: Empower adaptiveness generalizability and fidelity for all-in-one image restoration

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:45.601553Z

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

Observation af1f007e-283e-4536-b106-f94e0420e065 · outbound

This paper cites PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:40.833021Z

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Observation 2ef018e4-7d7d-4683-a8c9-2f0605bf3126 · outbound

This paper cites Prompt engineering for zero-shot and few-shot defect detection and classification using a visual-language pretrained model.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Prompt engineering for zero-shot and few-shot defect detection and classification using a visual-language pretrained model

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:45.422370Z

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Observation 8a1757fb-0847-4a57-b776-379e6a3ef43b · outbound

This paper cites Promptcharm: Text-to-image generation through multi-modal prompting and refinement.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Promptcharm: Text-to-image generation through multi-modal prompting and refinement

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:45.316318Z

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Observation f161193d-fc2f-4a2c-aea5-812bfbab78d8 · outbound

This paper cites Prompting industrial anomaly segment with large vision-language models.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Prompting industrial anomaly segment with large vision-language models

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:45.217572Z

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

Observation a40d6278-33db-482d-a494-cb1094d91504 · outbound

This paper cites Visual Prompting in Multimodal Large Language Models: A Survey.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Visual Prompting in Multimodal Large Language Models: A Survey

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:41.257873Z

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Observation 2b52cc8c-8fb5-46f8-8fb8-5630793c7e8c · outbound

This paper cites Understanding and improving visual prompting: A label-mapping perspective.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Understanding and improving visual prompting: A label-mapping perspective

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:45.134917Z

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

Observation 011313bf-cbfa-42f9-ab5a-d3ee850c0512 · outbound

This paper cites Vp3d: Unleashing 2d visual prompt for text-to-3d generation.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Vp3d: Unleashing 2d visual prompt for text-to-3d generation

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:44.952477Z

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

Observation b35e5bdd-f5c5-40c4-907c-40339020a79f · outbound

This paper cites Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:41.549563Z

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Observation 1efe85d8-c42e-4f8a-a403-3af50ae9eedf · outbound

This paper cites An Open and Comprehensive Pipeline for Unified Object Grounding and Detection.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models An Open and Comprehensive Pipeline for Unified Object Grounding and Detection

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:41.692788Z

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Observation ee2b72e2-a38b-4cb5-8332-77c425c04928 · outbound

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

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Learning to prompt for open- vocabulary object detection with vision-language model

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:44.738724Z

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

Observation 3660d5f6-6942-4fb8-b902-dd8dce926aff · outbound

This paper cites Proximal Policy Optimization Algorithms.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Proximal Policy Optimization Algorithms

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:41.874245Z

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Observation d96daeee-c489-4fa0-b0b0-53c03e340c5f · outbound

This paper cites Training language models to follow instructions with human feedback.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Training language models to follow instructions with human feedback

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:41.985819Z

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Observation 187d8867-3352-42a4-8935-d9094727e887 · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Direct preference optimization: Your language model is secretly a reward model

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:42.119299Z

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Observation dd638852-dbbf-4862-ae9d-ad64d68c96b5 · outbound

This paper cites Learning transferable visual models from natural language supervision.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Learning transferable visual models from natural language supervision

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:42.236006Z

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Observation 5d3c849b-b1d9-4e16-a256-ce0158fff4bb · outbound

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

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Florence: A New Foundation Model for Computer Vision

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:42.302476Z

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Observation be4cbdb3-a54b-421d-bb99-570a88d2ebf7 · outbound

This paper cites Grounded language-image pre-training.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Grounded language-image pre-training

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:42.362469Z

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Observation 3f019d7f-0cb2-411a-8a18-fd4273d538d0 · outbound

This paper cites Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:42.418637Z

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Observation 4e5b519a-8bbb-4317-ac31-80c6cddfb1cc · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Swin transformer: Hierarchical vision transformer using shifted windows

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:42.491065Z

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Observation 3e583b6b-35c1-4fc9-a44a-a84b6bbd4f35 · outbound

This paper cites VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:42.554071Z

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Observation 0325b4b6-57f1-4fb1-b1e2-5125694fc982 · outbound

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

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Yolo-world: Real- time open-vocabulary object detection

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:44.581367Z

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 beab8506-1c34-412b-8baa-9a6374c7213c · outbound

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

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models End-to-end object detection with transformers

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:44.490030Z

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 ce2b969e-c6cb-44f4-9c79-594a261c9621 · outbound

This paper cites Dn-detr: Accelerate detr training by introducing query denoising.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Dn-detr: Accelerate detr training by introducing query denoising

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:44.346485Z

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 bab7b3c6-915c-4f98-ab1c-60e8a3c80c80 · outbound

This paper cites DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:42.812679Z

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Observation e6b29265-bd35-49d5-9570-0c7116e77fe6 · outbound

This paper cites Open-vocabulary detr with conditional matching.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Open-vocabulary detr with conditional matching

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:44.203328Z

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 8ee742f9-e387-402d-9859-1ce46891a8d3 · outbound

This paper cites Open-vocabulary object detection using captions.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Open-vocabulary object detection using captions

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:43:43.935458Z

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 6c86923d-74a5-40fd-8a62-21503d1aa8c2 · outbound

This paper cites Open-vocabulary Object Detection via Vision and Language Knowledge Distillation.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Open-vocabulary Object Detection via Vision and Language Knowledge Distillation

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T12:43:42.949925Z

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Observation 0534f1ae-8262-4bb0-b7cb-0844e041a973 · outbound

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

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Open- vocabulary panoptic segmentation with text-to-image diffusion models

Reference 56

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This paper cites Bert: Pre-training of deep bidi- rectional transformers for language understanding.

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Bert: Pre-training of deep bidi- rectional transformers for language understanding

Reference 57

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Observation 7126fe7f-fbd2-4ecc-9a7a-d89e7ebb504e · outbound

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

DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models Lvis: A dataset for large vocabulary instance segmentation

Reference 58

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