Advancing Visual Large Language Model for Multi-granular Versatile Perception | Paper Citation Record

Observation 661f57e2-4e32-438c-b154-cecc90c08493 · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 1

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Observation a6ac2b05-fc2e-46be-ae01-43ad91b33401 · outbound

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

Advancing Visual Large Language Model for Multi-granular Versatile Perception End-to- end object detection with transformers

Reference 2

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Observation 31b4a95e-7c6f-425c-b80d-b069b98b2a3c · outbound

This paper cites See-through-text grouping for referring image segmentation.

Advancing Visual Large Language Model for Multi-granular Versatile Perception See-through-text grouping for referring image segmentation

Reference 3

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Observation 47df1633-dd0f-4905-95a1-a9e790ebee79 · outbound

This paper cites Hybrid task cascade for instance seg- mentation.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Hybrid task cascade for instance seg- mentation

Reference 4

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Observation 7f727e24-feac-491c-87f6-c04008f95c50 · outbound

This paper cites Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic

Reference 5

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Observation 63d09218-62d6-4d70-9ae5-a041c6419513 · outbound

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

Advancing Visual Large Language Model for Multi-granular Versatile Perception Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks

Reference 6

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Observation bd29b6f5-3711-4d88-99ad-f6bbd9fa9895 · outbound

This paper cites Masked-attention mask transformer for universal image segmentation.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Masked-attention mask transformer for universal image segmentation

Reference 7

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Observation ab19eb58-68c8-41ff-8140-995f6a9f6a23 · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding.

Advancing Visual Large Language Model for Multi-granular Versatile Perception The cityscapes dataset for semantic urban scene understanding

Reference 8

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Observation 898316a0-dea6-4eea-9633-15ea60828160 · outbound

This paper cites Phraseclick: toward achieving flexible interactive segmenta- tion by phrase and click.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Phraseclick: toward achieving flexible interactive segmenta- tion by phrase and click

Reference 9

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Observation b1f8d708-1c0e-4e9b-9ad4-979bbb24ecc8 · outbound

This paper cites Vision-language transformer and query generation for refer- ring segmentation.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Vision-language transformer and query generation for refer- ring segmentation

Reference 10

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Observation 83ff79e8-4352-442e-be00-2935499567f7 · outbound

This paper cites Open- vocabulary universal image segmentation with maskclip.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Open- vocabulary universal image segmentation with maskclip

Reference 11

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Observation 1c7f3ab4-afa8-4929-a9b5-0f808f00106e · outbound

This paper cites The pascal visual object classes (voc) challenge.

Advancing Visual Large Language Model for Multi-granular Versatile Perception The pascal visual object classes (voc) challenge

Reference 12

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Observation 7bcd79f2-9a5a-4a80-97f0-ca091d410fc5 · outbound

This paper cites Scott, and Weilin Huang.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Scott, and Weilin Huang

Reference 13

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Observation 663cab1d-8cc8-4886-9358-6fe0e6212210 · outbound

This paper cites Prompt- det: Towards open-vocabulary detection using uncurated im- ages.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Prompt- det: Towards open-vocabulary detection using uncurated im- ages

Reference 14

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Observation 44550596-7f95-4528-9499-cfb82c36b281 · outbound

This paper cites Instagen: Enhancing object detection by training on syn- thetic dataset.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Instagen: Enhancing object detection by training on syn- thetic dataset

Reference 15

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Observation d86fcecc-3ec4-41d0-83dd-71f50e25360e · outbound

This paper cites Video-r1: Reinforcing video reasoning in mllms, 2025.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Video-r1: Reinforcing video reasoning in mllms, 2025

Reference 16

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Observation 76ea7b3a-3f72-4747-b11b-86cc895129d4 · outbound

This paper cites Frozen-detr: Enhancing detr with image understanding from frozen foundation models.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Frozen-detr: Enhancing detr with image understanding from frozen foundation models

Reference 17

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Observation 4a16c1d6-ff58-4e98-9d1d-9379d7b4e173 · outbound

This paper cites Open-vocabulary object detection via vision and language knowledge distillation.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Open-vocabulary object detection via vision and language knowledge distillation

Reference 18

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Observation 99d82295-8421-4552-897d-37e54cda6251 · outbound

This paper cites Dataseg: Taming a universal multi-dataset multi-task segmentation model.Ad- vances in Neural Information Processing Systems, 36, 2024.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Dataseg: Taming a universal multi-dataset multi-task segmentation model.Ad- vances in Neural Information Processing Systems, 36, 2024

Reference 19

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Observation 6d5f152c-c330-4b77-a032-e1b528425c83 · outbound

This paper cites Textbooks Are All You Need.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Textbooks Are All You Need

Reference 20

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Observation 7a0f9581-8649-42bc-a3c2-150d50c2c733 · outbound

This paper cites Llava-uhd: An lmm perceiving any aspect ratio and high- resolution images.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Llava-uhd: An lmm perceiving any aspect ratio and high- resolution images

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:25:03.825303Z

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Observation d7750ed9-fcd1-4a89-b2fe-202c81e2919c · outbound

This paper cites Open-vocabulary semantic segmentation with decou- pled one-pass network.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Open-vocabulary semantic segmentation with decou- pled one-pass network

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:25:03.817355Z

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Observation 4b126936-388e-4bcb-9fb3-ce6583a596a5 · outbound

This paper cites Mask r-cnn.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Mask r-cnn

Reference 23

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Observation 532f1273-a3a6-4c33-8345-f58a67eb9341 · outbound

This paper cites Bi-directional relationship inferring net- work for referring image segmentation.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Bi-directional relationship inferring net- work for referring image segmentation

Reference 24

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Observation f170f6c4-87ad-442b-9138-18c255b56c3c · outbound

This paper cites Densely connected parameter- efficient tuning for referring image segmentation, 2025.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Densely connected parameter- efficient tuning for referring image segmentation, 2025

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:25:03.797166Z

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Observation 8937b392-bcdb-4806-998f-86ff53dc2c35 · outbound

This paper cites Referring im- age segmentation via cross-modal progressive comprehen- sion.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Referring im- age segmentation via cross-modal progressive comprehen- sion

Reference 26

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Observation 4af64b3f-61c2-4889-81eb-fa0ee31fc2fe · outbound

This paper cites Linguistic structure guided context modeling for referring image segmentation.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Linguistic structure guided context modeling for referring image segmentation

Reference 27

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Observation 742758de-c6f1-475c-9ffa-76cb73bafa49 · outbound

This paper cites Oneformer: One transformer to rule universal image segmentation.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Oneformer: One transformer to rule universal image segmentation

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:25:03.774039Z

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Observation 31ef1670-998f-420a-ba23-576d05d84415 · outbound

This paper cites Mdetr- modulated detection for end-to-end multi-modal understand- ing.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Mdetr- modulated detection for end-to-end multi-modal understand- ing

Reference 29

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Observation d53df0f2-aa30-40d1-9b56-9037c47276d8 · outbound

This paper cites A spoken language dataset of descrip- tions for speech-based grounded language learning.

Advancing Visual Large Language Model for Multi-granular Versatile Perception A spoken language dataset of descrip- tions for speech-based grounded language learning

Reference 30

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Observation 0b4fbcd1-5e82-4d56-8527-7aebafa74693 · outbound

This paper cites F-vlm: Open-vocabulary object detection upon frozen vision and language models.

Advancing Visual Large Language Model for Multi-granular Versatile Perception F-vlm: Open-vocabulary object detection upon frozen vision and language models

Reference 31

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Observation 8230abef-3c1f-45fa-a6dd-28c842cf370b · outbound

This paper cites LISA: Reasoning Segmentation via Large Language Model.

Advancing Visual Large Language Model for Multi-granular Versatile Perception LISA: Reasoning Segmentation via Large Language Model

Reference 32

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This paper cites Lisa: Reasoning segmentation via large language model.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Lisa: Reasoning segmentation via large language model

Reference 33

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Observation 0c07afee-1d4f-4160-ae0d-529ef1a26bd5 · outbound

This paper cites Discobox: Weakly supervised instance segmentation and semantic correspondence from box super- vision.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Discobox: Weakly supervised instance segmentation and semantic correspondence from box super- vision

Reference 34

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Observation 63fea573-a68e-4e5b-8d5d-f9b7e661d10f · outbound

This paper cites Vision Transformers Are Good Mask Auto-Labelers.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Vision Transformers Are Good Mask Auto-Labelers

Reference 35

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Observation b97777ef-b346-48bf-9c22-83ad347d63df · outbound

This paper cites Mask dino: Towards a unified transformer-based framework for object detection and segmentation.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Mask dino: Towards a unified transformer-based framework for object detection and segmentation

Reference 36

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Observation c765920b-5ec7-4141-bf16-ba1e6b80c835 · outbound

This paper cites Distilling detr with visual-linguistic knowledge for open-vocabulary object detection.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Distilling detr with visual-linguistic knowledge for open-vocabulary object detection

Reference 37

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Observation 5f13d64f-ef6b-4553-9b6b-3ee39ac0a1b7 · outbound

This paper cites Grounded language-image pre-training.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Grounded language-image pre-training

Reference 38

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Observation ee04b3ad-bf10-4c3d-9327-b6faafbf605d · outbound

This paper cites Box-supervised instance seg- mentation with level set evolution.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Box-supervised instance seg- mentation with level set evolution

Reference 39

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Observation 8aed0da2-f026-4012-b0c5-292b7ad09795 · outbound

This paper cites Fully convolutional networks for panoptic segmentation with point-based supervision.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Fully convolutional networks for panoptic segmentation with point-based supervision

Reference 40

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Observation 00a7a282-fc22-4e44-a7d1-25e6ddf1e20e · outbound

This paper cites A real-time cross-modality correlation fil- tering method for referring expression comprehension.

Advancing Visual Large Language Model for Multi-granular Versatile Perception A real-time cross-modality correlation fil- tering method for referring expression comprehension

Reference 41

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Observation 4a1657f9-3b3c-4191-a321-b25b7226597a · outbound

This paper cites Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll ´ar, and C.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll ´ar, and C

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:25:03.680245Z

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Observation 2651bf09-4a8b-45ec-9acb-82ad86de3712 · outbound

This paper cites Gres: Gener- alized referring expression segmentation.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Gres: Gener- alized referring expression segmentation

Reference 43

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This paper cites Visual instruction tuning.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Visual instruction tuning

Reference 44

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Observation 396ea93a-94bd-43c9-bcfe-5e7eadd99cda · outbound

This paper cites Poly- former: Referring image segmentation as sequential poly- gon generation.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Poly- former: Referring image segmentation as sequential poly- gon generation

Reference 45

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Observation 0b59ecd3-f4d4-4402-b6b1-8d487fe0098b · outbound

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

Advancing Visual Large Language Model for Multi-granular Versatile Perception Grounding dino: Marrying dino with grounded pre-training for open-set object detection

Reference 46

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Observation 4bc258ef-85e1-45bc-9f4e-ec3f03888c67 · outbound

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

Advancing Visual Large Language Model for Multi-granular Versatile Perception Knowledge-guided pairwise recon- struction network for weakly supervised referring expression grounding

Reference 47

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Observation b5080384-84b0-44e6-b417-7c5cd23d5ae8 · outbound

This paper cites Learn- ing cross-modal context graph for visual grounding.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Learn- ing cross-modal context graph for visual grounding

Reference 48

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Observation 11f99a97-e9db-4615-9b27-6c0bb48cc3bd · outbound

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

Advancing Visual Large Language Model for Multi-granular Versatile Perception Swin transformer: Hierarchical vision transformer using shifted windows

Reference 49

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Observation 90d19aa5-8690-4e28-99a6-18f1f3dec4e4 · outbound

This paper cites Visual- rft: Visual reinforcement fine-tuning, 2025.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Visual- rft: Visual reinforcement fine-tuning, 2025

Reference 50

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Observation e9b21f22-e97f-48cb-9b98-250a81250791 · outbound

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

Advancing Visual Large Language Model for Multi-granular Versatile Perception DeepSeek-VL: Towards Real-World Vision-Language Understanding

Reference 51

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Observation adf0279d-f848-4aa0-bb46-befbf3392261 · outbound

This paper cites Scaling open-vocabulary object detection.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Scaling open-vocabulary object detection

Reference 52

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Observation 01d20d78-7508-4559-a76f-22cb2c4321b9 · outbound

This paper cites The role of context for object detection and semantic segmentation in the wild.

Advancing Visual Large Language Model for Multi-granular Versatile Perception The role of context for object detection and semantic segmentation in the wild

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:25:03.600915Z

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Observation 50d46028-436f-4fc3-a959-83ce2362efd2 · outbound

This paper cites Mod- eling context between objects for referring expression under- standing.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Mod- eling context between objects for referring expression under- standing

Reference 55

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Observation c58cdb6b-764e-4a4f-bce1-383a37d9f6e5 · outbound

This paper cites Vision-aware text features in referring image segmentation: From object understanding to context understanding, 2024.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Vision-aware text features in referring image segmentation: From object understanding to context understanding, 2024

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:25:03.584054Z

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Observation 15adb626-08b6-4fb3-8aef-df41b9d5c851 · outbound

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Advancing Visual Large Language Model for Multi-granular Versatile Perception GLaMM: Pixel Grounding Large Multimodal Model

Reference 57

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Advancing Visual Large Language Model for Multi-granular Versatile Perception PixelLM: Pixel Reasoning with Large Multimodal Model

Reference 58

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This paper cites Pixellm: Pixel reasoning with large multimodal model.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Pixellm: Pixel reasoning with large multimodal model

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:25:03.574542Z

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Observation 13f2513a-d917-4376-bb03-c9e5c923fe19 · outbound

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

Advancing Visual Large Language Model for Multi-granular Versatile Perception Grounding of textual phrases in images by reconstruction

Reference 60

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

Advancing Visual Large Language Model for Multi-granular Versatile Perception Objects365: A large-scale, high-quality dataset for object detection

Reference 61

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Advancing Visual Large Language Model for Multi-granular Versatile Perception Unresolved cited work

Reference 62

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This paper cites Conceptual captions: A cleaned, hypernymed, im- age alt-text dataset for automatic image captioning.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Conceptual captions: A cleaned, hypernymed, im- age alt-text dataset for automatic image captioning

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:25:03.541868Z

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Advancing Visual Large Language Model for Multi-granular Versatile Perception Boxinst: High-performance instance segmentation with box annotations

Reference 64

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Advancing Visual Large Language Model for Multi-granular Versatile Perception Max-deeplab: End-to-end panoptic segmentation with mask transformers

Reference 65

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Advancing Visual Large Language Model for Multi-granular Versatile Perception OV-DINO: Unified Open-Vocabulary Detection with Language-Aware Selective Fusion

Reference 66

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This paper cites Neighbourhood watch: Refer- ring expression comprehension via language-guided graph attention networks.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Neighbourhood watch: Refer- ring expression comprehension via language-guided graph attention networks

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:25:03.518088Z

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Advancing Visual Large Language Model for Multi-granular Versatile Perception Time-R1: Post-Training Large Vision Language Model for Temporal Video Grounding

Reference 68

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Advancing Visual Large Language Model for Multi-granular Versatile Perception Cris: Clip- driven referring image segmentation

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:25:03.509852Z

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Advancing Visual Large Language Model for Multi-granular Versatile Perception LaSagnA: Language-based Segmentation Assistant for Complex Queries

Reference 70

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Advancing Visual Large Language Model for Multi-granular Versatile Perception Hyperseg: Hybrid segmentation assistant with fine-grained visual perceiver

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:25:03.501288Z

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Observation 02f804a7-f5e7-4b3a-ace8-42250d64c4e5 · outbound

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Advancing Visual Large Language Model for Multi-granular Versatile Perception Chain-of-thought prompting elicits reasoning in large lan- guage models

Reference 72

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This paper cites Aligning bag of regions for open- vocabulary object detection.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Aligning bag of regions for open- vocabulary object detection

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:25:03.487596Z

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Advancing Visual Large Language Model for Multi-granular Versatile Perception GSVA: Generalized Segmentation via Multimodal Large Language Models

Reference 74

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Advancing Visual Large Language Model for Multi-granular Versatile Perception Upsnet: A unified panoptic segmentation network

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:25:03.480035Z

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This paper cites A simple baseline for open- vocabulary semantic segmentation with pre-trained vision- language model.

Advancing Visual Large Language Model for Multi-granular Versatile Perception A simple baseline for open- vocabulary semantic segmentation with pre-trained vision- language model

Reference 76

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This paper cites Dynamic graph at- tention for referring expression comprehension.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Dynamic graph at- tention for referring expression comprehension

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:25:03.464252Z

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Observation 0fa72d14-b0f8-446d-8546-d554b1f3a4a6 · outbound

This paper cites R1-onevision: Advancing generalized multimodal reasoning through cross- modal formalization, 2025.

Advancing Visual Large Language Model for Multi-granular Versatile Perception R1-onevision: Advancing generalized multimodal reasoning through cross- modal formalization, 2025

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:25:03.456045Z

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Observation c0a577bb-61fe-4fe8-ae83-69622b4af0f6 · outbound

This paper cites Lavt: Language-aware vision transformer for referring image segmentation.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Lavt: Language-aware vision transformer for referring image segmentation

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:25:03.447077Z

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Observation 4c3979b2-5e45-45c8-9d4d-5aeb617b094f · outbound

This paper cites Modeling context in referring expres- sions.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Modeling context in referring expres- sions

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:25:03.438827Z

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This paper cites Modeling context in referring expres- sions.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Modeling context in referring expres- sions

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:25:03.430991Z

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Observation 55de4384-c3c4-4386-a750-18a9859a40bb · outbound

This paper cites v-clr: View-consistent learning for open-world instance seg- mentation.

Advancing Visual Large Language Model for Multi-granular Versatile Perception v-clr: View-consistent learning for open-world instance seg- mentation

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:25:03.422698Z

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Advancing Visual Large Language Model for Multi-granular Versatile Perception Unresolved cited work

Reference 83

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This paper cites Grounding referring expressions in images by variational context.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Grounding referring expressions in images by variational context

Reference 84

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This paper cites A simple framework for open-vocabulary segmentation and detection.

Advancing Visual Large Language Model for Multi-granular Versatile Perception A simple framework for open-vocabulary segmentation and detection

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:25:03.401452Z

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Observation a3953e7e-616e-4d20-b302-4430a72a2996 · outbound

This paper cites R1-vl: Learning to reason with multimodal large language models via step- wise group relative policy optimization, 2025.

Advancing Visual Large Language Model for Multi-granular Versatile Perception R1-vl: Learning to reason with multimodal large language models via step- wise group relative policy optimization, 2025

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:25:03.392598Z

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Observation b42c9963-2ac2-42be-af19-2bc9d7ac31f2 · outbound

This paper cites OMG-LLaVA: Bridging Image-level, Object-level, Pixel-level Reasoning and Understanding.

Advancing Visual Large Language Model for Multi-granular Versatile Perception OMG-LLaVA: Bridging Image-level, Object-level, Pixel-level Reasoning and Understanding

Reference 87

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This paper cites Psalm: Pixelwise segmentation with large multi-modal model.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Psalm: Pixelwise segmentation with large multi-modal model

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:25:03.384291Z

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Observation c5aaccc8-7ae1-40fb-a4bf-45c33e0a6902 · outbound

This paper cites Semantic under- standing of scenes through the ade20k dataset.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Semantic under- standing of scenes through the ade20k dataset

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:25:03.376001Z

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Observation 27c1b8fe-6856-40f3-812b-6a0db2ab204d · outbound

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

Advancing Visual Large Language Model for Multi-granular Versatile Perception Generalized decoding for pixel, image, and lan- guage

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:25:03.367658Z

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Observation ee94fd56-822c-4f04-8f19-475ccc1c2842 · outbound

This paper cites Segment everything everywhere all at once.

Advancing Visual Large Language Model for Multi-granular Versatile Perception Segment everything everywhere all at once

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:25:03.359490Z

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