Describe Anything Model for Visual Question Answering on Text-rich Images | Paper Citation Record

Observation 84beee77-f1d4-4035-a9b7-5f7201e7181c · outbound

This paper cites Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs.

Describe Anything Model for Visual Question Answering on Text-rich Images Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs

Reference 1

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Observation 79bac547-d27f-430e-944b-1006cabfc0d4 · outbound

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

Describe Anything Model for Visual Question Answering on Text-rich Images Flamingo: a visual language model for few-shot learning

Reference 2

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Observation 7c81a785-24f2-415b-bfa1-4e5fd412585c · outbound

This paper cites Lawrence Zitnick, and Devi Parikh.

Describe Anything Model for Visual Question Answering on Text-rich Images Lawrence Zitnick, and Devi Parikh

Reference 3

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Observation e9cc3a14-c47a-477e-8694-381b28179564 · outbound

This paper cites Manmatha.

Describe Anything Model for Visual Question Answering on Text-rich Images Manmatha

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:51:32.796643Z

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Observation 8a470535-b176-4f33-94f3-45f224946407 · outbound

This paper cites Manmatha.

Describe Anything Model for Visual Question Answering on Text-rich Images Manmatha

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:51:32.663645Z

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Observation d8d2a888-5a6f-4ff0-9ad3-e569c7f39201 · outbound

This paper cites Qwen2.5-VL Technical Report.

Describe Anything Model for Visual Question Answering on Text-rich Images Qwen2.5-VL Technical Report

Reference 6

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Observation 344dca3c-927f-4deb-86dc-f41c4a3ea717 · outbound

This paper cites Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluation.

Describe Anything Model for Visual Question Answering on Text-rich Images Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluation

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:51:28.793646Z

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Observation 4ad8f5d5-e9bb-415d-b736-ca6e5a7cb673 · outbound

This paper cites Meyer, Yuning Chai, Dennis Park, and Yong Jae Lee.

Describe Anything Model for Visual Question Answering on Text-rich Images Meyer, Yuning Chai, Dennis Park, and Yong Jae Lee

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:51:32.487993Z

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Observation bf59f0d9-51d7-4de1-9dc9-850e09d2d67b · outbound

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

Describe Anything Model for Visual Question Answering on Text-rich Images Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic

Reference 9

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Observation 24af260d-1cfe-4dfd-a9b9-c4cffd26ca9c · outbound

This paper cites InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning.

Describe Anything Model for Visual Question Answering on Text-rich Images InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T16:51:23.059337Z

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Observation c2a536a4-87fd-4d88-82a1-083890949eaf · outbound

This paper cites Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models.

Describe Anything Model for Visual Question Answering on Text-rich Images Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T16:51:23.141636Z

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Observation e726fda4-961a-49f9-9f94-11b812202f0d · outbound

This paper cites Scalable Vision Language Model Training via High Quality Data Curation.

Describe Anything Model for Visual Question Answering on Text-rich Images Scalable Vision Language Model Training via High Quality Data Curation

Reference 12

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Observation 1b8edb5d-19a6-4eb7-9eef-0f7e89541c57 · outbound

This paper cites Making the v in vqa matter: Elevating the role of image understanding in visual question answer- ing.

Describe Anything Model for Visual Question Answering on Text-rich Images Making the v in vqa matter: Elevating the role of image understanding in visual question answer- ing

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:51:32.334006Z

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Observation f5dd4f26-1fc6-476c-82ae-17cc33a5a2b1 · outbound

This paper cites A Survey on LLM-as-a-Judge.

Describe Anything Model for Visual Question Answering on Text-rich Images A Survey on LLM-as-a-Judge

Reference 14

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Observation 06b64956-6bb4-49af-9e10-b9e49bf46061 · outbound

This paper cites Regiongpt: Towards region understanding vision lan- guage model.

Describe Anything Model for Visual Question Answering on Text-rich Images Regiongpt: Towards region understanding vision lan- guage model

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:51:32.189072Z

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Observation e8c18f00-3001-4fe4-886f-8417e463ac3e · outbound

This paper cites Hires-llava: Restoring fragmen- tation input in high-resolution large vision-language models.

Describe Anything Model for Visual Question Answering on Text-rich Images Hires-llava: Restoring fragmen- tation input in high-resolution large vision-language models

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:51:31.945422Z

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Observation 8449d0f0-9f88-4b7c-ba38-825c116fea0b · outbound

This paper cites Seeing out of the box: End- to-end pre-training for vision-language representation learn- ing.

Describe Anything Model for Visual Question Answering on Text-rich Images Seeing out of the box: End- to-end pre-training for vision-language representation learn- ing

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:51:31.820105Z

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Observation 2d1d844b-fbb9-4eba-9dd2-e89947ffb9d8 · outbound

This paper cites Multi-Agent VQA: Exploring Multi-Agent Foundation Models in Zero-Shot Visual Question Answering.

Describe Anything Model for Visual Question Answering on Text-rich Images Multi-Agent VQA: Exploring Multi-Agent Foundation Models in Zero-Shot Visual Question Answering

Reference 18

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Observation 00a07c69-fb59-494f-a0bb-a36d5b4123d9 · outbound

This paper cites Spa- tially aware multimodal transformers for textvqa.

Describe Anything Model for Visual Question Answering on Text-rich Images Spa- tially aware multimodal transformers for textvqa

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:51:31.696201Z

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Observation a8f78605-3409-4d13-a3f9-d51b2fdcfc5f · outbound

This paper cites Binary codes capable of cor- recting deletions, insertions, and reversals.

Describe Anything Model for Visual Question Answering on Text-rich Images Binary codes capable of cor- recting deletions, insertions, and reversals

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:51:31.585020Z

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Observation 92358a79-729d-47d1-aa8b-4cdc606fd0c0 · outbound

This paper cites From gener- ation to judgment: Opportunities and challenges of llm-as-a- judge.

Describe Anything Model for Visual Question Answering on Text-rich Images From gener- ation to judgment: Opportunities and challenges of llm-as-a- judge

Reference 21

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Observation 5ba75bd4-02e0-44dc-8bcf-3d33d8a96cdd · outbound

This paper cites LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models.

Describe Anything Model for Visual Question Answering on Text-rich Images LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models

Reference 22

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Observation 95cb28d3-56cf-49dc-9ab2-76a98c31f153 · outbound

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

Describe Anything Model for Visual Question Answering on Text-rich Images Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 23

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Observation 1b0da12a-8b7c-41f3-8183-55f5ebaf44b9 · outbound

This paper cites Blip-2: Bootstrapping language-image pre-training with 9 frozen image encoders and large language models.

Describe Anything Model for Visual Question Answering on Text-rich Images Blip-2: Bootstrapping language-image pre-training with 9 frozen image encoders and large language models

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:51:31.329965Z

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Observation 733fa32e-9fdf-4b49-aa95-9a54c59959ea · outbound

This paper cites VisualBERT: A Simple and Performant Baseline for Vision and Language.

Describe Anything Model for Visual Question Answering on Text-rich Images VisualBERT: A Simple and Performant Baseline for Vision and Language

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T16:51:24.115929Z

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Observation e4b0003d-c9ef-4039-aaf3-b683aa25f547 · outbound

This paper cites UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning.

Describe Anything Model for Visual Question Answering on Text-rich Images UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T16:51:24.195805Z

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Observation 7dedfffd-9ff3-42f3-95f0-e645bc29d63b · outbound

This paper cites Oscar: Object-semantics aligned pre-training for vision-language tasks.

Describe Anything Model for Visual Question Answering on Text-rich Images Oscar: Object-semantics aligned pre-training for vision-language tasks

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T16:51:24.351412Z

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Observation 00944919-2683-4e18-9e2b-9d25779b7491 · outbound

This paper cites Enhancing visual document understanding with contrastive learning in large visual-language models.

Describe Anything Model for Visual Question Answering on Text-rich Images Enhancing visual document understanding with contrastive learning in large visual-language models

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:51:31.125927Z

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Observation 786371db-77b2-4394-9012-dd66e803d6f6 · outbound

This paper cites Mon- key: Image resolution and text label are important things for large multi-modal models.

Describe Anything Model for Visual Question Answering on Text-rich Images Mon- key: Image resolution and text label are important things for large multi-modal models

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:51:30.961752Z

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Observation c5e311f2-055f-4a36-8c66-36da3105ce8f · outbound

This paper cites Describe Anything: Detailed Localized Image and Video Captioning.

Describe Anything Model for Visual Question Answering on Text-rich Images Describe Anything: Detailed Localized Image and Video Captioning

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T16:51:24.655762Z

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Observation 1309521c-a60a-444f-ba0b-13861ed211f6 · outbound

This paper cites Perceive Anything: Recognize, Explain, Caption, and Segment Anything in Images and Videos.

Describe Anything Model for Visual Question Answering on Text-rich Images Perceive Anything: Recognize, Explain, Caption, and Segment Anything in Images and Videos

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T16:51:24.717294Z

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Observation 3b93f440-cb96-4770-bf11-64273b8b170f · outbound

This paper cites Revive: regional visual representa- tion matters in knowledge-based visual question answering.

Describe Anything Model for Visual Question Answering on Text-rich Images Revive: regional visual representa- tion matters in knowledge-based visual question answering

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:51:30.758577Z

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Observation b2244a65-2661-4e5a-9c3e-68e7cf52ac17 · outbound

This paper cites TextMonkey: An OCR-Free Large Multimodal Model for Understanding Document.

Describe Anything Model for Visual Question Answering on Text-rich Images TextMonkey: An OCR-Free Large Multimodal Model for Understanding Document

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T16:51:25.051188Z

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Observation a6538a19-2053-4f7b-9b23-425ec16e278e · outbound

This paper cites Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks.

Describe Anything Model for Visual Question Answering on Text-rich Images Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T16:51:25.447570Z

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Observation f4713053-4307-437d-9fb0-c1d5bf82f1c1 · outbound

This paper cites Ovis: Structural Embedding Alignment for Multimodal Large Language Model.

Describe Anything Model for Visual Question Answering on Text-rich Images Ovis: Structural Embedding Alignment for Multimodal Large Language Model

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T16:51:25.555322Z

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Observation e20bd04a-b6ce-4e0a-b61c-6265f5921053 · outbound

This paper cites Chartqa: A benchmark for question an- swering about charts with visual and logical reasoning.

Describe Anything Model for Visual Question Answering on Text-rich Images Chartqa: A benchmark for question an- swering about charts with visual and logical reasoning

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:51:30.597058Z

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Observation c027a98e-a9e1-4ea7-bf6c-cdc626865f15 · outbound

This paper cites ChartQAPro: A More Diverse and Challenging Benchmark for Chart Question Answering.

Describe Anything Model for Visual Question Answering on Text-rich Images ChartQAPro: A More Diverse and Challenging Benchmark for Chart Question Answering

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T16:51:25.669672Z

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Observation 0ed54717-4d24-459a-8862-33932e98ed8f · outbound

This paper cites Docvqa: A dataset for vqa on document images.

Describe Anything Model for Visual Question Answering on Text-rich Images Docvqa: A dataset for vqa on document images

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:51:30.453754Z

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Observation ed5aed7a-4b02-4b57-b8d6-d165feae8ca5 · outbound

This paper cites Infographicvqa.

Describe Anything Model for Visual Question Answering on Text-rich Images Infographicvqa

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:51:30.341084Z

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Observation 4bd88eda-5aae-4f02-9111-162a601db482 · outbound

This paper cites Im- proving automatic vqa evaluation using large language mod- els.

Describe Anything Model for Visual Question Answering on Text-rich Images Im- proving automatic vqa evaluation using large language mod- els

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:51:30.215862Z

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Observation aec207f3-7fb0-490b-9c7d-b048f9e70ac8 · outbound

This paper cites Dual dynamic consis- tency regularization for semi-supervised domain adaptation.

Describe Anything Model for Visual Question Answering on Text-rich Images Dual dynamic consis- tency regularization for semi-supervised domain adaptation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:51:30.060757Z

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Observation 3f77f525-cd8e-49ef-8582-b112865b8d08 · outbound

This paper cites Enhancing Vietnamese VQA through Curriculum Learning on Raw and Augmented Text Representations.

Describe Anything Model for Visual Question Answering on Text-rich Images Enhancing Vietnamese VQA through Curriculum Learning on Raw and Augmented Text Representations

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:51:28.310404Z

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Observation 9e63eb81-1d27-45f8-905b-48b84c43d7ab · outbound

This paper cites Kosmos-2: Grounding Multimodal Large Language Models to the World.

Describe Anything Model for Visual Question Answering on Text-rich Images Kosmos-2: Grounding Multimodal Large Language Models to the World

Reference 43

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Observation e0d03647-33cc-4a4f-be42-c6cb0f661b42 · outbound

This paper cites Going full-tilt boogie on document understanding with text-image-layout transformer.

Describe Anything Model for Visual Question Answering on Text-rich Images Going full-tilt boogie on document understanding with text-image-layout transformer

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:51:29.965392Z

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Observation 90daa82d-0ea6-4b00-af00-80fb5d86b8e1 · outbound

This paper cites Towards vqa models that can read.

Describe Anything Model for Visual Question Answering on Text-rich Images Towards vqa models that can read

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:51:29.892501Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c66fd355-92af-4098-a040-fc45860ef684 · outbound

This paper cites Gemini 2.5: Pushing the frontier with ad- vanced reasoning, multimodality, long context, and next gen- eration agentic capabilities.

Describe Anything Model for Visual Question Answering on Text-rich Images Gemini 2.5: Pushing the frontier with ad- vanced reasoning, multimodality, long context, and next gen- eration agentic capabilities

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:51:29.825409Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d2fa1117-902e-4b5a-9b1f-80ce461457ff · outbound

This paper cites Igl-dt: Itera- tive global-local feature learning with dual-teacher semantic segmentation framework under limited annotation scheme.

Describe Anything Model for Visual Question Answering on Text-rich Images Igl-dt: Itera- tive global-local feature learning with dual-teacher semantic segmentation framework under limited annotation scheme

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:51:29.753418Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 245d17f9-558d-4cfc-b599-229a623e6672 · outbound

This paper cites Mlg2net: Molecular global graph network for drug response prediction in lung cancer cell lines.

Describe Anything Model for Visual Question Answering on Text-rich Images Mlg2net: Molecular global graph network for drug response prediction in lung cancer cell lines

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:51:29.657037Z

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Observation 4897f6b0-da76-4f82-b5a6-5728fd922136 · outbound

This paper cites Describe Anything in Medical Images.

Describe Anything Model for Visual Question Answering on Text-rich Images Describe Anything in Medical Images

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T16:51:27.152580Z

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Observation 3c94e104-6be6-41c1-ac8e-556f2f15b7ee · outbound

This paper cites Layoutlm: Pre-training of text and layout for document image understanding.

Describe Anything Model for Visual Question Answering on Text-rich Images Layoutlm: Pre-training of text and layout for document image understanding

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:51:29.556329Z

Source-reported events for the cited work

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Observation eb9ae5ac-42fa-4da1-b241-8ea11a581587 · outbound

This paper cites LayoutLMv2: Multi-modal pre-training for visually-rich document under- standing.

Describe Anything Model for Visual Question Answering on Text-rich Images LayoutLMv2: Multi-modal pre-training for visually-rich document under- standing

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:51:29.460892Z

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Observation b3db362a-53d8-482d-aefe-0a1772ec2630 · outbound

This paper cites Qwen3 Technical Report.

Describe Anything Model for Visual Question Answering on Text-rich Images Qwen3 Technical Report

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-06T16:51:27.484932Z

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Observation f42afdef-4402-4d9a-b516-53d06f90cedb · outbound

This paper cites MiniCPM-V: A GPT-4V Level MLLM on Your Phone.

Describe Anything Model for Visual Question Answering on Text-rich Images MiniCPM-V: A GPT-4V Level MLLM on Your Phone

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T16:51:27.556990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5e660d4-88a1-4d62-b350-74de87d435b9 · outbound

This paper cites Ferret: Refer and Ground Anything Anywhere at Any Granularity.

Describe Anything Model for Visual Question Answering on Text-rich Images Ferret: Refer and Ground Anything Anywhere at Any Granularity

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T16:51:27.621969Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28d13954-ed74-4af8-affb-853296e5eb02 · outbound

This paper cites MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities.

Describe Anything Model for Visual Question Answering on Text-rich Images MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T16:51:27.706414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62c9845f-ed78-4449-bf61-0f9f206f4657 · outbound

This paper cites Osprey: Pixel un- derstanding with visual instruction tuning.

Describe Anything Model for Visual Question Answering on Text-rich Images Osprey: Pixel un- derstanding with visual instruction tuning

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:51:29.320554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 88b25ccb-9f43-4a5b-9165-a65529b3b75f · outbound

This paper cites VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding.

Describe Anything Model for Visual Question Answering on Text-rich Images VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T16:51:27.876456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b847188f-6b8a-41bd-b0be-40ba08d13d9e · outbound

This paper cites Gpt4roi: Instruction tuning large language model on region- of-interest.

Describe Anything Model for Visual Question Answering on Text-rich Images Gpt4roi: Instruction tuning large language model on region- of-interest

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:51:29.166465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c5f36874-abf2-4723-9ad4-0522f873d8d7 · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena.Advances in Neural Information Processing Systems, 36:46595–46623, 2023.

Describe Anything Model for Visual Question Answering on Text-rich Images Judging llm-as-a-judge with mt-bench and chatbot arena.Advances in Neural Information Processing Systems, 36:46595–46623, 2023

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:51:29.029064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b30d1ff6-ecec-48bc-abdf-5041da93373e · outbound

This paper cites InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models.

Describe Anything Model for Visual Question Answering on Text-rich Images InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T16:51:28.030074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af5746fb-3597-4393-ac85-7f9f29d0aac8 · outbound

This paper cites an unresolved cited work.

Describe Anything Model for Visual Question Answering on Text-rich Images Unresolved cited work

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-06T16:51:27.417470Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fec0588c-817c-4e8d-a524-d66f157feb45 · outbound

This paper cites an unresolved cited work.

Describe Anything Model for Visual Question Answering on Text-rich Images Unresolved cited work

Reference 2022

Resolution: parse uncertain; no resolver link, observed 2026-08-06T16:51:24.843669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Outbound references

Pith citing papers