NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment | Paper Citation Record

Observation e929890f-7c22-408b-8827-cda41a539c40 · outbound

This paper cites Qwen2.5-VL Technical Report.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Qwen2.5-VL Technical Report

Reference 2

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Observation 9e05d5ea-f606-4b73-92b9-2ca9dd39794b · outbound

This paper cites Xgboost: A scalable tree boosting system.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Xgboost: A scalable tree boosting system

Reference 3

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Observation a940a387-5253-45c4-9e9b-4b49d87856c3 · outbound

This paper cites AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities

Reference 4

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Observation fd697631-9e78-465c-bab7-f0c29442016f · outbound

This paper cites Teacher-guided learning for blind image quality assessment.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Teacher-guided learning for blind image quality assessment

Reference 5

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Observation 4a662c05-09dd-41e8-b956-9eef91aacce1 · outbound

This paper cites SEAGULL: No-reference Image Quality Assessment for Regions of Interest via Vision-Language Instruction Tuning.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment SEAGULL: No-reference Image Quality Assessment for Regions of Interest via Vision-Language Instruction Tuning

Reference 6

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Observation a075c7a6-6f15-4551-b397-40d6859966ba · outbound

This paper cites Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

Reference 7

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Observation 78e5e8a8-d527-461b-af00-4d9105298f9f · outbound

This paper cites NTIRE 2025 challenge on image super-resolution (×4): Methods and results.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment NTIRE 2025 challenge on image super-resolution (×4): Methods and results

Reference 8

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Observation 8d8fdd6c-2844-42a4-9e12-0a77db1b6d6e · outbound

This paper cites Promptiqa: Boosting the performance and generalization for no-reference image quality assessment via prompts.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Promptiqa: Boosting the performance and generalization for no-reference image quality assessment via prompts

Reference 9

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Observation ed58c9d2-a31f-4af8-9bdd-797bc4e6b8a5 · outbound

This paper cites NTIRE 2025 challenge on real-world face restoration: Methods and results.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment NTIRE 2025 challenge on real-world face restoration: Methods and results

Reference 10

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Observation a19b45a1-6df7-4907-81b0-07c84fa0b8ad · outbound

This paper cites NTIRE 2025 challenge on raw image restoration and super-resolution.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment NTIRE 2025 challenge on raw image restoration and super-resolution

Reference 11

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Observation e138687e-2807-446c-910d-fc62872b2b1b · outbound

This paper cites Raw image reconstruc- tion from RGB on smartphones.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Raw image reconstruc- tion from RGB on smartphones

Reference 12

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Observation 06553da2-39b5-4417-b0dc-d2b545978c8c · outbound

This paper cites NTIRE 2025 challenge on night photography rendering.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment NTIRE 2025 challenge on night photography rendering

Reference 13

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Observation ce6e330a-06a9-4c8b-afc1-1e0a5dd192ee · outbound

This paper cites EVA: Exploring the Limits of Masked Visual Representation Learning at Scale.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment EVA: Exploring the Limits of Masked Visual Representation Learning at Scale

Reference 14

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Observation 413ae559-0e28-4060-94c6-5c330398e1e2 · outbound

This paper cites NTIRE 2025 challenge on cross-domain few-shot object detection: Methods and results.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment NTIRE 2025 challenge on cross-domain few-shot object detection: Methods and results

Reference 15

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Observation 30006855-dcad-4077-921c-7d7c7014e513 · outbound

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

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 16

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Observation 2246a11c-dda8-41f7-90aa-2bbc57c4fa1c · outbound

This paper cites Evalmuse-40k: A reliable and fine-grained benchmark with comprehensive human annotations for text- to-image generation model evaluation, 2024.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Evalmuse-40k: A reliable and fine-grained benchmark with comprehensive human annotations for text- to-image generation model evaluation, 2024

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:57.929801Z

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Observation 23edce82-59ba-475a-a903-e1410ecd5350 · outbound

This paper cites NTIRE 2025 challenge on text to image generation model quality assess- ment.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment NTIRE 2025 challenge on text to image generation model quality assess- ment

Reference 18

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Observation a8ee6e06-a43c-4b47-b737-312b67132778 · outbound

This paper cites Latent Video Diffusion Models for High-Fidelity Long Video Generation.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Latent Video Diffusion Models for High-Fidelity Long Video Generation

Reference 19

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Observation ff830603-2229-45c0-b0b0-30770ccab734 · outbound

This paper cites LoRA: Low-rank adaptation of large language models.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment LoRA: Low-rank adaptation of large language models

Reference 20

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Observation 1cc6692b-dd77-4ae6-8125-7db296717b59 · outbound

This paper cites Tifa: Accu- rate and interpretable text-to-image faithfulness evaluation with question answering.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Tifa: Accu- rate and interpretable text-to-image faithfulness evaluation with question answering

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:57.420224Z

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Observation c4c692a6-51e4-41c8-bd76-5dacdbd6df8f · outbound

This paper cites NTIRE 2025 challenge on video quality enhancement for video con- ferencing: Datasets, methods and results.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment NTIRE 2025 challenge on video quality enhancement for video con- ferencing: Datasets, methods and results

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:57.249495Z

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Observation aa76e1ff-fa81-4447-9425-a7a4abd22870 · outbound

This paper cites Pick-a-pic: An open dataset of user preferences for text-to-image generation.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Pick-a-pic: An open dataset of user preferences for text-to-image generation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:57.138367Z

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Observation 299aeca2-5274-4555-a128-4836304edf1e · outbound

This paper cites NTIRE 2025 challenge on efficient burst hdr and restoration: Datasets, methods, and results.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment NTIRE 2025 challenge on efficient burst hdr and restoration: Datasets, methods, and results

Reference 24

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Observation a8ab0b5e-2c44-4626-a456-6b8df16829bd · outbound

This paper cites GenAI-Bench: Evaluating and Improving Compositional Text-to-Visual Generation.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment GenAI-Bench: Evaluating and Improving Compositional Text-to-Visual Generation

Reference 25

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Observation da29c1ac-a414-4647-85c9-78986351272d · outbound

This paper cites LLaVA-OneVision: Easy Visual Task Transfer.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment LLaVA-OneVision: Easy Visual Task Transfer

Reference 26

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Observation 4d09e96a-16e0-4157-8823-360827be5e9e · outbound

This paper cites Agiqa-3k: An open database for ai-generated image quality assessment.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Agiqa-3k: An open database for ai-generated image quality assessment

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:57.006346Z

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Observation b941df22-625f-413c-9cdd-493f63786b76 · outbound

This paper cites Aigiqa-20k: A large database for ai- generated image quality assessment.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Aigiqa-20k: A large database for ai- generated image quality assessment

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:56.889493Z

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Observation 08777ad3-e71e-4624-80a1-f045fec8cb55 · outbound

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

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:56.761697Z

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Observation b65be012-890b-4565-a1bb-6cc88ffde44e · outbound

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

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 30

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Observation 284e4e4b-5766-48c0-8554-0d335f5aedfb · outbound

This paper cites NTIRE 2025 challenge on day and night raindrop removal for dual-focused images: Methods and results.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment NTIRE 2025 challenge on day and night raindrop removal for dual-focused images: Methods and results

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:56.424775Z

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Observation 278ed33d-dc08-4c3d-ae0b-fd167c7ab5c4 · outbound

This paper cites NTIRE 2025 challenge on short-form ugc video quality assessment and enhancement: Kwaisr dataset and study.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment NTIRE 2025 challenge on short-form ugc video quality assessment and enhancement: Kwaisr dataset and study

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:56.308953Z

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Observation 64453233-e32f-4469-9efd-d5762acc1166 · outbound

This paper cites NTIRE 2025 challenge on short-form ugc video quality assessment and enhancement: Methods and results.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment NTIRE 2025 challenge on short-form ugc video quality assessment and enhancement: Methods and results

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:56.196257Z

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Observation 39fc6385-8b9e-41ad-96c6-2b1085b76825 · outbound

This paper cites NTIRE 2025 the 2nd restore any image model (RAIM) in the wild challenge.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment NTIRE 2025 the 2nd restore any image model (RAIM) in the wild challenge

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T15:09:46.278883Z

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Observation 566c90c3-1d30-4b0b-b44c-cbee5062a62d · outbound

This paper cites Rich human feedback for text-to-image generation.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Rich human feedback for text-to-image generation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:56.045722Z

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Observation 6f465708-2daa-43dc-8deb-2fb026a50177 · outbound

This paper cites Evaluating text-to-visual generation with image-to-text gen- eration.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Evaluating text-to-visual generation with image-to-text gen- eration

Reference 36

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Observation dd871405-ecc8-4b35-a227-c1b9ba227326 · outbound

This paper cites NTIRE 2025 XGC quality assessment chal- lenge: Methods and results.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment NTIRE 2025 XGC quality assessment chal- lenge: Methods and results

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:55.910720Z

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Observation 32dc8943-1cfd-4b58-a98b-304da3be1866 · outbound

This paper cites NTIRE 2025 challenge on low light image enhancement: Methods and results.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment NTIRE 2025 challenge on low light image enhancement: Methods and results

Reference 38

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Observation c8943ba2-5b1c-4e7c-9385-3d5061573032 · outbound

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

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Swin transformer: Hierarchical vision transformer using shifted windows

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:55.754117Z

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Observation 8d565fb3-4a9a-4d30-9dc0-c791df6aad28 · outbound

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

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Ovis: Structural Embedding Alignment for Multimodal Large Language Model

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T15:09:47.031949Z

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Observation 556d63fb-f748-44fe-a0d8-f434d5f9ae80 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Learning transferable visual models from natural language supervi- sion

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T15:09:47.142042Z

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Observation 66ff6b2d-9a04-4999-9f91-6b17107da5ed · outbound

This paper cites The tenth NTIRE 2025 efficient super- resolution challenge report.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment The tenth NTIRE 2025 efficient super- resolution challenge report

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:55.606842Z

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Observation aff8ac72-e62f-4937-af03-fe5923ee6f86 · outbound

This paper cites U- net: Convolutional networks for biomedical image segmen- tation.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment U- net: Convolutional networks for biomedical image segmen- tation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:55.487411Z

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Observation 1f5d5c42-6608-45f2-ae19-3373d64bfcbf · outbound

This paper cites NTIRE 2025 challenge on UGC video enhancement: Meth- ods and results.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment NTIRE 2025 challenge on UGC video enhancement: Meth- ods and results

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:55.278500Z

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Observation 13d5b861-8ea3-4ea4-877f-708f16357d75 · outbound

This paper cites NTIRE 2025 challenge on event-based image deblurring: Methods and results.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment NTIRE 2025 challenge on event-based image deblurring: Methods and results

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:54.999725Z

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Observation 0d15b770-d46a-46b8-9705-aef62e4720e9 · outbound

This paper cites The tenth ntire 2025 image denoising challenge report.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment The tenth ntire 2025 image denoising challenge report

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:54.811728Z

Source-reported events for the cited work

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Observation 5be8130c-858a-47d4-a475-ba75bdb5caab · outbound

This paper cites Blind quality assessment for in-the-wild images via hierarchical feature fusion and iterative mixed database training.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Blind quality assessment for in-the-wild images via hierarchical feature fusion and iterative mixed database training

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:54.619811Z

Source-reported events for the cited work

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Observation d4fdfaf6-97f9-47d4-9518-ba95d9c0dc64 · outbound

This paper cites SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T15:09:48.049439Z

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Unavailable: canonical work link unavailable.

Observation 362a9418-d02b-48b8-8337-7c62080f17fd · outbound

This paper cites NTIRE 2025 image shadow removal challenge report.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment NTIRE 2025 image shadow removal challenge report

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:54.396302Z

Source-reported events for the cited work

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Observation 9af1d693-3081-4b3a-9baf-5d4aefc4996e · outbound

This paper cites NTIRE 2025 ambi- ent lighting normalization challenge.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment NTIRE 2025 ambi- ent lighting normalization challenge

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:54.127462Z

Source-reported events for the cited work

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Observation 72d91ad0-f022-491b-affe-13db8bb37fdd · outbound

This paper cites Hierarchical curriculum learning for no-reference image quality assessment.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Hierarchical curriculum learning for no-reference image quality assessment

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:53.900013Z

Source-reported events for the cited work

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Observation 995404a8-2555-42fe-bf4f-61e38fb53443 · outbound

This paper cites Aigciqa2023: A large-scale image quality assessment database for ai generated images: from the perspectives of quality, authenticity and correspondence.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Aigciqa2023: A large-scale image quality assessment database for ai generated images: from the perspectives of quality, authenticity and correspondence

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:53.675955Z

Source-reported events for the cited work

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Observation b8fa76ce-f3d3-4ecd-9c21-2f29632dc4e3 · outbound

This paper cites NTIRE 2025 challenge on light field image super-resolution: Methods and results.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment NTIRE 2025 challenge on light field image super-resolution: Methods and results

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:53.491924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c323d025-d429-4df1-b32b-be0564420642 · outbound

This paper cites Unified Reward Model for Multimodal Understanding and Generation.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Unified Reward Model for Multimodal Understanding and Generation

Reference 54

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Observation 07b73ccc-01c4-4ff5-97f0-22ce089d250a · outbound

This paper cites DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models

Reference 55

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Observation ace5e260-5e3c-49bd-adc4-946f4dc02aba · outbound

This paper cites Revisiting Text-to-Image Evaluation with Gecko: On Metrics, Prompts, and Human Ratings.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Revisiting Text-to-Image Evaluation with Gecko: On Metrics, Prompts, and Human Ratings

Reference 56

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Observation cbf7cad9-744a-4897-ad7d-7b90c7042071 · outbound

This paper cites Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels

Reference 58

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This paper cites Human preference score: Better aligning text- to-image models with human preference.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Human preference score: Better aligning text- to-image models with human preference

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:53.357887Z

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Observation 0c9448f7-1303-4394-bfd1-9413147c6951 · outbound

This paper cites DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding

Reference 60

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Observation 178f0670-d488-436d-8d86-c2c953a0d908 · outbound

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

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Florence-2: Advancing a unified representation for a variety of vision tasks

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:53.206034Z

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Observation 74e39e8c-431c-4e65-a14a-c2826a7ab82d · outbound

This paper cites Sam2-unet: Segment anything 2 makes strong encoder for natural and medical image segmentation.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Sam2-unet: Segment anything 2 makes strong encoder for natural and medical image segmentation

Reference 62

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Observation 8a08317c-e244-4eb8-a703-ee5876bfbd4a · outbound

This paper cites Imagere- ward: Learning and evaluating human preferences for text- to-image generation.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Imagere- ward: Learning and evaluating human preferences for text- to-image generation

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:53.086628Z

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Observation b989a894-b547-4ec3-b3ab-c51694939b47 · outbound

This paper cites Boosting image quality assessment through efficient transformer adaptation with lo- cal feature enhancement.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Boosting image quality assessment through efficient transformer adaptation with lo- cal feature enhancement

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:52.919377Z

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Observation df519ae5-de4b-4893-bbeb-b8609af7f300 · outbound

This paper cites A Sanity Check for AI-generated Image Detection.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment A Sanity Check for AI-generated Image Detection

Reference 65

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This paper cites NTIRE 2025 challenge on single image reflection removal in the wild: Datasets, methods and results.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment NTIRE 2025 challenge on single image reflection removal in the wild: Datasets, methods and results

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:52.771844Z

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Observation 4cd89c44-2a47-4abf-8b0b-8edcdc12e06c · outbound

This paper cites Maniqa: Multi-dimension attention network for no-reference image quality assessment.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Maniqa: Multi-dimension attention network for no-reference image quality assessment

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:52.633899Z

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Observation a43ba5b8-a0e2-4f5d-93a5-306a84c4053f · outbound

This paper cites mplug- owl2: Revolutionizing multi-modal large language model with modality collaboration.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment mplug- owl2: Revolutionizing multi-modal large language model with modality collaboration

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:52.503359Z

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Observation 8db9ef52-85f2-4f87-b34c-9297e26133d0 · outbound

This paper cites Teaching large language models to regress accurate image quality scores using score distribution.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Teaching large language models to regress accurate image quality scores using score distribution

Reference 70

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Observation 767aef20-b7d5-4659-8b90-5d5efa3e74a6 · outbound

This paper cites Instruction-augmented multimodal alignment for image-text and element matching.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Instruction-augmented multimodal alignment for image-text and element matching

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:52.390982Z

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Observation 1ccd165d-6b1e-4233-9632-b8feeb53644a · outbound

This paper cites NTIRE 2025 challenge on hr depth from images of specular and transparent surfaces.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment NTIRE 2025 challenge on hr depth from images of specular and transparent surfaces

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:52.272425Z

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Observation b9eabf49-9714-41f3-bf80-26fe3144495c · outbound

This paper cites Blind image quality assessment using a deep bilinear convolutional neural network.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Blind image quality assessment using a deep bilinear convolutional neural network

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:52.177141Z

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Observation 0015bd98-3492-42c7-96a1-ac64e327e6b3 · outbound

This paper cites Blind image quality assessment via vision- language correspondence: A multitask learning perspective.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Blind image quality assessment via vision- language correspondence: A multitask learning perspective

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:52.057106Z

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Observation a8d9c08d-32f8-44ee-ae98-16ee660b7b53 · outbound

This paper cites A perceptual quality assessment exploration for aigc images.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment A perceptual quality assessment exploration for aigc images

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:51.919313Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b7113197-5357-4962-ba2c-648620721343 · outbound

This paper cites Tokenfocus-vqa: Enhancing text-to-image alignment with position-aware focus and multi-perspective aggregations on lvlms, 2025.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Tokenfocus-vqa: Enhancing text-to-image alignment with position-aware focus and multi-perspective aggregations on lvlms, 2025

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:51.774925Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b524e6b2-d060-4909-9710-067324c27949 · outbound

This paper cites Study group learning: Improving retinal vessel segmentation trained with noisy labels.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Study group learning: Improving retinal vessel segmentation trained with noisy labels

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:51.669463Z

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Observation b41587d1-425c-43b5-bc80-a6f4045b3dad · outbound

This paper cites Detrs with col- laborative hybrid assignments training.

NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment Detrs with col- laborative hybrid assignments training

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:09:51.507749Z

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