Paper Citation Record · LEDGER

Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics

As of 12 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 1 inbound Pith citation observation for arXiv:2412.10594.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.10594 v1

Coverage vector

measured 59 of 59 reference resolution

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measured 60 of 60 standing notices

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: arxiv_reference, observed 2026-05-12T08:36:26.482689Z

Reference resolution

59 of 59 outbound references displayed

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External citation measurements

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Outbound references

Observation 17c76d01-0a43-4413-9ca1-1c11c3c02838 · outbound

This paper cites GPT-4 Technical Report.

Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics GPT-4 Technical Report

Reference 1

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Observation 8a9ada32-5c20-4329-ae7a-bb6c736e9d9f · outbound

This paper cites Getting vit in shape: Scaling laws for compute-optimal model design.

Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Getting vit in shape: Scaling laws for compute-optimal model design

Reference 2

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Observation fcbf14c3-cec0-4f98-9c87-b6bc37f76105 · outbound

This paper cites Improving image captioning descriptive- ness by ranking and llm-based fusion.

Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Improving image captioning descriptive- ness by ranking and llm-based fusion

Reference 3

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This paper cites Learning a deep single image contrast enhancer from multi-exposure images.

Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Learning a deep single image contrast enhancer from multi-exposure images

Reference 4

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This paper cites Emerg- ing properties in self-supervised vision transformers.

Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Emerg- ing properties in self-supervised vision transformers

Reference 5

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Observation 46faf46c-e022-4296-a3ee-a7738fa3cfaf · outbound

This paper cites Reproducible scal- ing laws for contrastive language-image learning.

Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Reproducible scal- ing laws for contrastive language-image learning

Reference 6

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Observation 5301a0e6-3010-4e86-bcfd-0c4e4172cdf5 · outbound

This paper cites Adversarially robust clip mod- els induce better (robust) perceptual metrics.

Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Adversarially robust clip mod- els induce better (robust) perceptual metrics

Reference 7

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Observation 5f256352-438c-4a75-b6b7-5d347f933941 · outbound

This paper cites Dream- sim: Learning new dimensions of human visual similarity using synthetic data.

Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Dream- sim: Learning new dimensions of human visual similarity using synthetic data

Reference 8

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This paper cites BLINK: Multimodal Large Language Models Can See but Not Perceive.

Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics BLINK: Multimodal Large Language Models Can See but Not Perceive

Reference 9

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This paper cites R-LPIPS: An adversarially robust perceptual similarity metric.

Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics R-LPIPS: An adversarially robust perceptual similarity metric

Reference 10

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Observation 7553f00d-4b45-46b2-a213-2e46d7755918 · outbound

This paper cites EMMA: Efficient Visual Alignment in Multi-Modal LLMs.

Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics EMMA: Efficient Visual Alignment in Multi-Modal LLMs

Reference 11

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This paper cites Lipsim: A provably robust perceptual similarity metric.

Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Lipsim: A provably robust perceptual similarity metric

Reference 12

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This paper cites Generative adversarial networks.

Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Generative adversarial networks

Reference 13

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This paper cites Masked autoencoders are scalable vision learners.

Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Masked autoencoders are scalable vision learners

Reference 14

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This paper cites Clipscore: A reference-free evaluation met- ric for image captioning.

Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Clipscore: A reference-free evaluation met- ric for image captioning

Reference 15

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Observation 7d9e3fd5-4727-4813-983a-4c43c38a8296 · outbound

This paper cites Denoising dif- fusion probabilistic models.

Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Denoising dif- fusion probabilistic models

Reference 16

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Observation 4bca766e-af38-420e-b26e-12dec1e77cc4 · outbound

This paper cites Koniq-10k: An ecologically valid database for deep learning of blind image quality assessment.

Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Koniq-10k: An ecologically valid database for deep learning of blind image quality assessment

Reference 17

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

Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics LoRA: Low-rank adaptation of large language models

Reference 18

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This paper cites Aesexpert: Towards multi-modality foun- dation model for image aesthetics perception.

Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Aesexpert: Towards multi-modality foun- dation model for image aesthetics perception

Reference 19

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This paper cites HQ-Edit: A High-Quality Dataset for Instruction-based Image Editing.

Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics HQ-Edit: A High-Quality Dataset for Instruction-based Image Editing

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Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics MANTIS: Interleaved Multi-Image Instruction Tuning

Reference 21

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This paper cites Pipal: a large-scale image quality assessment dataset for perceptual image restoration.

Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Pipal: a large-scale image quality assessment dataset for perceptual image restoration

Reference 22

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Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics ImagenHub: Standardizing the evaluation of conditional image generation models

Reference 23

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Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics UMIC: An Unreferenced Metric for Image Captioning via Contrastive Learning

Reference 24

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Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Agiqa-3k: An open database for ai-generated image quality assessment

Reference 25

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Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics AIGIQA-20K: A Large Database for AI-Generated Image Quality Assessment

Reference 26

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Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models

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Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

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Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Kadid-10k: A large-scale artificially distorted iqa database

Reference 29

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Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Microsoft coco: Common objects in context

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Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Unresolved cited work

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Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Improved baselines with visual instruction tuning

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Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space Optimization

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Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics RoBERTa: A Robustly Optimized BERT Pretraining Approach

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Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Vandermeulen, and Simon Kornblith

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Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Lost in quantization: Improving particu- lar object retrieval in large scale image databases

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Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

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Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Pieapp: Perceptual image-error assessment through pairwise preference

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Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Learning transferable visual models from natural language supervision

Reference 39

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Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Positive-augmented contrastive learning for image and video captioning evaluation

Reference 40

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Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics When Does Perceptual Alignment Benefit Vision Representations?

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Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Chameleon: Mixed-Modal Early-Fusion Foundation Models

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Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Polos: Multimodal metric learning from human feed- back for image captioning

Reference 43

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Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding

Reference 44

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Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Ex- ploring clip for assessing the look and feel of images

Reference 45

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Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision

Reference 46

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Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Towards Open-ended Visual Quality Comparison

Reference 47

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Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis

Reference 48

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Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Imagere- ward: Learning and evaluating human preferences for text- to-image generation

Reference 49

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

Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics mplug- owl2: Revolutionizing multi-modal large language model with modality collaboration

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:52:21.530751Z

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Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Sigmoid Loss for Language Image Pre-Training

Reference 51

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This paper cites Text-to-image Diffusion Models in Generative AI: A Survey.

Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Text-to-image Diffusion Models in Generative AI: A Survey

Reference 52

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Observation 63550c03-bfd8-424b-a306-0f8b6a1c56ca · outbound

This paper cites Magicbrush: A manually annotated dataset for instruction- guided image editing.

Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Magicbrush: A manually annotated dataset for instruction- guided image editing

Reference 53

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Observation fd1a6379-26b0-4115-86de-fda62eba9f85 · outbound

This paper cites Efros, Eli Shecht- man, and Oliver Wang.

Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Efros, Eli Shecht- man, and Oliver Wang

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:52:21.496296Z

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Observation c7625820-c4d3-4a4b-808b-23313a459cfb · outbound

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

Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Blind image quality assessment via vision- language correspondence: A multitask learning perspective

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:52:21.480909Z

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Observation afb4e741-d43f-4779-8027-ba6b89d1f49b · outbound

This paper cites A-Bench: Are LMMs Masters at Evaluating AI-generated Images?.

Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics A-Bench: Are LMMs Masters at Evaluating AI-generated Images?

Reference 56

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Observation 6155f7f7-7f0b-45a1-b3da-ddc8328ad328 · outbound

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Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics 2AFC Prompting of Large Multimodal Models for Image Quality Assessment

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-11T15:52:20.845040Z

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Observation 8d03083d-7bac-494f-bcf0-dd6c7656c0fa · outbound

This paper cites Adaptive Image Quality Assessment via Teaching Large Multimodal Model to Compare.

Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Adaptive Image Quality Assessment via Teaching Large Multimodal Model to Compare

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-11T15:52:20.853170Z

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Observation d86a91e8-aa96-4272-9426-cdbabaa8de2c · outbound

This paper cites an unresolved cited work.

Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics Unresolved cited work

Reference 2023

Resolution: unresolved; raw_fallback, observed 2026-08-11T15:52:21.563678Z

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Pith citing papers

Observation 199e40c4-055f-4d48-bd48-e116c72c03bf · inbound

SYNCR: A Cross-Video Reasoning Benchmark with Synthetic Grounding cites this paper.

SYNCR: A Cross-Video Reasoning Benchmark with Synthetic Grounding Towards Unified Benchmark and Models for Multi-Modal Perceptual Metrics

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:36:26.485902Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.