Paper Citation Record · LEDGER

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models

As of 17 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2505.00150.

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

pith.paper-citation-record.v1
2505.00150 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:55:28.327964Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

48 of 48 outbound references displayed

verified exact0
verified fuzzy15
unresolved33
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 27a3026c-8019-4ea1-8265-bac079e7f749 · outbound

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

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models Flamingo: a visual language model for few-shot learning

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:55:29.141780Z

Source-reported events for the cited work

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Observation 715f2511-14f1-45ad-91ea-94d41e0d9a33 · outbound

This paper cites OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models

Reference 2

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Observation ab681ece-2041-4fd9-ac45-237358c941fb · outbound

This paper cites Prompting for Multimodal Hateful Meme Classification.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models Prompting for Multimodal Hateful Meme Classification

Reference 3

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Source-reported events for the cited work

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Observation 4dae2445-6fe6-4f36-ac7e-5c76545edf3d · outbound

This paper cites Modularized networks for few-shot hateful meme detection.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models Modularized networks for few-shot hateful meme detection

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:55:29.124204Z

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Observation 8cc59cd8-b6d4-4ccb-979a-ad3a279ab324 · outbound

This paper cites Microsoft COCO Captions: Data Collection and Evaluation Server.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models Microsoft COCO Captions: Data Collection and Evaluation Server

Reference 5

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Source-reported events for the cited work

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Observation 17e15a67-b841-44b9-978a-f1a048b1bb29 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T04:55:28.118746Z

Source-reported events for the cited work

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Observation 58749952-d8c6-4df9-8cc9-7cb8672a5cda · outbound

This paper cites Deep residual learning for image recognition.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models Deep residual learning for image recognition

Reference 7

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Source-reported events for the cited work

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Observation d29ebf04-445a-4509-8709-b2dfbb4d99bb · outbound

This paper cites Recent advances in online hate speech moderation: Multimodality and the role of large models.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models Recent advances in online hate speech moderation: Multimodality and the role of large models

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:55:29.093645Z

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Observation b0a87666-f99c-40cb-ab81-2bd4a9bac7fc · outbound

This paper cites Openclip, 2021.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models Openclip, 2021

Reference 9

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Source-reported events for the cited work

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Observation c5d1c160-87dd-4d46-b1b1-721aacc15379 · outbound

This paper cites Capalign: Improving cross modal alignment via informative captioning for harmful meme detection.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models Capalign: Improving cross modal alignment via informative captioning for harmful meme detection

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:55:29.064962Z

Source-reported events for the cited work

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Observation 4784a6f9-9cad-4d84-9cc5-9d12b36057ed · outbound

This paper cites Supervised Multimodal Bitransformers for Classifying Images and Text.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models Supervised Multimodal Bitransformers for Classifying Images and Text

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-16T04:55:28.143837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90167aa6-9778-4423-819e-70e3b590095b · outbound

This paper cites The hateful memes challenge: Detecting hate speech in multimodal memes.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models The hateful memes challenge: Detecting hate speech in multimodal memes

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-16T04:55:28.149189Z

Source-reported events for the cited work

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Observation a6da361d-0501-424c-a64e-90b3f5094bed · outbound

This paper cites The hateful memes challenge: Competition report.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models The hateful memes challenge: Competition report

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:55:29.036156Z

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Observation 83116132-e221-472d-99c3-835d39beed2a · outbound

This paper cites Why is it hate speech? masked ratio- nale prediction for explainable hate speech detection.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models Why is it hate speech? masked ratio- nale prediction for explainable hate speech detection

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:55:29.017987Z

Source-reported events for the cited work

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Observation cfe61be9-246c-491d-ba36-3ce256c73838 · outbound

This paper cites Segment anything.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models Segment anything

Reference 15

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Source-reported events for the cited work

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Observation c2ca12f7-87b6-4278-a7c3-a1028ff340de · outbound

This paper cites LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day

Reference 16

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Observation 9a673173-6d41-46a3-92b8-bc454f84d70f · outbound

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

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models VisualBERT: A Simple and Performant Baseline for Vision and Language

Reference 17

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Observation 27cd51f3-2628-4dbd-b8cf-95fce6661f8f · outbound

This paper cites Towards explainable harmful meme detection through multimodal debate between large language models.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models Towards explainable harmful meme detection through multimodal debate between large language models

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:55:28.984925Z

Source-reported events for the cited work

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Observation cb0c1590-2025-4a6d-91dd-c5cd6381f695 · outbound

This paper cites A Multimodal Framework for the Detection of Hateful Memes.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models A Multimodal Framework for the Detection of Hateful Memes

Reference 19

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Source-reported events for the cited work

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Observation 12607ca3-8f04-45db-b528-c90674b99931 · outbound

This paper cites Visual Instruction Tuning.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models Visual Instruction Tuning

Reference 20

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Observation 92183d9d-f78a-4287-ae26-597e4739640c · outbound

This paper cites Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

Reference 21

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Observation 16d433d1-594f-41b7-90f4-22b6da9fb43c · outbound

This paper cites ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T04:55:28.198790Z

Source-reported events for the cited work

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Observation 81d60a65-1c98-4d21-8c04-49174206c2b1 · outbound

This paper cites Hatexplain: A benchmark dataset for explainable hate speech detection.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models Hatexplain: A benchmark dataset for explainable hate speech detection

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:55:28.966826Z

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Observation 32d06113-ec91-4234-ae51-93dbe547cd37 · outbound

This paper cites ETHOS: an Online Hate Speech Detection Dataset.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models ETHOS: an Online Hate Speech Detection Dataset

Reference 24

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Observation 3e834cf9-0d87-4b47-b0bf-850ddca0a227 · outbound

This paper cites Vilio: State-of-the-art Visio-Linguistic Models applied to Hateful Memes.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models Vilio: State-of-the-art Visio-Linguistic Models applied to Hateful Memes

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T04:55:28.213637Z

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Observation ec4bab69-10c7-4529-92a0-14085d108fb3 · outbound

This paper cites GPT-4 Technical Report.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models GPT-4 Technical Report

Reference 26

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Observation 0f8bd0e9-2177-4d03-b8dd-4e67ba36e8e1 · outbound

This paper cites Unsafe diffusion: On the generation of unsafe images and hateful memes from text-to-image models.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models Unsafe diffusion: On the generation of unsafe images and hateful memes from text-to-image models

Reference 27

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Observation 1da24017-0a3d-41c0-b2ae-fa22a565caab · outbound

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

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models Learning transferable visual models from natural language supervision

Reference 28

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This paper cites Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Reference 29

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Observation c781a117-7ef3-44fe-929b-d80fef9691a6 · outbound

This paper cites Detecting Hateful Memes Using a Multimodal Deep Ensemble.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models Detecting Hateful Memes Using a Multimodal Deep Ensemble

Reference 30

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Observation 9eda2e25-6c3e-42d7-8b47-906f7f2afa7b · outbound

This paper cites Detecting formal thought disorder by deep contextualized word representations.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models Detecting formal thought disorder by deep contextualized word representations

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:55:28.927220Z

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Observation ecba3b8f-09f6-407b-979a-efc115661f23 · outbound

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

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning

Reference 32

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This paper cites Large Language Models Encode Clinical Knowledge.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models Large Language Models Encode Clinical Knowledge

Reference 33

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Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models Resolution-robust large mask inpainting with fourier convolutions

Reference 34

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Observation e860ffdb-c39d-4dfa-878e-80d67f035e85 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models Gemini: A Family of Highly Capable Multimodal Models

Reference 35

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

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 36

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This paper cites On large visual language models for medical imaging analysis: An empirical study.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models On large visual language models for medical imaging analysis: An empirical study

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:55:28.877975Z

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Observation 68a69b57-5211-4cec-aab3-d4b763283f99 · outbound

This paper cites Detecting Hate Speech in Memes Using Multimodal Deep Learning Approaches: Prize-winning solution to Hateful Memes Challenge.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models Detecting Hate Speech in Memes Using Multimodal Deep Learning Approaches: Prize-winning solution to Hateful Memes Challenge

Reference 38

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Observation 176e6f5b-20fb-42bf-9ac9-65154d5a5c81 · outbound

This paper cites Memecraft: Contextual and stance-driven multimodal meme generation.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models Memecraft: Contextual and stance-driven multimodal meme generation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:55:28.859820Z

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Observation d48cd019-2e12-462d-9843-8ecb8b589f24 · outbound

This paper cites NExT-GPT: Any-to-Any Multimodal LLM.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models NExT-GPT: Any-to-Any Multimodal LLM

Reference 40

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Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models Aggregated residual transformations for deep neural networks

Reference 41

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Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models Coded hate speech detection via contextual information

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:55:28.830858Z

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Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models An empirical study of gpt-3 for few-shot knowledge-based vqa

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:55:28.813384Z

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Observation 041eb16d-2f38-4db5-a17d-8deb554b4ecc · outbound

This paper cites Inpaint Anything: Segment Anything Meets Image Inpainting.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models Inpaint Anything: Segment Anything Meets Image Inpainting

Reference 44

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Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention

Reference 45

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This paper cites OPT: Open Pre-trained Transformer Language Models.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models OPT: Open Pre-trained Transformer Language Models

Reference 46

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This paper cites Enhance Multimodal Transformer With External Label And In-Domain Pretrain: Hateful Meme Challenge Winning Solution.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models Enhance Multimodal Transformer With External Label And In-Domain Pretrain: Hateful Meme Challenge Winning Solution

Reference 47

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Observation d1570484-a50e-4715-a304-3ca31926a19e · outbound

This paper cites C.2 Multimodal: unimodal pretraining Multimodal models from unimodal pretraining typically combine the output or the features of vision and linguistic models.

Detecting and Mitigating Hateful Content in Multimodal Memes with Vision-Language Models C.2 Multimodal: unimodal pretraining Multimodal models from unimodal pretraining typically combine the output or the features of vision and linguistic models

Reference 768

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:55:28.795461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

No inbound Pith citation observations are available.