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Paper Citation Record · LEDGER

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding

As of 15 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 1 inbound Pith citation observation for arXiv:2412.08049.

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

pith.paper-citation-record.v1
2412.08049 v3

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T18:21:40.130112Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T08:26:49.097526Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

35 of 35 outbound references displayed

  • verified exact1
  • verified fuzzy13
  • unresolved21
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 38cfe572-9de1-4a26-bfed-f833afb4993a · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 1

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source=pdf_text observed=2026-08-11T18:21:39.877031Z digest=sha256:fee94aba522317a0c724bc2dd8df94431191600a16b1223869e917f8644a823d

Observation ad6fd5f2-348b-483b-a391-bf0c003a45e0 · outbound

This paper cites Semi-supervised mul- timodal emotion recognition with expression mae.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding Semi-supervised mul- timodal emotion recognition with expression mae

Reference 4

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raw_fallback, observed 2026-08-11T18:21:40.856845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T18:21:39.894745Z digest=sha256:25cb56f0bcf349dee3ff6da223a846ccd9fb836b5e803d119a892bde9fa4944c

Observation 1cbf995a-8d64-4d85-a7eb-727bc0427149 · outbound

This paper cites Emotion-LLaMA: Multimodal Emotion Recognition and Reasoning with Instruction Tuning.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding Emotion-LLaMA: Multimodal Emotion Recognition and Reasoning with Instruction Tuning

Reference 5

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source=pdf_text observed=2026-08-11T18:21:39.900836Z digest=sha256:63668e0e398c9cd3430a1f231c6a68806e993860314e4fce618e7db2d9513cd5

Observation c7af2a0a-c42c-466e-b75d-ef422d42529a · outbound

This paper cites VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs

Reference 6

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source=pdf_text observed=2026-08-11T18:21:39.906511Z digest=sha256:1b07aedd5dd9b5ec950eb6459a69959eb4dde7529637535c36604e458cac1d69

Observation 0b78fe39-4f5a-42d6-9d35-83871aea5406 · outbound

This paper cites An image is worth 16x16 words: Trans- formers for image recognition at scale,.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding An image is worth 16x16 words: Trans- formers for image recognition at scale,

Reference 7

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verified fuzzy
raw_fallback, observed 2026-08-11T18:21:40.841153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T18:21:39.913103Z digest=sha256:f9e258e4ecfea4b0527c298ca28f36425cdddf7ed13871503416458264693b29

Observation 13600439-a05d-4b2f-987f-c6fea71f90f6 · outbound

This paper cites Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis

Reference 10

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:21:39.935774Z digest=sha256:934009bc76e1131821503a4d714367ed444064ec57679818f4a3092249952a83

Observation 63ce4830-d61d-405d-a5b2-2e7622a1c93e · outbound

This paper cites Misa: Modality-invariant and-specific representations for multimodal sentiment analysis.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding Misa: Modality-invariant and-specific representations for multimodal sentiment analysis

Reference 11

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source=pdf_text observed=2026-08-11T18:21:39.944300Z digest=sha256:2ea4c6032f0acb45ab10df88e05bedc32723cbe1f3e876a889c5e930bae989d1

Observation 4646f9c1-2410-4214-be60-911b2b7631d2 · outbound

This paper cites DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations

Reference 13

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source=pdf_text observed=2026-08-11T18:21:39.960892Z digest=sha256:ef51a066e4a9900ffe29a6ad08825b3d450240faa5aaba6679d69fbd5515ad49

Observation c8f09bc7-6a46-461f-99c7-de298d0f8202 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding LoRA: Low-Rank Adaptation of Large Language Models

Reference 14

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:21:39.970411Z digest=sha256:92faa58fd1b72154fdce352a923424aa2c1e71d5530981f1090ed2b715cc4ff5

Observation 6ee4b11e-4ac7-4cae-a8ca-2714cf3d90d0 · outbound

This paper cites MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation

Reference 15

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source=pdf_text observed=2026-08-11T18:21:39.977064Z digest=sha256:36432a939dae73fd05aa478c45d2bed649f2fd7ca31e72eeb611d65977e4ee6a

Observation 8548ba0b-19cd-4b6f-9926-edaa57f94683 · outbound

This paper cites Mm-dfn: Multimodal dy- namic fusion network for emotion recognition in con- versations.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding Mm-dfn: Multimodal dy- namic fusion network for emotion recognition in con- versations

Reference 16

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raw_fallback, observed 2026-08-11T18:21:40.783500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T18:21:39.990244Z digest=sha256:f226431c9f71c7eb8aef880ccbee6f77749966460cc8135ab147ca3d14cfcee0

Observation 46fe8cbc-802e-4a71-aee9-49035aa7d748 · outbound

This paper cites Dfew: A large-scale database for recognizing dynamic facial expressions in the wild.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding Dfew: A large-scale database for recognizing dynamic facial expressions in the wild

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T18:21:40.001853Z digest=sha256:4346099c9a91995458cc9da9f856c9ef53d488b99f87232707d7ce25141d933b

Observation 84d3364f-fb6b-4ade-b9be-fc82fdf8daf4 · outbound

This paper cites InstructERC: Reforming Emotion Recognition in Conversation with Multi-task Retrieval-Augmented Large Language Models.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding InstructERC: Reforming Emotion Recognition in Conversation with Multi-task Retrieval-Augmented Large Language Models

Reference 19

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source=pdf_text observed=2026-08-11T18:21:40.008378Z digest=sha256:b1f4053d4904a60025e668c2ae940e467468e3ae526e01e35ca3af85777045ac

Observation f29ba094-f9f0-41b5-bada-ecdee5db2065 · outbound

This paper cites EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognition.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognition

Reference 20

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source=pdf_text observed=2026-08-11T18:21:40.017251Z digest=sha256:bb263ddedcaa86edfaf980192c8af44a209ac591445544faa25b23097f342525

Observation fd9823b1-df60-478f-bbc9-7b7aa1a56cfd · outbound

This paper cites Explainable mul- timodal emotion recognition,.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding Explainable mul- timodal emotion recognition,

Reference 21

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raw_fallback, observed 2026-08-11T18:21:40.751483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T18:21:40.031448Z digest=sha256:05a798b5dd69764ae17f60b585c52e29cee0b066f66974a8ef4e092d622117b3

Observation aab7339c-115e-4cb4-a2f8-80ec527ca8e0 · outbound

This paper cites Visual instruction tuning.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding Visual instruction tuning

Reference 22

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-11T18:21:40.036712Z digest=sha256:669caca98fec7ba0a2834d42b5f8aca6a29a97cff28b6a4d368c759f7cf24e1a

Observation e7c415b4-e5f2-4904-89b9-2e90276cd614 · outbound

This paper cites Progressive modality re- inforcement for human multimodal emotion recognition from unaligned multimodal sequences.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding Progressive modality re- inforcement for human multimodal emotion recognition from unaligned multimodal sequences

Reference 23

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source=pdf_text observed=2026-08-11T18:21:40.047875Z digest=sha256:4d1c6a9901bc1cc9aca0c84633fceef038a3edbda5b96f4a4842125d0e03055c

Observation 1444384f-54c8-484c-b745-b60e68337c55 · outbound

This paper cites MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations

Reference 24

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source=pdf_text observed=2026-08-11T18:21:40.053544Z digest=sha256:010c9b55d6eb6d4e0ab32f08fe56554ebe1b32f8adc63b71c9f915b3a7c9f6df

Observation 72734523-fcd9-4099-a78d-cbd198eff2d7 · outbound

This paper cites A discourse-aware graph neural network for emotion recog- nition in multi-party conversation.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding A discourse-aware graph neural network for emotion recog- nition in multi-party conversation

Reference 26

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verified fuzzy
raw_fallback, observed 2026-08-11T18:21:40.715435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T18:21:40.067954Z digest=sha256:928d57175a0eadad5575fc9afffdcad0edad21970a252692ae62044ca2bd1f1e

Observation f3f173ee-91d7-47db-a27f-83203db5072d · outbound

This paper cites Multimodal transformer for un- aligned multimodal language sequences.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding Multimodal transformer for un- aligned multimodal language sequences

Reference 27

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source=pdf_text observed=2026-08-11T18:21:40.074867Z digest=sha256:44917aa62a96e475b7bf2022f5609186adfcf52e75f202747a731e503335f0f7

Observation 5f4cf55e-d52b-48d9-ac13-d5517cca97e5 · outbound

This paper cites SemEval-2024 task 3: Multimodal emotion cause analysis in conversations.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding SemEval-2024 task 3: Multimodal emotion cause analysis in conversations

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T18:21:40.084097Z digest=sha256:927e7d208422f049508f3f6c0abb6ad79a9739eebd3317ccda6c8cbd093f7aa8

Observation bdb5e7d8-6c67-443f-8fbc-bfb831e57274 · outbound

This paper cites Confede: Contrastive feature de- composition for multimodal sentiment analysis.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding Confede: Contrastive feature de- composition for multimodal sentiment analysis

Reference 29

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verified fuzzy
raw_fallback, observed 2026-08-11T18:21:40.657080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T18:21:40.091473Z digest=sha256:d29a733315c8a569b64d966407c1b5cca1bec51a72c7012bd559ccba5a8b7ee9

Observation 7fb9be27-7497-445a-948e-a62133d6c9e9 · outbound

This paper cites Learning modality-specific representations with self- supervised multi-task learning for multimodal sentiment analysis.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding Learning modality-specific representations with self- supervised multi-task learning for multimodal sentiment analysis

Reference 30

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verified fuzzy
raw_fallback, observed 2026-08-11T18:21:40.640279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T18:21:40.097739Z digest=sha256:bb785f5e8f9f3ad924602feb31988b04bbea723ef9c3e24c274ca73b4e7bfbcb

Observation 22fa6b69-d2a6-4559-97c4-8d1b672a22b7 · outbound

This paper cites ConKI: Contrastive Knowledge Injection for Multimodal Sentiment Analysis.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding ConKI: Contrastive Knowledge Injection for Multimodal Sentiment Analysis

Reference 31

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verified exact
local_arxiv, observed 2026-08-11T18:21:40.213308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T18:21:40.104909Z digest=sha256:e3f046e39bd4ccb223d18eb7cb5c7bd3de228e58949bc9740947e16d7df167e7

Observation 4d621abb-ab80-451c-8467-9752863ab3af · outbound

This paper cites Multimodal language analysis in the wild: Cmu- mosei dataset and interpretable dynamic fusion graph.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding Multimodal language analysis in the wild: Cmu- mosei dataset and interpretable dynamic fusion graph

Reference 32

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source=pdf_text observed=2026-08-11T18:21:40.111189Z digest=sha256:601ad750f284dc122fbbfbf145b1b882d35ed101278d0a78ee0e87400c962692

Observation 3309d835-496c-4d93-905d-9ae35c6d2b95 · outbound

This paper cites A multitask learning model for multimodal sarcasm, sentiment and emotion recognition in conversa- tions.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding A multitask learning model for multimodal sarcasm, sentiment and emotion recognition in conversa- tions

Reference 33

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verified fuzzy
raw_fallback, observed 2026-08-11T18:21:40.601901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T18:21:40.116921Z digest=sha256:1bb6e3933bc8f3febb413a4c0570194e2cc292df43768da935ca16045042063e

Observation 31a005c2-9ab3-41f9-9704-da2a26c1485a · outbound

This paper cites LLaVA-Video: Video Instruction Tuning With Synthetic Data.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding LLaVA-Video: Video Instruction Tuning With Synthetic Data

Reference 34

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source=pdf_text observed=2026-08-11T18:21:40.124826Z digest=sha256:b8d17f12583e7ff57394c04b4d1877f7a7274c6cdd7416f02948bdeb235d6e09

Observation f9f1515f-ce4e-4ca9-9a9e-d649395dfb71 · outbound

This paper cites A facial expression-aware multimodal multi- task learning framework for emotion recognition in multi- party conversations.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding A facial expression-aware multimodal multi- task learning framework for emotion recognition in multi- party conversations

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T18:21:40.577739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T18:21:40.130112Z digest=sha256:6149201bca5f6a3436bd8f76b8aefead437c77aa75991a8b4ca21bd2f51f3319

Observation 4df90b3c-8f35-4f47-b85a-3bbdafb567b0 · outbound

This paper cites Directed Acyclic Graph Network for Conversational Emotion Recognition.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding Directed Acyclic Graph Network for Conversational Emotion Recognition

Reference 2018

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source=pdf_text observed=2026-08-11T18:21:40.061006Z digest=sha256:22f4943cbc23720f08d3957147ff9327d1573da544d15ce12a16462472b23f53

Observation be1debd5-d809-40d3-ae3a-f8ebf9f8cfb2 · outbound

This paper cites Bi-bimodal modality fusion for correlation-controlled multimodal sentiment analysis.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding Bi-bimodal modality fusion for correlation-controlled multimodal sentiment analysis

Reference 2019

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verified fuzzy
raw_fallback, observed 2026-08-11T18:21:40.824020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T18:21:39.929705Z digest=sha256:9f1ea96aca877a22831e8cee00dc4e5e52b064f3c3c8ec5c1c22f138c45ea3fc

Observation 91e507e8-8cc7-41c2-92a5-72d86bb8a72d · outbound

This paper cites Hubert: Self- supervised speech representation learning by masked pre- diction of hidden units.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding Hubert: Self- supervised speech representation learning by masked pre- diction of hidden units

Reference 2020

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T18:21:40.797771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T18:21:39.951671Z digest=sha256:fce06bd7c20e5efb34de69a9e6d10489202934d1a436b09191bcc7fd4463b163

Observation 37ab2d83-624c-47cc-9c87-69ac303faf45 · outbound

This paper cites DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation

Reference 2021

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:21:39.924087Z digest=sha256:d545ec2e3fd5bdd0a3a6b1536d3e8627da684264d82dc9de34f2c4dde5bc1700

Observation 30395d18-969e-4706-942a-949d16027849 · outbound

This paper cites UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition

Reference 2022

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source=pdf_text observed=2026-08-11T18:21:39.994601Z digest=sha256:a97020aa3abe4be7e6d0c5a1779fcb9db22364761cf59c3ad15d52023dd38352

Observation ea631df8-e4ad-4d4f-89bd-cdf051da4453 · outbound

This paper cites How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites

Reference 2023

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:21:39.888754Z digest=sha256:ea02b467f3574eb86f7fcc4e773b81a1671c438b34d076349146eaeb82783dc0

Observation 39ffd1b8-6266-4116-a151-0f461cff5335 · outbound

This paper cites MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning.

EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning

Reference 2024

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no resolver link, observed 2026-08-11T18:21:39.882645Z

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source=pdf_text observed=2026-08-11T18:21:39.882645Z digest=sha256:999f992a5b8dcc8e18e7135f0055a300d486571a675ac09c1039f94db8e4f459

Pith citing papers

Observation 0770b773-f9f8-486f-8a63-2e77ea8dca96 · inbound

Empathic Prompting: Non-Verbal Context Integration for Multimodal LLM Conversations cites this paper.

Empathic Prompting: Non-Verbal Context Integration for Multimodal LLM Conversations EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understanding

Reference 30

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no resolver link, observed 2026-08-04T08:26:49.097526Z

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