Paper Citation Record · LEDGER

Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition

As of 18 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2505.14143.

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

pith.paper-citation-record.v1
2505.14143 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:42:27.730663Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

27 of 27 outbound references displayed

verified exact0
verified fuzzy27
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e8575aba-1b68-4e1b-85a7-d8b4188f5e11 · outbound

This paper cites Multi-task learning for multi-modal emotion recognition and sentiment analysis,.

Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition Multi-task learning for multi-modal emotion recognition and sentiment analysis,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:34.660379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 699b33f6-380f-434d-b4d5-1d1fd6a9670c · outbound

This paper cites UniMSE: Towards unified multimodal sentiment analysis and emotion recognition,.

Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition UniMSE: Towards unified multimodal sentiment analysis and emotion recognition,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:34.442809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7576069d-5897-4c32-9950-493fc14ef6e5 · outbound

This paper cites Progres- sive layered extraction (ple): A novel multi-task learning (mtl) model for personalized recommendations,.

Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition Progres- sive layered extraction (ple): A novel multi-task learning (mtl) model for personalized recommendations,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:34.253598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b7273d97-45d6-4906-9e22-c8e8c5e18745 · outbound

This paper cites Adaptive mixtures of local experts,.

Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition Adaptive mixtures of local experts,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:34.083700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 05db8619-b891-4a18-a731-936545211943 · outbound

This paper cites Multi-task dense prediction via mixture of low-rank experts,.

Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition Multi-task dense prediction via mixture of low-rank experts,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:33.893597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cadf77c8-6043-4de7-b168-429a27b23cc8 · outbound

This paper cites Multimodal multi-loss fusion network for sentiment analysis,.

Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition Multimodal multi-loss fusion network for sentiment analysis,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:33.741535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dc87dba0-4857-4695-9e08-e7914974ba73 · outbound

This paper cites Multimodal trans- former for unaligned multimodal language sequences,.

Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition Multimodal trans- former for unaligned multimodal language sequences,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:33.543705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 693ea582-416c-4be4-8cfc-9d7b03055c21 · outbound

This paper cites Hybrid contrastive learning of tri-modal representation for multimodal sentiment analysis,.

Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition Hybrid contrastive learning of tri-modal representation for multimodal sentiment analysis,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:33.351025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1522a01e-d2e9-4688-badd-1b2f82d29d3a · outbound

This paper cites COGMEN: COntextualized GNN based multimodal emotion recognitioN,.

Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition COGMEN: COntextualized GNN based multimodal emotion recognitioN,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:33.189724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d58d2056-1a64-4acb-bff6-a1a983783812 · outbound

This paper cites Multimodal language analysis in the wild: CMU-MOSEI dataset and interpretable dynamic fusion graph,.

Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition Multimodal language analysis in the wild: CMU-MOSEI dataset and interpretable dynamic fusion graph,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:33.000598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 725f0ee8-e461-4fab-83d1-c2e276120d2d · outbound

This paper cites Demt: deformable mixer transformer for multi-task learning of dense prediction,.

Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition Demt: deformable mixer transformer for multi-task learning of dense prediction,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:32.850030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ae59820b-2d9e-4997-ac1e-07dd1dd04602 · outbound

This paper cites Mod-squad: Designing mixtures of experts as modular multi-task learners,.

Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition Mod-squad: Designing mixtures of experts as modular multi-task learners,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:32.702412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bcd54648-23a9-4874-99d5-f651fc549e2b · outbound

This paper cites Roberta: A robustly optimized bert pretraining approach,.

Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition Roberta: A robustly optimized bert pretraining approach,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:32.451989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d667f7fe-965f-4ac1-aba6-0a40fbb2ea6f · outbound

This paper cites data2vec: A general framework for self-supervised learning in speech, vision and language,.

Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition data2vec: A general framework for self-supervised learning in speech, vision and language,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:32.133267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3abe978f-ff3c-43d6-8e97-a1b762ca9d96 · outbound

This paper cites Multimodal sentiment intensity analysis in videos: Facial gestures and verbal messages,.

Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition Multimodal sentiment intensity analysis in videos: Facial gestures and verbal messages,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:31.725689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 89d94570-956f-48ef-916d-646561d371fc · outbound

This paper cites Misa: Modality-invariant and -specific representations for multimodal senti- ment analysis,.

Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition Misa: Modality-invariant and -specific representations for multimodal senti- ment analysis,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:31.399302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2b1f1b7c-0647-4939-b881-67e83ecf5021 · outbound

This paper cites Integrating multimodal information in large pretrained trans- formers,.

Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition Integrating multimodal information in large pretrained trans- formers,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:31.156265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5ab8333f-8d99-4a92-b87f-59b9c3e126c2 · outbound

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

Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition Learning modality- specific representations with self-supervised multi-task learning for multimodal sentiment analysis,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:30.851588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c6add986-6065-4a43-b75d-ad3604857e3d · outbound

This paper cites Improving multimodal fu- sion with hierarchical mutual information maximization for multimodal sentiment analysis,.

Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition Improving multimodal fu- sion with hierarchical mutual information maximization for multimodal sentiment analysis,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:30.475343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a71f8832-ac20-4e53-a686-515eefca42da · outbound

This paper cites Speech-text pre-training for spoken dialog understanding with explicit cross-modal alignment,.

Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition Speech-text pre-training for spoken dialog understanding with explicit cross-modal alignment,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:30.195138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d55e325c-436a-4eb2-8362-6b0e3d569ce9 · outbound

This paper cites Multilogue-net: A context-aware RNN for multi-modal emotion detection and sentiment analysis in conversation,.

Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition Multilogue-net: A context-aware RNN for multi-modal emotion detection and sentiment analysis in conversation,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:29.912593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3f479aab-1b97-49c3-bbc0-e91d6fdf815e · outbound

This paper cites A transformer-based joint-encoding for emotion recogni- tion and sentiment analysis,.

Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition A transformer-based joint-encoding for emotion recogni- tion and sentiment analysis,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:29.669252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8f6df33e-a7fa-4678-ba43-9f2f7fb81d5f · outbound

This paper cites Multimodal routing: Improving local and global interpretability of multimodal language analysis,.

Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition Multimodal routing: Improving local and global interpretability of multimodal language analysis,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:29.451202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5a15b4da-71e1-40a9-90bf-bccc16f81e2f · outbound

This paper cites Conversation understanding using relational temporal graph neural networks with auxiliary cross-modality interaction,.

Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition Conversation understanding using relational temporal graph neural networks with auxiliary cross-modality interaction,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:29.082172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2032773c-f0b3-4aef-8253-637567d13627 · outbound

This paper cites MELD: A multimodal multi- party dataset for emotion recognition in conversations,.

Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition MELD: A multimodal multi- party dataset for emotion recognition in conversations,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:28.765645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 56d410f6-a651-4297-b0e2-7600920dcc44 · outbound

This paper cites IEMOCAP: interactive emotional dyadic motion capture database,.

Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition IEMOCAP: interactive emotional dyadic motion capture database,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:28.412194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 388ee527-dda4-44bf-8f60-1f06277eef0f · outbound

This paper cites SimCSE: Simple contrastive learning of sentence embeddings,.

Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition SimCSE: Simple contrastive learning of sentence embeddings,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:28.086331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

No inbound Pith citation observations are available.