Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T05:02:24.778729Z
Paper Citation Record · LEDGER
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting
As of 14 August 2026, this Paper Citation Record lists 74 of 74 outbound references and 2 inbound Pith citation observations for arXiv:2508.02429.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-03T11:19:34.140857Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-15T13:55:53.243562Z
Reference resolution
74 of 74 outbound references displayed
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 97ea4dbf-5925-42e3-87e0-8cbc9aab1064 · outbound
This paper cites A review of affective computing: From unimodal analysis to multimodal fusion,.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting A review of affective computing: From unimodal analysis to multimodal fusion,
Reference 1
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Observation 11db27f3-e04a-4364-a9a2-a32fe4fb8e4a · outbound
This paper cites A systematic review on affective computing: Emotion models, databases, and recent advances,.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting A systematic review on affective computing: Emotion models, databases, and recent advances,
Reference 2
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Observation de78fbb7-3a1d-4f76-84a8-a9aca737d6bd · outbound
This paper cites An effective data fusion methodology for multi-modal emotion recognition: A survey,.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting An effective data fusion methodology for multi-modal emotion recognition: A survey,
Reference 3
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Observation b7525118-6794-41e5-a078-930cd45e178b · outbound
This paper cites Surveying the mllm landscape: A meta-review of current surveys,.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Surveying the mllm landscape: A meta-review of current surveys,
Reference 4
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Observation f1b077f7-53f5-49e0-b917-d81568e64559 · outbound
This paper cites Learning by comparing: Boosting multi- modal affective computing through ordinal learning,.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Learning by comparing: Boosting multi- modal affective computing through ordinal learning,
Reference 5
- Resolution
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Observation e75fac92-5937-4d9f-99cf-2ab6992642da · outbound
This paper cites Llm-based nlg evaluation: Current status and challenges,.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Llm-based nlg evaluation: Current status and challenges,
Reference 6
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Observation f3f1bbd2-9bca-4223-b3f0-c63677aba923 · outbound
This paper cites The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision).
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)
Reference 7
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Observation 81d76dc5-c191-4d76-9687-2d6e99d50007 · outbound
This paper cites Visual instruction tuning,.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Visual instruction tuning,
Reference 8
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Observation 0fe48994-3171-4398-b5a2-43388ce28bca · outbound
This paper cites Gemini: A Family of Highly Capable Multimodal Models.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Gemini: A Family of Highly Capable Multimodal Models
Reference 9
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Observation bd611aaf-b1b6-4e25-96de-8140eb598eae · outbound
This paper cites Qwen-vl: A versatile vision-language model for understanding, localization,.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Qwen-vl: A versatile vision-language model for understanding, localization,
Reference 10
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Source-reported events for the cited work
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Observation df5a41c4-2648-4435-a407-e74517b4513f · outbound
This paper cites A survey on multimodal large language models,.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting A survey on multimodal large language models,
Reference 11
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Observation b0d994dc-4d41-410e-91a1-54be9d9d8e9a · outbound
This paper cites EmotionQueen: A Benchmark for Evaluating Empathy of Large Language Models.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting EmotionQueen: A Benchmark for Evaluating Empathy of Large Language Models
Reference 12
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Observation 54470714-89de-4455-97f4-f8f45267dceb · outbound
This paper cites Eemo-bench: A benchmark for multi-modal large lan- guage models on image evoked emotion assessment,.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Eemo-bench: A benchmark for multi-modal large lan- guage models on image evoked emotion assessment,
Reference 13
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Observation d1c5d6d0-bdc4-4b2a-8647-0b82c892956b · outbound
This paper cites Can Large Language Models Help Multimodal Language Analysis? MMLA: A Comprehensive Benchmark.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Can Large Language Models Help Multimodal Language Analysis? MMLA: A Comprehensive Benchmark
Reference 14
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Source-reported events for the cited work
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Observation 42d0d87b-261b-41e3-8ed8-a2c60f5f6119 · outbound
This paper cites The Future of MLLM Prompting is Adaptive: A Comprehensive Experimental Evaluation of Prompt Engineering Methods for Robust Multimodal Performance.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting The Future of MLLM Prompting is Adaptive: A Comprehensive Experimental Evaluation of Prompt Engineering Methods for Robust Multimodal Performance
Reference 15
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This paper cites MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos
Reference 16
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Observation 21db1851-7af0-41ee-ba55-5898ff420f84 · outbound
This paper cites Ch- sims: A chinese multimodal sentiment analysis dataset with fine-grained annotation of modality,.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Ch- sims: A chinese multimodal sentiment analysis dataset with fine-grained annotation of modality,
Reference 17
- Resolution
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Source-reported events for the cited work
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Observation 3309977f-1b35-4894-a074-94440234caa7 · outbound
This paper cites Make acoustic and visual cues matter: Ch-sims v2. 0 dataset and av-mixup consistent module,.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Make acoustic and visual cues matter: Ch-sims v2. 0 dataset and av-mixup consistent module,
Reference 18
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Observation d915f76c-e091-4c4e-8496-012f69e9870b · outbound
This paper cites MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
Reference 19
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Observation 0855267a-b265-4c1c-838b-74924086cff2 · outbound
This paper cites UR-FUNNY: A Multimodal Language Dataset for Understanding Humor.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting UR-FUNNY: A Multimodal Language Dataset for Understanding Humor
Reference 20
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Observation fd73413d-c3a3-48cf-b9f0-4314da5146bc · outbound
This paper cites Generated Knowledge Prompting for Commonsense Reasoning.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Generated Knowledge Prompting for Commonsense Reasoning
Reference 21
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Source-reported events for the cited work
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Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Self-attentive feature-level fusion for multimodal emotion detection,
Reference 22
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Source-reported events for the cited work
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Observation adfaf75b-99c2-44bb-a848-01b9d6828b6c · outbound
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Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Emotion recognition using feature-level fusion of facial expressions and body gestures,
Reference 23
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Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Decision-level fusion method for emotion recognition using multimodal emotion recog- nition information,
Reference 24
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Source-reported events for the cited work
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Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Deep learning-based late fusion of multi- modal information for emotion classification of music video,
Reference 25
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Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting A joint cross-attention model for audio-visual fusion in dimensional emotion recognition,
Reference 26
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Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Speech emotion recognition with co-attention based multi-level acoustic information,
Reference 27
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Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Omni-Emotion: Extending Video MLLM with Detailed Face and Audio Modeling for Multimodal Emotion Analysis
Reference 28
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Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting OmniVox: Zero-Shot Emotion Recognition with Omni-LLMs
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Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Mellm: Exploring llm-powered micro-expression understanding enhanced by subtle motion perception,
Reference 30
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Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Learning transferable visual models from natural language supervision,
Reference 31
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Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Videomae: Masked autoen- coders are data-efficient learners for self-supervised video pre-training,
Reference 32
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Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting BEATs: Audio Pre-Training with Acoustic Tokenizers
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Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Blip-2: Bootstrapping language- image pre-training with frozen image encoders and large language models,
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Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting SALMONN: Towards Generic Hearing Abilities for Large Language Models
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Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models
Reference 36
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Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting MiniCPM-V: A GPT-4V Level MLLM on Your Phone
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Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting HumanOmni: A Large Vision-Speech Language Model for Human-Centric Video Understanding
Reference 38
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Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Ola: Pushing the frontiers of omni-modal language model with progressive modality alignment,
Reference 39
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Observation ce2dff1b-3623-49ca-8b58-96642e13d7f8 · outbound
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Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Qwen2.5-Omni Technical Report
Reference 40
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Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Learning emotional prompt features with multiple views for visual emotion analysis,
Reference 41
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This paper cites Visual and textual prompts in vllms for enhancing emotion recognition,.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Visual and textual prompts in vllms for enhancing emotion recognition,
Reference 42
- Resolution
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Source-reported events for the cited work
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Observation 86ed16c1-d44c-4cd9-80cf-0b421f060ff1 · outbound
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Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Multimodal language analysis in the wild: Cmu-mosei dataset and interpretable dynamic fusion graph,
Reference 43
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Source-reported events for the cited work
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Observation b4ec7c38-d45f-40db-9361-da55708317ab · outbound
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Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs
Reference 44
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Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Emotion-llama: Multimodal emotion recognition and reasoning with instruction tuning,
Reference 45
- Resolution
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Source-reported events for the cited work
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Observation a138cafe-fb39-4808-a7e6-ba7c95b95a7d · outbound
This paper cites PandaGPT: One Model To Instruction-Follow Them All.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting PandaGPT: One Model To Instruction-Follow Them All
Reference 46
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 42933506-5da1-4470-bf52-7a1a9093cb09 · outbound
This paper cites Lora: Low-rank adaptation of large language models.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Lora: Low-rank adaptation of large language models
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:02:27.979157Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 23935ab7-5e3e-4cd0-bf5e-dfdfc236d63f · outbound
This paper cites Multimodal information bottleneck: Learning minimal sufficient unimodal and multimodal representations,.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Multimodal information bottleneck: Learning minimal sufficient unimodal and multimodal representations,
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:02:27.838128Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation f4ade031-da1b-491c-a62b-695a1d283a13 · outbound
This paper cites Injecting multimodal informa- tion into pre-trained language model for multimodal sentiment analysis,.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Injecting multimodal informa- tion into pre-trained language model for multimodal sentiment analysis,
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:02:27.684303Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 9364ce03-6343-4e38-8aba-e1f5a2243737 · outbound
This paper cites Towards Explainable Fusion and Balanced Learning in Multimodal Sentiment Analysis.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Towards Explainable Fusion and Balanced Learning in Multimodal Sentiment Analysis
Reference 50
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T05:02:25.031605Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 07d8b9bb-8567-4c82-b9d9-4716b492c995 · outbound
This paper cites Hgtfm: Hierarchical gating-driven transformer fusion model for robust multimodal sentiment analysis,.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Hgtfm: Hierarchical gating-driven transformer fusion model for robust multimodal sentiment analysis,
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:02:27.533321Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 19298fd8-686c-4506-ab67-daa8ca63f8cf · outbound
This paper cites End-to-end Semantic-centric Video-based Multimodal Affective Computing.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting End-to-end Semantic-centric Video-based Multimodal Affective Computing
Reference 52
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T05:02:24.999298Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 721e03b3-f413-49f6-b0d5-41838fea01f9 · outbound
This paper cites LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Reference 53
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 50dd4fc4-8c65-4259-999c-7920a4cd450d · outbound
This paper cites FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning
Reference 54
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ef9dcab4-4f63-40a7-a256-d0d59273805d · outbound
This paper cites Divide, conquer and combine: Hierarchical feature fusion network with local and global perspectives for multimodal affective computing,.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Divide, conquer and combine: Hierarchical feature fusion network with local and global perspectives for multimodal affective computing,
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:02:27.338026Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation e1fd8b1b-21fa-48b1-9fa3-0f4edd6ef6ae · outbound
This paper cites Qwen2.5-Coder Technical Report.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Qwen2.5-Coder Technical Report
Reference 56
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 85140fb3-e5f7-4992-910b-cca5bdd125be · outbound
This paper cites Robust speech recognition via large-scale weak supervi- sion,.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Robust speech recognition via large-scale weak supervi- sion,
Reference 57
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8c2e31f1-ad87-48ef-89f8-083ee16eb6b6 · outbound
This paper cites Qwen2.5-VL Technical Report.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Qwen2.5-VL Technical Report
Reference 58
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T05:02:24.671751Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4f549136-c179-4339-8972-b9a90ce551b8 · outbound
This paper cites A survey on vision transformer,.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting A survey on vision transformer,
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:02:27.174956Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 4e5f5b46-67cf-4e38-9266-19f98ad5916a · outbound
This paper cites Sigmoid loss for language image pre-training,.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Sigmoid loss for language image pre-training,
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:02:27.008775Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 8b1132f2-adf1-4797-9d46-f0ba22dcad40 · outbound
This paper cites LLaVA-OneVision: Easy Visual Task Transfer.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting LLaVA-OneVision: Easy Visual Task Transfer
Reference 61
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T05:02:24.688796Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b95fbbae-b9c3-43f3-8cf9-7f2c29168dbd · outbound
This paper cites Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution
Reference 62
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T05:02:24.696608Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 39714fe6-3995-4275-b61f-95aee8211602 · outbound
This paper cites Qwen2 Technical Report.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Qwen2 Technical Report
Reference 63
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T05:02:24.703321Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5548e265-db02-49f1-82f6-2416ffd59237 · outbound
This paper cites Imagebind: One embedding space to bind them all,.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Imagebind: One embedding space to bind them all,
Reference 64
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:02:26.848544Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 3fa0f8c0-2f9e-4206-a2a3-56bda65f93b1 · outbound
This paper cites Hubert: Self-supervised speech representation learning by masked prediction of hidden units,.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Hubert: Self-supervised speech representation learning by masked prediction of hidden units,
Reference 65
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T05:02:24.717254Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 263337ce-aea8-41fb-9da2-b727ba36fc0d · outbound
This paper cites Mae-dfer: Efficient masked au- toencoder for self-supervised dynamic facial expression recognition,.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Mae-dfer: Efficient masked au- toencoder for self-supervised dynamic facial expression recognition,
Reference 66
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:02:26.617560Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 0b18373b-846d-452e-9c72-040a0914caa9 · outbound
This paper cites Eva: Exploring the limits of masked visual representation learning at scale,.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Eva: Exploring the limits of masked visual representation learning at scale,
Reference 67
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:02:26.349226Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 74da8b45-edc1-4c74-897f-a14243688431 · outbound
This paper cites LLaMA: Open and Efficient Foundation Language Models.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting LLaMA: Open and Efficient Foundation Language Models
Reference 68
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T05:02:24.737622Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cde85087-202c-4242-87b8-d29ff4cbc18a · outbound
This paper cites an unresolved cited work.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Unresolved cited work
Reference 69
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T05:02:26.217538Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 917be28d-0b19-4dab-b624-cd745e496966 · outbound
This paper cites an unresolved cited work.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Unresolved cited work
Reference 70
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T05:02:26.031375Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 311dbcf4-fc63-4d0f-8ad0-e80cfbc0cb9b · outbound
This paper cites an unresolved cited work.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Unresolved cited work
Reference 71
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T05:02:25.873698Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation abe15f6e-c07c-4049-80bb-ed134b901b3a · outbound
This paper cites an unresolved cited work.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Unresolved cited work
Reference 72
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T05:02:25.826168Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 99597bf0-f020-44c7-84c0-680b7b5a62ee · outbound
This paper cites Its core architecture follows the Thinker-Talker design.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Its core architecture follows the Thinker-Talker design
Reference 73
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:02:25.786279Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 27a231c1-e83b-44f8-b2fd-748b9bd889d0 · outbound
This paper cites Its key innovation lies in the ability to simultaneously process visual and speech information in human-centric scenes.
Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting Its key innovation lies in the ability to simultaneously process visual and speech information in human-centric scenes
Reference 74
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T05:02:25.746097Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Pith citing papers
Observation 8b65755c-ea84-4a75-8437-4557e50e4303 · inbound
QASA: Quality-Aware Semantic Augmentation for Robust Multimodal Sentiment Analysis cites this paper.
QASA: Quality-Aware Semantic Augmentation for Robust Multimodal Sentiment Analysis Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T11:19:34.140857Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 73165972-423e-47ac-9fdc-f2fb77c25237 · inbound
C2F-Thinker: Coarse-to-Fine Reasoning with Hint-Guided Reinforcement Learning for Multimodal Sentiment Analysis cites this paper.
C2F-Thinker: Coarse-to-Fine Reasoning with Hint-Guided Reinforcement Learning for Multimodal Sentiment Analysis Multimodal Large Language Models for End-to-End Affective Computing: Benchmarking and Boosting with Generative Knowledge Prompting
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T13:55:53.245819Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.