{"list":"other","total":124,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"id":"2010.11929","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","created":"2020-10-22","cited_by_count":30186},{"id":"2303.08774","title":"GPT-4 Technical Report","created":"2023-03-15","cited_by_count":18396},{"id":"1910.10683","title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","created":"2019-10-23","cited_by_count":14599},{"id":"2302.13971","title":"LLaMA: Open and Efficient Foundation Language Models","created":"2023-02-27","cited_by_count":14132},{"id":"1907.11692","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","created":"2019-07-26","cited_by_count":13372},{"id":"2307.09288","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","created":"2023-07-18","cited_by_count":13028},{"id":"2304.08485","title":"Visual Instruction Tuning","created":"2023-04-17","cited_by_count":7869},{"id":"1905.11946","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","created":"2019-05-28","cited_by_count":6442},{"id":"2301.12597","title":"BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models","created":"2023-01-30","cited_by_count":6137},{"id":"1910.13461","title":"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension","created":"2019-10-29","cited_by_count":5893},{"id":"2312.00752","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","created":"2023-12-01","cited_by_count":4718},{"id":"2204.14198","title":"Flamingo: a Visual Language Model for Few-Shot Learning","created":"2022-04-29","cited_by_count":4386},{"id":"1902.00751","title":"Parameter-Efficient Transfer Learning for NLP","created":"2019-02-02","cited_by_count":4236},{"id":"2212.04356","title":"Robust Speech Recognition via Large-Scale Weak Supervision","created":"2022-12-06","cited_by_count":4057},{"id":"2101.00190","title":"Prefix-Tuning: Optimizing Continuous Prompts for Generation","created":"2021-01-01","cited_by_count":3915},{"id":"2006.11477","title":"wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations","created":"2020-06-20","cited_by_count":3905},{"id":"2109.01652","title":"Finetuned Language Models Are Zero-Shot Learners","created":"2021-09-03","cited_by_count":3780},{"id":"1310.4546","title":"Distributed Representations of Words and Phrases and their Compositionality","created":"2013-10-16","cited_by_count":3474},{"id":"2310.06825","title":"Mistral 7B","created":"2023-10-10","cited_by_count":3164},{"id":"2305.06500","title":"InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning","created":"2023-05-11","cited_by_count":2876},{"id":"2106.07447","title":"HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units","created":"2021-06-14","cited_by_count":2342},{"id":"2308.12966","title":"Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond","created":"2023-08-24","cited_by_count":2224},{"id":"1908.02265","title":"ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks","created":"2019-08-06","cited_by_count":2222},{"id":"2107.07651","title":"Align before Fuse: Vision and Language Representation Learning with Momentum Distillation","created":"2021-07-16","cited_by_count":1619},{"id":"2205.01917","title":"CoCa: Contrastive Captioners are Image-Text Foundation Models","created":"2022-05-04","cited_by_count":1274},{"id":"1612.03975","title":"ConceptNet 5.5: An Open Multilingual Graph of General Knowledge","created":"2016-12-12","cited_by_count":1067},{"id":"1705.09406","title":"Multimodal Machine Learning: A Survey and Taxonomy","created":"2017-05-26","cited_by_count":1013},{"id":"2110.04366","title":"Towards a Unified View of Parameter-Efficient Transfer Learning","created":"2021-10-08","cited_by_count":872},{"id":"1707.08114","title":"A Survey on Multi-Task Learning","created":"2017-07-25","cited_by_count":840},{"id":"1906.00295","title":"Multimodal Transformer for Unaligned Multimodal Language Sequences","created":"2019-06-01","cited_by_count":645},{"id":"1810.02508","title":"MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations","created":"2018-10-05","cited_by_count":643},{"id":"2104.01778","title":"AST: Audio Spectrogram Transformer","created":"2021-04-05","cited_by_count":643},{"id":"2208.10442","title":"Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks","created":"2022-08-22","cited_by_count":511},{"id":"1906.05317","title":"COMET: Commonsense Transformers for Automatic Knowledge Graph Construction","created":"2019-06-12","cited_by_count":450},{"id":"1707.07250","title":"Tensor Fusion Network for Multimodal Sentiment Analysis","created":"2017-07-23","cited_by_count":433},{"id":"2104.11178","title":"VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text","created":"2021-04-22","cited_by_count":429},{"id":"2111.02358","title":"VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts","created":"2021-11-03","cited_by_count":425},{"id":"2003.10580","title":"Meta Pseudo Labels","created":"2020-03-23","cited_by_count":374},{"id":"2005.03545","title":"MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis","created":"2020-05-07","cited_by_count":265},{"id":"2002.06353","title":"UniVL: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation","created":"2020-02-15","cited_by_count":259},{"id":"2301.04856","title":"Multimodal Deep Learning","created":"2023-01-12","cited_by_count":258},{"id":"2201.05966","title":"UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models","created":"2022-01-16","cited_by_count":231},{"id":"2203.10020","title":"Challenges and Strategies in Cross-Cultural NLP","created":"2022-03-18","cited_by_count":218},{"id":"2001.01526","title":"Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification","created":"2020-01-06","cited_by_count":215},{"id":"1908.11540","title":"DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation","created":"2019-08-30","cited_by_count":186},{"id":"2102.04830","title":"Learning Modality-Specific Representations with Self-Supervised Multi-Task Learning for Multimodal Sentiment Analysis","created":"2021-02-09","cited_by_count":180},{"id":"2109.00412","title":"Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis","created":"2021-09-01","cited_by_count":131},{"id":"2008.05924","title":"DFEW: A Large-Scale Database for Recognizing Dynamic Facial Expressions in the Wild","created":"2020-08-13","cited_by_count":128},{"id":"2107.06779","title":"MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation","created":"2021-07-14","cited_by_count":99},{"id":"2106.04300","title":"A Unified Generative Framework for Aspect-Based Sentiment Analysis","created":"2021-06-08","cited_by_count":97},{"id":"1806.07110","title":"Modality Distillation with Multiple Stream Networks for Action Recognition","created":"2018-06-19","cited_by_count":83},{"id":"1911.05544","title":"Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis","created":"2019-11-13","cited_by_count":82},{"id":"2303.13802","title":"Decoupled Multimodal Distilling for Emotion Recognition","created":"2023-03-24","cited_by_count":76},{"id":"2211.11256","title":"UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition","created":"2022-11-21","cited_by_count":61},{"id":"2005.00206","title":"TransOMCS: From Linguistic Graphs to Commonsense Knowledge","created":"2020-05-01","cited_by_count":52},{"id":"2203.02385","title":"MM-DFN: Multimodal Dynamic Fusion Network for Emotion Recognition in Conversations","created":"2022-03-04","cited_by_count":52},{"id":"2205.10237","title":"M3ED: Multi-modal Multi-scene Multi-label Emotional Dialogue Database","created":"2022-05-09","cited_by_count":50},{"id":"2203.13504","title":"EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognition","created":"2022-03-25","cited_by_count":48},{"id":"1911.07848","title":"Modality to Modality Translation: An Adversarial Representation Learning and Graph Fusion Network for Multimodal Fusion","created":"2019-11-18","cited_by_count":45},{"id":"1808.03920","title":"Multimodal Language Analysis with Recurrent Multistage Fusion","created":"2018-08-12","cited_by_count":44},{"id":"2209.02604","title":"Make Acoustic and Visual Cues Matter: CH-SIMS v2.0 Dataset and AV-Mixup Consistent Module","created":"2022-08-22","cited_by_count":44},{"id":"2104.09805","title":"CTNet: Context-based Tandem Network for Semantic Segmentation","created":"2021-04-20","cited_by_count":43},{"id":"2109.01797","title":"Hybrid Contrastive Learning of Tri-Modal Representation for Multimodal Sentiment Analysis","created":"2021-09-04","cited_by_count":41},{"id":"2209.04355","title":"MIntRec: A New Dataset for Multimodal Intent Recognition","created":"2022-09-09","cited_by_count":41},{"id":"2009.02902","title":"TransModality: An End2End Fusion Method with Transformer for Multimodal Sentiment Analysis","created":"2020-09-07","cited_by_count":40},{"id":"1806.06228","title":"Multimodal Sentiment Analysis using Hierarchical Fusion with Context Modeling","created":"2018-06-16","cited_by_count":39},{"id":"1905.05812","title":"Multi-task Learning for Multi-modal Emotion Recognition and Sentiment Analysis","created":"2019-05-14","cited_by_count":39},{"id":"2310.20494","title":"A Transformer-Based Model With Self-Distillation for Multimodal Emotion Recognition in Conversations","created":"2023-10-31","cited_by_count":37},{"id":"2010.00734","title":"Training Strategies to Handle Missing Modalities for Audio-Visual Expression Recognition","created":"2020-10-02","cited_by_count":36},{"id":"1708.06724","title":"VIGAN: Missing View Imputation with Generative Adversarial Networks","created":"2017-08-22","cited_by_count":33},{"id":"2404.17862","title":"Revisiting Multimodal Emotion Recognition in Conversation from the Perspective of Graph Spectrum","created":"2024-04-27","cited_by_count":33},{"id":"2110.08020","title":"Multimodal Emotion-Cause Pair Extraction in Conversations","created":"2021-10-15","cited_by_count":31},{"id":"1808.02096","title":"Semi-supervised Deep Generative Modelling of Incomplete Multi-Modality Emotional Data","created":"2018-07-27","cited_by_count":29},{"id":"2205.02455","title":"COGMEN: COntextualized GNN based Multimodal Emotion recognitioN","created":"2022-05-05","cited_by_count":29},{"id":"2310.05804","title":"Learning Language-guided Adaptive Hyper-modality Representation for Multimodal Sentiment Analysis","created":"2023-10-09","cited_by_count":29},{"id":"2204.13707","title":"Tag-assisted Multimodal Sentiment Analysis under Uncertain Missing Modalities","created":"2022-04-28","cited_by_count":28},{"id":"2104.07123","title":"The MuSe 2021 Multimodal Sentiment Analysis Challenge: Sentiment, Emotion, Physiological-Emotion, and Stress","created":"2021-04-14","cited_by_count":27},{"id":"2401.03429","title":"MERBench: A Unified Evaluation Benchmark for Multimodal Emotion Recognition","created":"2024-01-07","cited_by_count":27},{"id":"2408.04638","title":"Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspective","created":"2024-07-30","cited_by_count":25},{"id":"2109.05812","title":"UniMS: A Unified Framework for Multimodal Summarization with Knowledge Distillation","created":"2021-09-13","cited_by_count":23},{"id":"2101.06053","title":"The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements","created":"2021-01-15","cited_by_count":22},{"id":"2201.05834","title":"Tailor Versatile Multi-modal Learning for Multi-label Emotion Recognition","created":"2022-01-15","cited_by_count":22},{"id":"2204.05515","title":"CLMLF:A Contrastive Learning and Multi-Layer Fusion Method for Multimodal Sentiment Detection","created":"2022-04-12","cited_by_count":21},{"id":"2204.07955","title":"Vision-Language Pre-Training for Multimodal Aspect-Based Sentiment Analysis","created":"2022-04-17","cited_by_count":21},{"id":"2207.11900","title":"GA2MIF: Graph and Attention Based Two-Stage Multi-Source Information Fusion for Conversational Emotion Detection","created":"2022-07-25","cited_by_count":20},{"id":"2306.15401","title":"Explainable Multimodal Emotion Recognition","created":"2023-06-27","cited_by_count":19},{"id":"1911.09826","title":"Factorized Multimodal Transformer for Multimodal Sequential Learning","created":"2019-11-22","cited_by_count":16},{"id":"2210.15359","title":"Exploiting modality-invariant feature for robust multimodal emotion recognition with missing modalities","created":"2022-10-27","cited_by_count":16},{"id":"2111.04735","title":"Feature-enhanced Generation and Multi-modality Fusion based Deep Neural Network for Brain Tumor Segmentation with Missing MR Modalities","created":"2021-11-08","cited_by_count":13},{"id":"2408.09481","title":"PanoSent: A Panoptic Sextuple Extraction Benchmark for Multimodal Conversational Aspect-based Sentiment Analysis","created":"2024-08-18","cited_by_count":13},{"id":"2112.01368","title":"ScaleVLAD: Improving Multimodal Sentiment Analysis via Multi-Scale Fusion of Locally Descriptors","created":"2021-12-02","cited_by_count":12},{"id":"2312.10201","title":"CARAT: Contrastive Feature Reconstruction and Aggregation for Multi-Modal Multi-Label Emotion Recognition","created":"2023-12-15","cited_by_count":12},{"id":"2203.16369","title":"Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis","created":"2022-03-30","cited_by_count":10},{"id":"2408.16272","title":"Beyond Uncertainty: Evidential Deep Learning for Robust Video Temporal Grounding","created":"2024-08-29","cited_by_count":10},{"id":"2204.04637","title":"UniDU: Towards A Unified Generative Dialogue Understanding Framework","created":"2022-04-10","cited_by_count":9},{"id":"2302.13661","title":"Using Auxiliary Tasks In Multimodal Fusion Of Wav2vec 2.0 And BERT For Multimodal Emotion Recognition","created":"2023-02-27","cited_by_count":9},{"id":"2305.08473","title":"Shared and Private Information Learning in Multimodal Sentiment Analysis with Deep Modal Alignment and Self-supervised Multi-Task Learning","created":"2023-05-15","cited_by_count":9}],"next_cursor":"100"}