Data to 7 Oct 2026

Bilinear Attention Networks

arXiv 1805.07932 ↗First posted 21 May 2018cs.CV cs.AI cs.CLcited by 277

Not on the map. Why?

Cited by 39 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Attention networks in multimodal learning provide an efficient way to utilize given visual information selectively. However, the computational cost to learn attention distributions for every pair of multimodal input channels is prohibitively expensive. To solve this problem, co-attention builds two separate attention distributions for each modality neglecting the interaction between multimodal inputs. In this paper, we propose bilinear attention networks (BAN) that find bilinear attention distributions to utilize given vision-language information seamlessly. BAN considers bilinear interactions among two groups of input channels, while low-rank bilinear pooling extracts the joint representations for each pair of channels. Furthermore, we propose a variant of multimodal residual networks to exploit eight-attention maps of the BAN efficiently. We quantitatively and qualitatively evaluate our model on visual question answering (VQA 2.0) and Flickr30k Entities datasets, showing that BAN significantly outperforms previous methods and achieves new state-of-the-arts on both datasets.

Key facts. Cited by 277 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 May 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 78; 2022: 74; 2023: 58; 2024: 40; 2025: 20; 2026: 7.

277 papers.

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2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,310
2022Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answeringcited by 1,881
2021Align before Fuse: Vision and Language Representation Learning with Momentum Distillationcited by 1,612
2021Grounded Language-Image Pre-trainingcited by 1,147
2021CLIP-Adapter: Better Vision-Language Models with Feature Adapterscited by 1,021
2021MDETR -- Modulated Detection for End-to-End Multi-Modal Understandingcited by 811
2023LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attentioncited by 776
2023Multimodal Chain-of-Thought Reasoning in Language Modelscited by 740
2021Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modelingcited by 429
2021Unifying Vision-and-Language Tasks via Text Generationcited by 411
2022Tip-Adapter: Training-free Adaption of CLIP for Few-shot Classificationcited by 355
2023Chameleon: Plug-and-Play Compositional Reasoning with Large Language Modelscited by 352

38 more

2021IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoningcited by 262
2023NuScenes-QA: A Multi-modal Visual Question Answering Benchmark for Autonomous Driving Scenariocited by 258
2021Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learningcited by 187
2023VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Datasetcited by 138
2023CLIPN for Zero-Shot OOD Detection: Teaching CLIP to Say Nocited by 130
2022A Survey of Deep Learning for Mathematical Reasoningcited by 124
2024Vision-Language Models for Medical Report Generation and Visual Question Answering: A Reviewcited by 121
2022Vision-Language Pre-training: Basics, Recent Advances, and Future Trendscited by 117
2021Energy-Based Learning for Scene Graph Generationcited by 104
2022Multi-Modal Masked Autoencoders for Medical Vision-and-Language Pre-Trainingcited by 104
2022DualCoOp: Fast Adaptation to Multi-Label Recognition with Limited Annotationscited by 102
2021Medical Visual Question Answering: A Surveycited by 93
2022Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoningcited by 90
2021Does CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain?cited by 85
2021Multi-Modal Answer Validation for Knowledge-Based VQAcited by 78
2021ML-Decoder: Scalable and Versatile Classification Headcited by 78
2021Show, Attend and Distill:Knowledge Distillation via Attention-based Feature Matchingcited by 76
2024Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Surveycited by 74
2021Contrastive Attention for Automatic Chest X-ray Report Generationcited by 71
2021Probing Inter-modality: Visual Parsing with Self-Attention for Vision-Language Pre-trainingcited by 67
2023T-SciQ: Teaching Multimodal Chain-of-Thought Reasoning via Mixed Large Language Model Signals for Science Question Answeringcited by 59
2023Read-only Prompt Optimization for Vision-Language Few-shot Learningcited by 58
2021Multiple Meta-model Quantifying for Medical Visual Question Answeringcited by 54
2021DoubleField: Bridging the Neural Surface and Radiance Fields for High-fidelity Human Reconstruction and Renderingcited by 52
2022Align, Reason and Learn: Enhancing Medical Vision-and-Language Pre-training with Knowledgecited by 51
2023EarthVQA: Towards Queryable Earth via Relational Reasoning-Based Remote Sensing Visual Question Answeringcited by 51
2021Attention, please! A survey of Neural Attention Models in Deep Learningcited by 50
2021A Simple Long-Tailed Recognition Baseline via Vision-Language Modelcited by 49
2022Divert More Attention to Vision-Language Trackingcited by 47
2024MedCoT: Medical Chain of Thought via Hierarchical Expertcited by 46
2023HiCLIP: Contrastive Language-Image Pretraining with Hierarchy-aware Attentioncited by 44
2022Gender and Racial Bias in Visual Question Answering Datasetscited by 39
2022Interpretable bilinear attention network with domain adaptation improves drug-target predictioncited by 39
2022SwapMix: Diagnosing and Regularizing the Over-Reliance on Visual Context in Visual Question Answeringcited by 38
2022The 8-Point Algorithm as an Inductive Bias for Relative Pose Prediction by ViTscited by 38
2021Exemplar-Based Open-Set Panoptic Segmentation Networkcited by 37
2021Zero-shot Visual Question Answering using Knowledge Graphcited by 37
2022MuKEA: Multimodal Knowledge Extraction and Accumulation for Knowledge-based Visual Question Answeringcited by 36

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