The DOI matches its Crossref record; 154 of the 154 references it cites were found in a registry or an index.
Flamingo: A Visual Language Model for Few-Shot Learning
Abstract
Building models that can be rapidly adapted to novel tasks using only a handful of annotated examples is an open challenge for multimodal machine learning research. We introduce Flamingo, a family of Visual Language Models (VLM) with this ability. We propose key architectural innovations to: (i) bridge powerful pretrained vision-only and language-only models, (ii) handle sequences of arbitrarily interleaved visual and textual data, and (iii) seamlessly ingest images or videos as inputs. Thanks to their flexibility, Flamingo models can be trained on large-scale multimodal web corpora containing arbitrarily interleaved text and images, which is key to endow them with in-context few-shot learning capabilities. We perform a thorough evaluation of our models, exploring and measuring their ability to rapidly adapt to a variety of image and video tasks. These include open-ended tasks such as visual question-answering, where the model is prompted with a question which it has to answer, captioning tasks, which evaluate the ability to describe a scene or an event, and close-ended tasks such as multiple-choice visual question-answering. For tasks lying anywhere on this spectrum, a single Flamingo model can achieve a new state of the art with few-shot learning, simply by prompting the model with task-specific examples. On numerous benchmarks, Flamingo outperforms models fine-tuned on thousands of times more task-specific data.
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In the citing paper
“vision-language models (VLMs) such as VilBERT 19 and Flamingo 2 combine a language model with an image encoder and are trained on a multi-modal corpus of text-image pairs.This enables them to predict how a given sequence of words will continue in the context of a given image.”
From Talking about Large Language Models (2024) · cites this paper as [2]
In the citing paper
“the latest generation of language models are multimodal, allowing interleaved image and text input (OpenAI, 2023a; Alayrac et al., 2022; Team et al., 2023; Li et al., 2023b)”
From Cognitive Architectures for Language Agents (2023) · cites this paper as Alayrac et al., 2022
In the citing paper
“The Switch Transformer [201] was one of the first MoE-based Transformers, activating experts selectively in each layer, reducing computational overhead while outperforming dense Transformers in NLP tasks.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [201]
In the citing paper
“The dominant architectural paradigm connects specialized, external multimodal encoders-such as CLIP for vision or CLAP for audio-to the LLM backbone via alignment modules like Q-Former or simple MLPs [19, 86, 615, 1139], a modular design that allows for independent encoder updates without retraining the entire model [624].”
From A Survey of Context Engineering for Large Language Models (2025) · cites this paper as [19]
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Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · cited 3×
“Language modelling has recently made substantial progress following the introduction of Transformers [115].”
BackgroundMethodInfluentialHochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · cited 2×
“To ensure that at initialization, the conditioned model yields the same results as the original language model, we use a tanh-gating mechanism [41].”
MethodBERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325 · cited 4×
“In particular, BERT [23] inspired a large body of vision-language work [16, 28, 29, 38, 59, 61, 66, 101, 106, 107, 109, 118, 121, 142, 143, 151].”
BackgroundMethodInfluentialAn Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 47,472
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · cited 2×
“Im ag e ImageNet-1k [94] 3 Object classification Val Top-1 acc.”
BackgroundSwin Transformer: Hierarchical Vision Transformer using Shifted Windows
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00986 · Cited by 35,534
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · cited 12×
“We evaluate the ability of our models to rapidly adapt to new tasks using in-context learning , analogously to GPT-3 [11], by interleaving support example pairs in the form of ( 𝑖𝑚𝑎𝑔𝑒, 𝑡𝑒𝑥𝑡 ) or ( 𝑣𝑖𝑑𝑒𝑜, 𝑡𝑒𝑥𝑡 ) , followed by the query visual input, to build a prompt (details in Method FT Shot OKVQA ( I )”
BackgroundMethodResultInfluentialEnd-to-End Object Detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_13 · Cited by 18,285 · cited 2×
“[13] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko.”
BackgroundMethodLearning Transferable Visual Models From Natural Language Supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al. (2021) · arXiv · doi:10.48550/arxiv.2103.00020 · Cited by 15,293 · cited 12×
“[85] we use “prompt-ensembling” in which we embed multiple texts using templates such as “A photo of a {class_name}” and average the resulting embedding.”
BackgroundMethodInfluentialExploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929 · cited 3×
“To measure the importance of text pretraining, we compare the performance of using a frozen decoder-only Transformer either pretrained on MassiveText (our main model) or pretrained on the C4 dataset [87] (row (iv) of Table 10).”
BackgroundMethod
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Liakopoulos, D., Sinha, P., Hu, T., Lee, M., & Yadwadkar, N. J. (2026) · ACM SIGOPS Operating Systems Review · doi:10.1145/3830422.3830428
“Examples include MoE-based models such as Mixtral-8-7B [16], recurrent architectures such as RWKV [35], and multimodal models such as Flamingo [3].”
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Yan, H., Cui, S., Li, F., Xie, P., & Chi, C. (2026) · Communications in computer and information science · doi:10.1007/978-981-92-3560-5_30
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Zhou, S., & Qin, F. (2026) · PLoS ONE · doi:10.1371/journal.pone.0328408
“Furthermore, LLaVA-Med employs specialized optimizations in its image encoder and language model to better adapt to the unique characteristics of biomedical data [42,43].”
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Çelik, R. F., Bağcı, U., & Kessler, S. (2026) · Structural Concrete · doi:10.1002/suco.70702
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Guo, Z., Yang, H., Yin, H., & Xu, J. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3495-0_21
Le, X. T., Vo, Q. N. T., Tran, T. H. M., Nguyen, T. T., & Quan, T. T. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-2885-0_8
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X-CapFusion: Efficient label-aware model for medical image captioning
Amorim, M. T., Oliveira, H. S., Oliveira, H. P., & Teixeira, L. F. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.111028
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Gao, Y., & Li, Z. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3507-0_12
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Adaptive Social Robot Navigation Using Vision-Language Models
Anand, K., Chang, D., & John, N. (2025) · Springer proceedings in mathematics & statistics · doi:10.1007/978-981-95-3841-6_9
Haq, S. U., Castilho, S., & Graham, Y. (2026) · Information · doi:10.3390/info17070689
“Models in this category integrate visual and linguistic information by pairing strong visual encoders with large, often frozen, language models to produce unified multimodal representations (e.g., CLIP [112] BLIP-2 [50], Flamingo [52], and Gemma-3 [113]).”
DreamActor-M2: Universal Character Image Animation via Spatiotemporal In-Context Learning
Luo, M., Liang, S., Rong, Z., Luo, Y., Hu, T., Hou, R., Chang, H., Li, Y., Zhang, Y., & Liu, J. (2026) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/3799902.3811114 · Cited by 3
“Inspired by in-context learning (ICL) in LLMs [4, 18] and VLMs [1, 11, 31], which achieves seamless task adaptation via direct input integration, we propose a spatiotemporal ICL strategy to resolve the aforementioned trade-off.”
Xie, R., LIU, Z., & Zhang, J. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3435-6_47
Towards Spatial Supersensing in the Wild
Gu, T., Xin, T., Zhang, K., Yang, B., Chua, K.-C., Li, P., Zhang, X., Chen, Y., Zhao, Q., Xie, Q., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37174-4_25 · Cited by 3
HVM: A HV-Attention Based Multi-modal Framework for Mathematical Expression Recognition
Jiang, L., Yang, F., Ye, J., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3417-2_25
Sun, L., Wang, Z., Jiao, L., Liu, X., Li, L., Lu, X., Liu, F., Li, S., Zhang, D., Tian, X., et al. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.114449 · Cited by 1
Иванушкина, Н. Г., Mnevets, A., & Ivanko, K. O. (2026) · IEEE International Conference on Electronics and Nanotechnology (ELNANO) · doi:10.1109/elnano63396.2026.11601389
“As the result, a coherently integrated representation was formed and used by the language model to establish context and generate the final response [11].”
GhostMarking: Embedding Invisible Textual Marks in VLM via Adversarial Trigger Learning
Yang, K., Li, Z., Wei, M., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3429-5_16
SparseVision-Drive: Hierarchical Sparse Multi-view Visual Aggregation for Autonomous-Driving VLMs
Yin, H., Li, S., Liu, S. X., & Watzenig, D. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3429-5_19
MGL-LMM: Generative OCR with Semantic Inpainting for Degraded Mongolian Lead-Type Printed Documents
Luo, Z., Lu, M., Liu, N., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3520-9_46
PaR: Prompt-as-Expert Routing with Context-Aware Fusion for Efficient Visual Document Understanding
Lin, B., Liu, H., He, C., & Hao, T. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3378-6_20
Wang, Y., Li, T., Zhao, Y., Zhang, B., & Huang, X. (2026) · Buildings · doi:10.3390/buildings16142882 · Cited by 2
When More Is Not Better: Multimodal Information Efficiency in Conversational Interfaces
Chen, X., Shi, H., & Zhao, H. (2026) · ACM Conference on Conversational User Interfaces (CUI) · doi:10.1145/3816046.3816267
A Comprehensive Review of Vision-Based Fatigue Detection AlgorithmsPreprint
Tang, C., Liu, J., Xiong, J., Han, J., Cao, M. L., Chi, H., Yu, Z., Zhou, B., & Yang, L. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7152575
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Abdelwahab, H. A., Younes, G., Redha, B. M., et al. (2026) · International Conference on Pattern Analysis and Intelligent Systems (PAIS) · doi:10.1109/pais69842.2026.11605179
“Multimodal learning combines signals such as vision, language, and structured measurements, and it often improves clean-data performance on reasoning tasks [7], [8].”
TigXMM: A Multimodal Cross-Lingual Transfer LLM Method for Low-Resource Sentiment Analysis
Gebremeskel, H. G., Feng, C., Abera, A. M., & Zhao, X. (2026) · doi:10.1145/3810987.3815534
Efficient FOD Detection through YOLO-VLM Collaboration with Confidence-Based Intelligent Routing
He, D., Li, J. H., Xie, Y., Shu, Y., & Li, Y. (2026) · IEEE International Conference on Pattern Recognition, Machine Vision and Artificial Intelligence (PRMVAI) · doi:10.1109/prmvai70103.2026.11605611
“…paradigms: Contrastive Learning (e.g., CLIP [14]) which maps images and text into a shared embedding space for zero-shot classification, and Generative Paradigm (e.g., Flamingo [15], BLIP-2 [16], LLaVA [17]) which achieves deep integration of vision and language models for visual question answering.”
Cloud and Edge Systems for Large AI Models
Yuan, H., Bi, J., Zhou, M., & Buyya, R. (2026) · Internet of things · doi:10.1007/978-3-032-18400-9_15
MRM++: Enhanced Masked Relation Modeling for Multi-Modal Medical Pre-training
Yang, Q., Li, W., Peng, Z., Cheng, F., & Yuan, Y. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-026-02938-5
“In addition, some latest works (Zhu et al., 2023; Liu et al., 2024; Alayrac et al., 2022; Li et al., 2023, 2024; Wang et al., 2024) as the hybrid strategy simultaneously employ contrastive learning and masked image modeling using paired image-text data to pre-train a large multi-modal foundation…”
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Survey on Multi-Modal Large Language Models: Bridging Text, Vision, and Beyond
Bhashkar, K., & Kumar, S. (2025) · IEEE International Conference on Innovate for Humanitarian: Tech Solutions for Global Challenge (ICIH) · doi:10.1109/icih67754.2025.11608965
“The cross-attention processes have proved to be useful, particularly in the language model to focus on the salient visual details during the text generation and the fine-tuning adjustments between the linguistic description and the visual data [8].”
ThaiROCR: A Thai Receipt Dataset for Robust OCR Development
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MambaCLIP-AD: A Novel Framework for Zero-Shot Anomaly Detection
Zhang, X., Wang, Z., Zhou, Y., & Feng, Y. (2025) · doi:10.1109/nnnlp68229.2025.00012
“Recent advances in vision-language models (VLMs)[2, 3, 4, 5], such as CLIP [6], offer a promising zero-shot alternative by aligning visual and textual data in a shared embedding space.”
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“Most existing works in this domain, including [32], [33], [34], [35], [36], and [37], have primarily focused on text-centric tasks such as image cap-tioning and Visual Question Answering (VQA).”
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Global image geolocalisation via inference with large vision–language models
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Shet, P., Sujata, C., & Baligar, V. P. (2026) · International Conference on Intelligent Technologies (CONIT) · doi:10.1109/conit69683.2026.11621750
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“Similarly, Flamingo introduced a few-shot multimodal learning framework that integrates visual tokens into large language models for improved reasoning and captioning performance [18].”
TunVECM: vector graph structured extraction and intelligent checking for tunnel portal
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“Recent multimodal systems, including Flamingo [7], In-structBLIP [8], LLaVA [10], [11], and GPT-4o extend VFMs with instruction-following capabilities and spatial reasoning.”
D3VL: Understanding Driving Scenes from 3D Time Series Data and Video with Language Models
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Dense Vehicle Detection using Multi-Stage Prompting and Grid Overlay with Vision-Language Models
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Visual Instruction-Finetuned Language Model for Versatile Brain MR Image Tasks
Kim, J., Ra, S., & Park, H. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-31335-5_2 · Cited by 1
GeoVP: A Unified Visual Prompting Framework for Multisource Remote-Sensing Image Understanding
Yu, L., Wang, Y., & Qi, X. (2026) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2026.3719361
Vishal, A. S., Kannan, S., Durai, S. K., Preetam, T. B., Duraisamy, P., & Keerthika, T. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-30005-8_25
Pseudo Contrastive Learning for Diagram Comprehension in Multimodal Models
Sasaki, H. (2026) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw68408.2026.00078
“Subsequent models, including BLIP (Bootstrapping Language-Image Pretraining) [7] and Flamingo [1], further enhanced multimodal understanding by incorporating more sophisticated architectures and training strategies, leading to improved performance in tasks like visual question answering (VQA) and…”
Context-Aligned Latent Enhancement for Multimodal Hate Meme Detection
Zong, L., Gui, Q., Wang, J., Liu, X., Ouyang, X., & Zhang, J. (2026) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2026.3715754
“2) CLIP Encoder-Based Models: Subsequently, multimodal pre-trained models represented by contrastive language–image pre-training (CLIP) demonstrate impressive capabilities across a wide range of downstream tasks by leveraging knowledge learned from large-scale image-text pairs [13], [14].”
InfluentialEgoAfford: Affordance-Aware Zero-Shot Open-Vocabulary Egocentric Action Recognition
Gesualdi, D., Santambrogio, R., Palermo, F., Plizzari, C., Mentasti, S., & Matteucci, M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-31936-4_7
Jin, S., Song, G., Xiang, H., Fu, Z., Zhang, X., Pei, J., Zhang, T., Fan, W., Wang, Q., & Zhang, D. (2026) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2026.07.066
Language-driven models for video anomaly detection: taxonomy, datasets, and future horizons
Saket, O., Aicha, A. B., & Fathallah, H. (2026) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-026-00415-w
Wang, Y., Kaggwa, P., Jolly, H., Bhandarkar, S. M., Sekandi, J. N., & Sun, J. (2026) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw68408.2026.00006
“Recent advances in large foundation models (LFMs) [1, 6, 11], particularly vision-language models (VLMs) [2, 17, 19, 37], offer a more flexible and generalizable alternative to conventional supervised video models and task-specific pipelines [26, 37].”
Rohilla, H., Singh, A. P., & Ratna, S. (2026) · International Conference on Data Science for Cyber-Physical Systems Resilience using Advanced Applications (ICDCA) · doi:10.1109/icdca69396.2026.11620238
“Large vision–language models (VLMs) enable zero-shot or few-shot report generation [35], [36].”
Visual Memory-Augmented Architecture for Persistent Multimodal Conversational Agents
Gandikota, L. R., Gudipati, V. R. S., Aluvala, S. K., PONUGOTI, S., & Konda, S. T. R. (2026) · International Conference on Revolutionary Artificial Intelligence and Future Applications (Rev-AI) · doi:10.1109/rev-ai70456.2026.11622132
“Subsequent work including BLIP [9], BLIP-2, and Flamingo [10] explored more sophisticated architectures combining frozen vision encoders with large language models through lightweight adapter modules, achieving impressive few-shot learning performance.”
SHIN, Y. W., Park, K. H., Turaga, P. K., & Jeon, E. S. (2026) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw68408.2026.00190
“With advancements in LLMs [1, 3, 30, 40–42] and vision-language models (VLMs) [2, 25, 34], perception capabilities have undergone enhancements to include surrounding visual context [31, 57].”
Satama, P., Radman, A., & Laaksonen, J. T. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-31397-3_19
RaTA-Tool: Retrieval-Based Tool Selection with Multimodal Large Language Models
Mattioli, G., Turri, E., Sarto, S., Baraldi, L., Cornia, M., Baraldi, L., & Cucchiara, R. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-31397-3_21 · Cited by 2
AVT-PAC: A Pipeline for Multimodal Action Prediction and Captioning
Athira Krishnan, R., Parthasarathy, A., Devarakonda, S., et al. (2026) · International Conference on Signal Processing and Communications (SPCOM) · doi:10.1109/spcom71105.2026.11623034
“In the past few years, the exponential growth of Large Language Models (LLMs) has led to the development of Vision-Language Models (VLMs) [13]–[15] that generate short/long free-form text captions for videos/images based on user requirements.”
Less is More: Agentic Prompt Design for Safe VLM Action Selection
Yahyapour, E., & Ai, C. (2026) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw68408.2026.00191
SPoRC-VIST: A Benchmark for Evaluating Generative Natural Narrative in Vision-Language Models
Zeng, Y. (2026) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw68408.2026.00118
“Deep Fusion approaches such as Flamingo [7] and OpenFlamingo [8] utilized gated cross-attention layers to inject visual information into frozen LLMs, hence enabling strong few-shot performance.”
A dual-pathway framework with vision-language adaption for data-efficient fire image classification
Cheng, G., Liu, Y., Han, X., Lu, S., Gao, S., Chen, X., & Li, X. (2026) · Fire Safety Journal · doi:10.1016/j.firesaf.2026.104941
ToothGPT: An Ensemble Visual Question Answering System for Automated Dental Radiograph Diagnostics
Anand, K., Naidu, R. C. A., & Sangeetha, V. (2026) · doi:10.1109/icicit69063.2026.11634240
Debiased controllable text generation with classifier-guided rectified flow language model
Park, J., Kim, S., & Joo, W. (2026) · Journal of the Korean Statistical Society · doi:10.1007/s42952-026-00383-1
HG-LMM: Unleashing High-Quality Pixel Grounding Capabilities in Frozen Large Multimodal Models
Li, W., Cao, J., Xie, J., Yang, A., & Pang, Y. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3718446
“For instance, some approaches [1], [13], [14], [15], [16] use MLP-based connectors, while some approaches [3], [17], [18] adopt query-based connectors.”
MixLM: High-Throughput and Effective LLM Ranking via Text-Embedding Mix-Interaction
Li, G., He, R., Jing, S., Behdin, K., Wang, Y., Ramachandran, S. R., Nguyen, C., Sheng, J., Ma, X., Zhu, C., et al. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3818448
Language-Enhanced Representation Learning for Single-Cell Transcriptomics
Yang, J., Liu, Z., Shi, Y., Zhang, Q., Nai, C., Fang, J., Long, Y., Zhang, Y., & Wang, X. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3819031
CoCo-IR: Contextual Composed Image Retrieval
Cao, S., Dabral, T. S., Ding, Z., Shanbhogue, M., Chen, K., Li, Z., Seyedhosseini, M., Wang, Y.-X., & Gui, L.-Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37556-8_20
PROBE: VLM-Guided Discrete Structural Reconfiguration for Customized and Efficient Video Retrieval
Gu, Y., Liu, K., Zhe, T., et al. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3818016
Cross-modal Fusion Transformer for Integrating Retrieved Knowledge into Video Caption Generation
Abubakirova, K., Ullah, W., Khan, L. U., & Guizani, M. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3818170
Text-Guided Visual Representation Learning for Robust Multimodal E-Commerce Recommendation
Guo, Y., Ma, J., Dong, Y., et al. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3818176
MOBI: Monolithic Graph-Language Modeling Beyond Modality Interference
Zhou, Z., Ji, Y., Xu, Z., Zheng, Z., Zhou, S., Wen, W., Chen, J., Gu, M., Chen, C., & Wang, C. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3818175
Zhang, Z., Hao, X., Yin, R., et al. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104682
Label space reduction for transductive zero-shot classification with large language models
Vandemoortele, N., Steenwinckel, B., Ongenae, F., & Hoecke, S. V. (2026) · Discover Computing · doi:10.1007/s10791-026-10420-6
Metadata
- Authors
- Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., Ring, R., Rutherford, E., Cabi, S., Han, T., Gong, Z., Samangooei, S., Monteiro, M., Menick, J. L., Borgeaud, S., Brock, A., Nematzadeh, A., Sharifzadeh, S., Binkowski, M., Barreira, R., Vinyals, O., Zisserman, A., & Simonyan, K.
- Venue
- Advances in Neural Information Processing Systems 35
- Published
- 2022
- Pages
- 23716-23736
- ISSN
- 1049-5258 (ISSN-L)
- OpenAlex
- W7133196460
- Cited by
- 966
- References
- 154
- Citations by year
- 2026: 847 · 2025: 107 · 2024: 3 · 2023: 7 · 2022: 2
- FWCI
- 15.48
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- neural information processing systems (publishedVersion)
- Oxford University Research Archive (ORA) (University of Oxford) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- vision-language models, few-shot learning, image-text models
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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