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2503.04839
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Advancing Multimodal In-Context Learning in Large Vision-Language Models with Task-aware Demonstrations
5 March 2025
Yanshu Li
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Papers citing
"Advancing Multimodal In-Context Learning in Large Vision-Language Models with Task-aware Demonstrations"
35 / 35 papers shown
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27 Nov 2025
TACO: Enhancing Multimodal In-context Learning via Task Mapping-Guided Sequence Configuration
Yanshu Li
Tian Yun
Tian Yun
Pinyuan Feng
Jinfa Huang
Ruixiang Tang
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21 May 2025
Make LVLMs Focus: Context-Aware Attention Modulation for Better Multimodal In-Context Learning
Yanshu Li
JianJiang Yang
Ziteng Yang
Bozheng Li
Yi Cao
...
Ligong Han
Yingjie Victor Chen
Songlin Fei
Dongfang Liu
Ruixiang Tang
488
8
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21 May 2025
Multimodal Task Vectors Enable Many-Shot Multimodal In-Context Learning
Brandon Huang
Chancharik Mitra
Assaf Arbelle
Leonid Karlinsky
Trevor Darrell
Roei Herzig
294
44
0
21 Jun 2024
ICLEval: Evaluating In-Context Learning Ability of Large Language Models
Wentong Chen
Yankai Lin
ZhenHao Zhou
HongYun Huang
Yantao Jia
Bo Zhao
Ji-Rong Wen
ELM
293
11
0
21 Jun 2024
How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites
Zhe Chen
Weiyun Wang
Hao Tian
Shenglong Ye
Zhangwei Gao
...
Tong Lu
Dahua Lin
Yu Qiao
Jifeng Dai
Wenhai Wang
MLLM
VLM
667
1,136
0
25 Apr 2024
Visual In-Context Learning for Large Vision-Language Models
Yucheng Zhou
Xiang Li
Qianning Wang
Jianbing Shen
MLLM
276
134
0
18 Feb 2024
Comparable Demonstrations are Important in In-Context Learning: A Novel Perspective on Demonstration Selection
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023
Caoyun Fan
Jidong Tian
Yitian Li
Hao He
Yaohui Jin
338
6
0
12 Dec 2023
How to Configure Good In-Context Sequence for Visual Question Answering
Computer Vision and Pattern Recognition (CVPR), 2023
Li Li
Jiawei Peng
Huiyi Chen
Chongyang Gao
Xu Yang
MLLM
303
40
0
04 Dec 2023
Meta-Adapter: An Online Few-shot Learner for Vision-Language Model
Neural Information Processing Systems (NeurIPS), 2023
Cheng Cheng
Lin Song
Ruoyi Xue
Hang Wang
Hongbin Sun
Yixiao Ge
Ying Shan
VLM
ObjD
580
56
0
07 Nov 2023
An Early Evaluation of GPT-4V(ision)
Yang Wu
Shilong Wang
Hao Yang
Tian Zheng
Hongbo Zhang
Yanyan Zhao
Bing Qin
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219
49
0
25 Oct 2023
Multimodal Neurons in Pretrained Text-Only Transformers
Sarah Schwettmann
Neil Chowdhury
Samuel J. Klein
David Bau
Antonio Torralba
MILM
386
50
0
03 Aug 2023
OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models
Anas Awadalla
Irena Gao
Josh Gardner
Jack Hessel
Yusuf Hanafy
...
Simon Kornblith
Pang Wei Koh
Gabriel Ilharco
Mitchell Wortsman
Ludwig Schmidt
MLLM
462
591
0
02 Aug 2023
Learning to Retrieve In-Context Examples for Large Language Models
Conference of the European Chapter of the Association for Computational Linguistics (EACL), 2023
Liang Wang
Nan Yang
Furu Wei
RALM
276
64
0
14 Jul 2023
Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations
Annual Meeting of the Association for Computational Linguistics (ACL), 2023
Chenglei Si
Dan Friedman
Nitish Joshi
Shi Feng
Danqi Chen
He He
386
65
0
22 May 2023
What In-Context Learning "Learns" In-Context: Disentangling Task Recognition and Task Learning
Annual Meeting of the Association for Computational Linguistics (ACL), 2023
Jane Pan
Tianyu Gao
Howard Chen
Danqi Chen
243
167
0
16 May 2023
Larger language models do in-context learning differently
Jerry W. Wei
Jason W. Wei
Yi Tay
Dustin Tran
Albert Webson
...
Xinyun Chen
Hanxiao Liu
Da Huang
Denny Zhou
Tengyu Ma
ReLM
LRM
553
461
0
07 Mar 2023
Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering
Annual Meeting of the Association for Computational Linguistics (ACL), 2022
Zhiyong Wu
Yaoxiang Wang
Jiacheng Ye
Lingpeng Kong
472
199
0
20 Dec 2022
Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations
Annual Meeting of the Association for Computational Linguistics (ACL), 2022
Xinxi Lyu
Sewon Min
Iz Beltagy
Luke Zettlemoyer
Hannaneh Hajishirzi
VLM
240
82
0
19 Dec 2022
What Can Transformers Learn In-Context? A Case Study of Simple Function Classes
Neural Information Processing Systems (NeurIPS), 2022
Shivam Garg
Dimitris Tsipras
Abigail Z. Jacobs
Gregory Valiant
777
734
0
01 Aug 2022
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models
International Conference on Learning Representations (ICLR), 2022
Denny Zhou
Nathanael Scharli
Le Hou
Jason W. Wei
Nathan Scales
...
Dale Schuurmans
Claire Cui
Olivier Bousquet
Quoc Le
Ed H. Chi
RALM
LRM
AI4CE
929
1,672
0
21 May 2022
Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022
Sewon Min
Xinxi Lyu
Ari Holtzman
Mikel Artetxe
M. Lewis
Hannaneh Hajishirzi
Luke Zettlemoyer
LLMAG
LRM
680
1,949
0
25 Feb 2022
Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing
ACM Computing Surveys (CSUR), 2021
Pengfei Liu
Weizhe Yuan
Jinlan Fu
Zhengbao Jiang
Hiroaki Hayashi
Graham Neubig
VLM
SyDa
913
5,235
0
28 Jul 2021
Multimodal Few-Shot Learning with Frozen Language Models
Neural Information Processing Systems (NeurIPS), 2021
Maria Tsimpoukelli
Jacob Menick
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S. M. Ali Eslami
Oriol Vinyals
Felix Hill
MLLM
700
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25 Jun 2021
Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity
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Bill Dolan
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AAML
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669
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Making Pre-trained Language Models Better Few-shot Learners
Annual Meeting of the Association for Computational Linguistics (ACL), 2021
Tianyu Gao
Adam Fisch
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945
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0
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Language Models are Few-Shot Learners
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Tom B. Brown
Benjamin Mann
Nick Ryder
Melanie Subbiah
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...
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Sam McCandlish
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Amanpreet Singh
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Davide Testuggine
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OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge
Computer Vision and Pattern Recognition (CVPR), 2019
Kenneth Marino
Mohammad Rastegari
Ali Farhadi
Roozbeh Mottaghi
854
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0
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VizWiz Grand Challenge: Answering Visual Questions from Blind People
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Abigale Stangl
Anhong Guo
Chi Lin
Kristen Grauman
Jiebo Luo
Jeffrey P. Bigham
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Microsoft COCO: Common Objects in Context
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