Fetching the paper…
Reading the bibliography…
Endowing Large Multimodal Models (LMMs) with visual grounding capability can significantly enhance AIs' understanding of the visual world and their interaction with humans.
Referitgame: Referring to objects in photographs of natural scenes
Sahar Kazemzadeh, Vicente Ordonez, Mark Matten, and Tamara Berg · 2014
Earlier work this paper cites.
Referitgame: Referring to objects in photographs of natural scenes
Sahar Kazemzadeh, Vicente Ordonez, Mark Matten, and Tamara Berg · 2014
Earlier work this paper cites.
Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models
Bryan A Plummer, Liwei Wang, Chris M Cervantes, Juan C Caicedo, Julia Hockenmaier, and Svetlana Lazebnik · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Generation and comprehension of unambiguous object descriptions
Junhua Mao, Jonathan Huang, Alexander Toshev, Oana Camburu, Alan L Yuille, and Kevin Murphy · 2016
Earlier work this paper cites.
Modeling context between objects for referring expression understanding
Varun K Nagaraja, Vlad I Morariu, and Larry S Davis · 2016
Earlier work this paper cites.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille · 2017
Earlier work this paper cites.
Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
Earlier work this paper cites.
spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing
Matthew Honnibal and Ines Montani · 2017
Earlier work this paper cites.
Visual Genome: Connecting language and vision using crowdsourced dense image annotations
Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A. Shamma, Michael S. Bernstein, and Li Fei-Fei · 2017
Earlier work this paper cites.
Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations
Carole H Sudre, Wenqi Li, Tom Vercauteren, Sebastien Ourselin, and M Jorge Cardoso · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Scene parsing through ade20k dataset
Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba · 2017
Earlier work this paper cites.
Coco-stuff: Thing and stuff classes in context
Holger Caesar, Jasper Uijlings, and Vittorio Ferrari · 2018
Earlier work this paper cites.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
Earlier work this paper cites.
Panoptic segmentation
Alexander Kirillov, Kaiming He, Ross B. Girshick, Carsten Rother, and Piotr Dollár · 2019
Earlier work this paper cites.
Decoupled weight decay regularization, 2019
Ilya Loshchilov and Frank Hutter · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Earlier work this paper cites.
Upsnet: A unified panoptic segmentation network
Yuwen Xiong, Renjie Liao, Hengshuang Zhao, Rui Hu, Min Bai, Ersin Yumer, and Raquel Urtasun · 2019
Earlier work this paper cites.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Earlier work this paper cites.
Panoptic-deeplab: A simple, strong, and fast baseline for bottom-up panoptic segmentation
Bowen Cheng, Maxwell D Collins, Yukun Zhu, Ting Liu, Thomas S Huang, Hartwig Adam, and Liang-Chieh Chen · 2020
Earlier work this paper cites.
Multi-task collaborative network for joint referring expression comprehension and segmentation
Gen Luo, Yiyi Zhou, Xiaoshuai Sun, Liujuan Cao, Chenglin Wu, Cheng Deng, and Rongrong Ji · 2020
Earlier work this paper cites.
Generic attention-model explainability for interpreting bi-modal and encoder-decoder transformers
Hila Chefer, Shir Gur, and Lior Wolf · 2021
Earlier work this paper cites.
Per-pixel classification is not all you need for semantic segmentation
Bowen Cheng, Alex Schwing, and Alexander Kirillov · 2021
Earlier work this paper cites.
Panoptic narrative grounding
Cristina González, Nicolás Ayobi, Isabela Hernández, José Hernández, Jordi Pont-Tuset, and Pablo Arbeláez · 2021
Cited alongside, same era.
Fully convolutional networks for panoptic segmentation
Yanwei Li, Hengshuang Zhao, Xiaojuan Qi, Liwei Wang, Zeming Li, Jian Sun, and Jiaya Jia · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Cited alongside, same era.
Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le · 2021
Cited alongside, same era.
K-net: Towards unified image segmentation
Wenwei Zhang, Jiangmiao Pang, Kai Chen, and Chen Change Loy · 2021
Cited alongside, same era.
Pixellm: Pixel reasoning with large multimodal model
Zhongwei Ren, Zhicheng Huang, Yunchao Wei, Yao Zhao, Dongmei Fu, Jiashi Feng, and Xiaojie Jin · 2023
Later among the works it cites.
Towards real-time panoptic narrative grounding by an end-to-end grounding network
Haowei Wang, Jiayi Ji, Yiyi Zhou, Yongjian Wu, and Xiaoshuai Sun · 2023
Later among the works it cites.
mplug-owl: Modularization empowers large language models with multimodality
Qinghao Ye, Haiyang Xu, Guohai Xu, Jiabo Ye, Ming Yan, Yiyang Zhou, Junyang Wang, Anwen Hu, Pengcheng Shi, Yaya Shi, et al · 2023
Later among the works it cites.
Ferret: Refer and ground anything anywhere at any granularity
Haoxuan You, Haotian Zhang, Zhe Gan, Xianzhi Du, Bowen Zhang, Zirui Wang, Liangliang Cao, Shih-Fu Chang, and Yinfei Yang · 2023
Later among the works it cites.
Contextual object detection with multimodal large language models
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Flamingo: a visual language model for few-shot learning
Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al · 2022
Cited alongside, same era.
Masked-attention mask transformer for universal image segmentation
Bowen Cheng, Ishan Misra, Alexander G Schwing, Alexander Kirillov, and Rohit Girdhar · 2022
Cited alongside, same era.
Ppmn: Pixel-phrase matching network for one-stage panoptic narrative grounding
Zihan Ding, Zi-han Ding, Tianrui Hui, Junshi Huang, Xiaoming Wei, Xiaolin Wei, and Si Liu · 2022
Cited alongside, same era.
Partimagenet: A large, high-quality dataset of parts
Ju He, Shuo Yang, Shaokang Yang, Adam Kortylewski, Xiaoding Yuan, Jie-Neng Chen, Shuai Liu, Cheng Yang, Qihang Yu, and Alan Yuille · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
Cited alongside, same era.
Lavt: Language-aware vision transformer for referring image segmentation
Zhao Yang, Jiaqi Wang, Yansong Tang, Kai Chen, Hengshuang Zhao, and Philip HS Torr · 2022
Cited alongside, same era.
Opt: Open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al · 2022
Cited alongside, same era.
Yuhang Zang, Wei Li, Jun Han, Kaiyang Zhou, and Chen Change Loy · 2023
Later among the works it cites.
Sigmoid loss for language image pre-training
Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer · 2023
Later among the works it cites.
Llava-grounding: Grounded visual chat with large multimodal models, 2023
Hao Zhang, Hongyang Li, Feng Li, Tianhe Ren, Xueyan Zou, Shilong Liu, Shijia Huang, Jianfeng Gao, Lei Zhang, Chunyuan Li, and Jianwei Yang · 2023
Later among the works it cites.
Bubogpt: Enabling visual grounding in multi-modal llms
Yang Zhao, Zhijie Lin, Daquan Zhou, Zilong Huang, Jiashi Feng, and Bingyi Kang · 2023
Later among the works it cites.
Generalized decoding for pixel, image, and language
Xueyan Zou, Zi-Yi Dou, Jianwei Yang, Zhe Gan, Linjie Li, Chunyuan Li, Xiyang Dai, Harkirat Behl, Jianfeng Wang, Lu Yuan, et al · 2023
Later among the works it cites.
Instructblip: Towards general-purpose vision-language models with instruction tuning
Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale N Fung, and Steven Hoi · 2024
Closest in time.
Improving panoptic narrative grounding by harnessing semantic relationships and visual confirmation
Tianyu Guo, Haowei Wang, Yiwei Ma, Jiayi Ji, and Xiaoshuai Sun · 2024
Closest in time.
Opera: Alleviating hallucination in multi-modal large language models via over-trust penalty and retrospection-allocation
Qidong Huang, Xiaoyi Dong, Pan Zhang, Bin Wang, Conghui He, Jiaqi Wang, Dahua Lin, Weiming Zhang, and Nenghai Yu · 2024
Closest in time.
What matters when building vision-language models?
Hugo Laurençon, Léo Tronchon, Matthieu Cord, and Victor Sanh · 2024
Closest in time.
Deepseek-vl: Towards real-world vision-language understanding, 2024
Haoyu Lu, Wen Liu, Bo Zhang, Bingxuan Wang, Kai Dong, Bo Liu, Jingxiang Sun, Tongzheng Ren, Zhuoshu Li, Hao Yang, Yaofeng Sun, Chengqi Deng, Hanwei Xu, Zhenda Xie, and Chong Ruan · 2024
Closest in time.
Mm1: Methods, analysis & insights from multimodal llm pre-training
Brandon McKinzie, Zhe Gan, Jean-Philippe Fauconnier, Sam Dodge, Bowen Zhang, Philipp Dufter, Dhruti Shah, Xianzhi Du, Futang Peng, Floris Weers, et al · 2024
Closest in time.
Gemma: Open models based on gemini research and technology
Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, et al · 2024
Closest in time.
Glamm: Pixel grounding large multimodal model
Hanoona Rasheed, Muhammad Maaz, Sahal Shaji, Abdelrahman Shaker, Salman Khan, Hisham Cholakkal, Rao M. Anwer, Eric Xing, Ming-Hsuan Yang, and Fahad S. Khan · 2024
Closest in time.
Visual cot: Unleashing chain-of-thought reasoning in multi-modal language models
Hao Shao, Shengju Qian, Han Xiao, Guanglu Song, Zhuofan Zong, Letian Wang, Yu Liu, and Hongsheng Li · 2024
Closest in time.
Hpt 1.5 air: Best open-sourced 8b multimodal llm with llama 3, 2024
HyperGAI Team · 2024
Closest in time.
Lasagna: Language-based segmentation assistant for complex queries
Cong Wei, Haoxian Tan, Yujie Zhong, Yujiu Yang, and Lin Ma · 2024
Closest in time.
Gsva: Generalized segmentation via multimodal large language models
Zhuofan Xia, Dongchen Han, Yizeng Han, Xuran Pan, Shiji Song, and Gao Huang · 2024
Closest in time.
Mm-vet: Evaluating large multimodal models for integrated capabilities
Weihao Yu, Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Zicheng Liu, Xinchao Wang, and Lijuan Wang · 2024
Closest in time.
Segment everything everywhere all at once
Xueyan Zou, Jianwei Yang, Hao Zhang, Feng Li, Linjie Li, Jianfeng Wang, Lijuan Wang, Jianfeng Gao, and Yong Jae Lee · 2024
Closest in time.