Fetching the paper…
Reading the bibliography…
Traditional computer vision generally solves each single task independently by a dedicated model with the task instruction implicitly designed in the model architecture, arising two limitations: (1) it leads to task-specific models, which require multiple models for different tasks and restrict the potential synergies from diverse tasks; (2) it leads to a pre-defined and fixed model interface that has limited interactivity and adaptability in following user' task instructions.
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao, “3d shapenets: A deep representation for volumetric shapes,” in
1920
Earlier work this paper cites.
L. Fei-Fei, R. Fergus, and P. Perona, “Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories,” in
2004
Earlier work this paper cites.
M.-E. Nilsback and A. Zisserman, “Automated flower classification over a large number of classes,” in
2008
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in
2009
Earlier work this paper cites.
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes (voc) challenge,”
2010
Earlier work this paper cites.
O. M. Parkhi, A. Vedaldi, A. Zisserman, and C. Jawahar, “Cats and dogs,” in
2012
Earlier work this paper cites.
J. Krause, J. Deng, M. Stark, and L. Fei-Fei, “Collecting a large-scale dataset of fine-grained cars,” 2013
2013
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,”
2014
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in
2014
Earlier work this paper cites.
S. Kazemzadeh, V. Ordonez, M. Matten, and T. Berg, “Referitgame: Referring to objects in photographs of natural scenes,” in
2014
Earlier work this paper cites.
P. Young, A. Lai, M. Hodosh, and J. Hockenmaier, “From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions,”
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
B. A. Plummer, L. Wang, C. M. Cervantes, J. C. Caicedo, J. Hockenmaier, and S. Lazebnik, “Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models,” in
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in
2016
Earlier work this paper cites.
F. Perazzi, J. Pont-Tuset, B. McWilliams, L. Van Gool, M. Gross, and A. Sorkine-Hornung, “A benchmark dataset and evaluation methodology for video object segmentation,” in
2016
Earlier work this paper cites.
G. Sigurdsson, O. Russakovsky, A. Farhadi, I. Laptev, and A. Gupta, “Much ado about time: Exhaustive annotation of temporal data,” in
2016
Earlier work this paper cites.
J. Mao, J. Huang, A. Toshev, O. Camburu, A. L. Yuille, and K. Murphy, “Generation and comprehension of unambiguous object descriptions,” in
2016
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,”
2017
Earlier work this paper cites.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,”
2017
Earlier work this paper cites.
R. Krishna, Y. Zhu, O. Groth, J. Johnson, K. Hata, J. Kravitz, S. Chen, Y. Kalantidis, L.-J. Li, D. A. Shamma
2017
Earlier work this paper cites.
R. Krishna, K. Hata, F. Ren, L. Fei-Fei, and J. Carlos Niebles, “Dense-captioning events in videos,” in
2017
Earlier work this paper cites.
Y. Goyal, T. Khot, D. Summers-Stay, D. Batra, and D. Parikh, “Making the v in vqa matter: Elevating the role of image understanding in visual question answering,” in
2017
Earlier work this paper cites.
D. Xu, Z. Zhao, J. Xiao, F. Wu, H. Zhang, X. He, and Y. Zhuang, “Video question answering via gradually refined attention over appearance and motion,” in
2017
Earlier work this paper cites.
Y. Jang, Y. Song, Y. Yu, Y. Kim, and G. Kim, “Tgif-qa: Toward spatio-temporal reasoning in visual question answering,” in
2017
Earlier work this paper cites.
A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Nießner, “Scannet: Richly-annotated 3d reconstructions of indoor scenes,” in
2017
Earlier work this paper cites.
A. Voulodimos, N. Doulamis, A. Doulamis, E. Protopapadakis
2018
Earlier work this paper cites.
P. Sharma, N. Ding, S. Goodman, and R. Soricut, “Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning,” in
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
D. Gurari, Q. Li, A. J. Stangl, A. Guo, C. Lin, K. Grauman, J. Luo, and J. P. Bigham, “Vizwiz grand challenge: Answering visual questions from blind people,” in
2018
Earlier work this paper cites.
J. J. Lau, S. Gayen, A. Ben Abacha, and D. Demner-Fushman, “A dataset of clinically generated visual questions and answers about radiology images,”
2018
Earlier work this paper cites.
A. Gupta, P. Dollar, and R. Girshick, “Lvis: A dataset for large vocabulary instance segmentation,” in
2019
Earlier work this paper cites.
K. Marino, M. Rastegari, A. Farhadi, and R. Mottaghi, “Ok-vqa: A visual question answering benchmark requiring external knowledge,” in
2019
Earlier work this paper cites.
H. Alamri, V. Cartillier, A. Das, J. Wang, A. Cherian, I. Essa, D. Batra, T. K. Marks, C. Hori, P. Anderson
2019
Earlier work this paper cites.
R. Zellers, Y. Bisk, A. Farhadi, and Y. Choi, “From recognition to cognition: Visual commonsense reasoning,” in
2019
Earlier work this paper cites.
A. Mishra, S. Shekhar, A. K. Singh, and A. Chakraborty, “Ocr-vqa: Visual question answering by reading text in images,” in
2019
Earlier work this paper cites.
S. Shao, Z. Li, T. Zhang, C. Peng, G. Yu, X. Zhang, J. Li, and J. Sun, “Objects365: A large-scale, high-quality dataset for object detection,” in
2019
Earlier work this paper cites.
D. A. Hudson and C. D. Manning, “Gqa: A new dataset for real-world visual reasoning and compositional question answering,” in
2019
Earlier work this paper cites.
A. Singh, V. Natarajan, M. Shah, Y. Jiang, X. Chen, D. Batra, D. Parikh, and M. Rohrbach, “Towards vqa models that can read,” in
2019
Earlier work this paper cites.
H. Agrawal, K. Desai, Y. Wang, X. Chen, R. Jain, M. Johnson, D. Batra, D. Parikh, S. Lee, and P. Anderson, “Nocaps: Novel object captioning at scale,” in
2019
Earlier work this paper cites.
C. D. Kim, B. Kim, H. Lee, and G. Kim, “Audiocaps: Generating captions for audios in the wild,” in
2019
Earlier work this paper cites.
X. Wang, J. Wu, J. Chen, L. Li, Y.-F. Wang, and W. Y. Wang, “Vatex: A large-scale, high-quality multilingual dataset for video-and-language research,” in
2019
Earlier work this paper cites.
Z. Yu, D. Xu, J. Yu, T. Yu, Z. Zhao, Y. Zhuang, and D. Tao, “Activitynet-qa: A dataset for understanding complex web videos via question answering,” in
2019
Earlier work this paper cites.
T. Li, Y. Gao, K. Wang, S. Guo, H. Liu, and H. Kang, “Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening,”
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
J. Zhang and D. Tao, “Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things,”
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
K. Drossos, S. Lipping, and T. Virtanen, “Clotho: An audio captioning dataset,” in
2020
Earlier work this paper cites.
2020
Cited alongside, same era.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
S. Changpinyo, P. Sharma, N. Ding, and R. Soricut, “Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts,” in
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2021
Cited alongside, same era.
H. Chen, W. Xie, T. Afouras, A. Nagrani, A. Vedaldi, and A. Zisserman, “Localizing visual sounds the hard way,” in
2021
Cited alongside, same era.
M. Bain, A. Nagrani, G. Varol, and A. Zisserman, “Frozen in time: A joint video and image encoder for end-to-end retrieval,” in
2021
Cited alongside, same era.
B. Liu, L.-M. Zhan, L. Xu, L. Ma, Y. Yang, and X.-M. Wu, “Slake: A semantically-labeled knowledge-enhanced dataset for medical visual question answering,” in
2021
Cited alongside, same era.
M. Mathew, D. Karatzas, and C. Jawahar, “Docvqa: A dataset for vqa on document images,” in
2021
Cited alongside, same era.
R. Tanaka, K. Nishida, and S. Yoshida, “Visualmrc: Machine reading comprehension on document images,” in
2021
Cited alongside, same era.
2022
Cited alongside, same era.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in
2022
Cited alongside, same era.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
B. Wang, F. Wu, X. Han, J. Peng, H. Zhong, P. Zhang, X. Dong, W. Li, W. Li, J. Wang
2023
Closest in time.
L. Li, Y. Yin, S. Li, L. Chen, P. Wang, S. Ren, M. Li, Y. Yang, J. Xu, X. Sun
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Z. Yin, J. Wang, J. Cao, Z. Shi, D. Liu, M. Li, L. Sheng, L. Bai, X. Huang, Z. Wang
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
W. Gao, Z. Deng, Z. Niu, F. Rong, C. Chen, Z. Gong, W. Zhang, D. Xiao, F. Li, Z. Cao
2023
Closest in time.
B. Peng, C. Li, P. He, M. Galley, and J. Gao, “Instruction tuning with gpt-4,”
2023
Closest in time.
T. Luo, C. Rockwell, H. Lee, and J. Johnson, “Scalable 3d captioning with pretrained models,”
2023
Closest in time.
Z. Xiao, Y. Chen, L. Zhang, J. Yao, Z. Wu, X. Yu, Y. Pan, L. Zhao, C. Ma, X. Liu
2023
Closest in time.
2023
Closest in time.
W. Wang, Z. Chen, X. Chen, J. Wu, X. Zhu, G. Zeng, P. Luo, T. Lu, J. Zhou, Y. Qiao
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
R. Dong, C. Han, Y. Peng, Z. Qi, Z. Ge, J. Yang, L. Zhao, J. Sun, H. Zhou, H. Wei
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
W. Dai, J. Li, D. Li, A. M. H. Tiong, J. Zhao, W. Wang, B. Li, P. Fung, and S. Hoi, “Instructblip: Towards general-purpose vision-language models with instruction tuning,” 2023
2023
Closest in time.
P. Zhang, X. D. B. Wang, Y. Cao, C. Xu, L. Ouyang, Z. Zhao, S. Ding, S. Zhang, H. Duan, H. Yan
2023
Closest in time.
P. Gao, J. Han, R. Zhang, Z. Lin, S. Geng, A. Zhou, W. Zhang, P. Lu, C. He, X. Yue
2023
Closest in time.
H. Liu, C. Li, Y. Li, and Y. J. Lee, “Improved baselines with visual instruction tuning,”
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
W. Wang, Q. Lv, W. Yu, W. Hong, J. Qi, Y. Wang, J. Ji, Z. Yang, L. Zhao, X. Song
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
J. Han, R. Zhang, W. Shao, P. Gao, P. Xu, H. Xiao, K. Zhang, C. Liu, S. Wen, Z. Guo
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
J. Ye, A. Hu, H. Xu, Q. Ye, M. Yan, Y. Dan, C. Zhao, G. Xu, C. Li, J. Tian
2023
Closest in time.