Built on

S. Antol, A. Agrawal, J. Lu, M. Mitchell, D. Batra, C. Lawrence Zitnick, and D. Parikh, “Vqa: Visual question answering,”
2015
Earlier work this paper cites.
A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,”
Original
2015
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,”
Original
2016
Earlier work this paper cites.
S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee, “Generative adversarial text to image synthesis,”
2016
Earlier work this paper cites.
K. Saito, A. Shin, Y. Ushiku, and T. Harada, “Dualnet: Domain-invariant network for visual question answering,”
Original
2016
Earlier work this paper cites.
A. Fukui, D. Park, D. Yang, A. Rohrbach, T. Darrell, and M. Rohrbach, “Multimodal compact bilinear pooling for visual question answering and visual grounding,”
Original
2016
Earlier work this paper cites.

Similar

J. Lu, J. Yang, D. Batra, and D. Parikh, “Hierarchical question-image co-attention for visual question answering,”
2016
Cited alongside, same era.
M. Arjovsky and L. Bottou, “Towards principled methods for training generative adversarial networks,”
2017
Cited alongside, same era.
W. Ding, S. Li, G. Zhang, X. Lei, and H. Qian, “Vehicle pose and shape estimation through multiple monocular vision,” in
2018
Cited alongside, same era.
Z. Li, P. Zhou, Y. Zhang, and L. Gao, “Joint coverage and power control in highly dynamic and massive uav networks: An aggregative game-theoretic learning approach,”
Original
2019
Cited alongside, same era.
L. Lin, W. Wu, Z. Shangguan, S. Wshah, R. Elmoudi, and B. Xu, “Hpt-rl: Calibrating power system models based on hierarchical parameter tuning and reinforcement learning,” in
2020

Then

A. Zhu, J. Li, and C. Lu, “Pseudo view representation learning for monocular rgb-d human pose and shape estimation,”
2021
Later among the works it cites.
T. Deng, G. Shen, T. Qin, J. Wang, W. Zhao, J. Wang, D. Wang, and W. Chen, “Plgslam: Progressive neural scene represenation with local to global bundle adjustment,”
Original
2023
Later among the works it cites.
J. Yao, C. Li, K. Sun, Y. Cai, H. Li, W. Ouyang, and H. Li, “Ndc-scene: Boost monocular 3d semantic scene completion in normalized devicecoordinates space,” in
2023
Later among the works it cites.
X. Song, D. Wu, B. Zhang, Z. Peng, B. Dang, F. Pan, and Z. Wu, “Zeroprompt: Streaming acoustic encoders are zero-shot masked lms,” in
2023
Later among the works it cites.
H. Chen, W. Huang, Y. Ni, S. Yun, F. Wen, H. Latapie, and M. Imani, “Taskclip: Extend large vision-language model for task oriented object detection,”

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2024

Exploring Diverse Methods in Visual Question Answering

Li, Panfeng, Yang, Qikai, Geng, Xieming et al.

Understand

This study explores innovative methods for improving Visual Question Answering (VQA) using Generative Adversarial Networks (GANs), autoencoders, and attention mechanisms.

Leveraging a balanced VQA dataset, we investigate three distinct strategies.
Firstly, GAN-based approaches aim to generate answer embeddings conditioned on image and question inputs, showing potential but struggling with more complex tasks.
Secondly, autoencoder-based techniques focus on learning optimal embeddings for questions and images, achieving comparable results with GAN due to better ability on complex questions.

Built on

S. Antol, A. Agrawal, J. Lu, M. Mitchell, D. Batra, C. Lawrence Zitnick, and D. Parikh, “Vqa: Visual question answering,”
2015
Earlier work this paper cites.
A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,”
Original
2015
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,”
Original
2016
Earlier work this paper cites.
S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee, “Generative adversarial text to image synthesis,”
2016
Earlier work this paper cites.
K. Saito, A. Shin, Y. Ushiku, and T. Harada, “Dualnet: Domain-invariant network for visual question answering,”
Original
2016
Earlier work this paper cites.
A. Fukui, D. Park, D. Yang, A. Rohrbach, T. Darrell, and M. Rohrbach, “Multimodal compact bilinear pooling for visual question answering and visual grounding,”
Original
2016
Earlier work this paper cites.

Similar

J. Lu, J. Yang, D. Batra, and D. Parikh, “Hierarchical question-image co-attention for visual question answering,”
2016
Cited alongside, same era.
M. Arjovsky and L. Bottou, “Towards principled methods for training generative adversarial networks,”
2017
Cited alongside, same era.
W. Ding, S. Li, G. Zhang, X. Lei, and H. Qian, “Vehicle pose and shape estimation through multiple monocular vision,” in
2018
Cited alongside, same era.
Z. Li, P. Zhou, Y. Zhang, and L. Gao, “Joint coverage and power control in highly dynamic and massive uav networks: An aggregative game-theoretic learning approach,”
Original
2019
Cited alongside, same era.
L. Lin, W. Wu, Z. Shangguan, S. Wshah, R. Elmoudi, and B. Xu, “Hpt-rl: Calibrating power system models based on hierarchical parameter tuning and reinforcement learning,” in
2020

Then

A. Zhu, J. Li, and C. Lu, “Pseudo view representation learning for monocular rgb-d human pose and shape estimation,”
2021
Later among the works it cites.
T. Deng, G. Shen, T. Qin, J. Wang, W. Zhao, J. Wang, D. Wang, and W. Chen, “Plgslam: Progressive neural scene represenation with local to global bundle adjustment,”
Original
2023
Later among the works it cites.
J. Yao, C. Li, K. Sun, Y. Cai, H. Li, W. Ouyang, and H. Li, “Ndc-scene: Boost monocular 3d semantic scene completion in normalized devicecoordinates space,” in
2023
Later among the works it cites.
X. Song, D. Wu, B. Zhang, Z. Peng, B. Dang, F. Pan, and Z. Wu, “Zeroprompt: Streaming acoustic encoders are zero-shot masked lms,” in
2023
Later among the works it cites.
H. Chen, W. Huang, Y. Ni, S. Yun, F. Wen, H. Latapie, and M. Imani, “Taskclip: Extend large vision-language model for task oriented object detection,”

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Cited alongside, same era.

H. A. Arief, M. Arief, G. Zhang, Z. Liu, M. Bhat, U. G. Indahl, H. Tveite, and D. Zhao, “Sane: Smart annotation and evaluation tools for point cloud data,”

2020

Cited alongside, same era.

2024

Closest in time.

C. Wang, Y. Yang, R. Li, D. Sun, R. Cai, Y. Zhang, C. Fu, and L. Floyd, “Adapting llms for efficient context processing through soft prompt compression,”

Original

2024

Closest in time.