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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

Federated Learning for Vision-and-Language Grounding Problems

February 1, 2023

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Authors

Fenglin Liu

Peking University


Xian Wu

Tencent


Shen Ge

Tencent


Wei Fan

Tencent


Yuexian Zou

Peking University


DOI:

10.1609/aaai.v34i07.6824


Abstract:

Recently, vision-and-language grounding problems, e.g., image captioning and visual question answering (VQA), has attracted extensive interests from both academic and industrial worlds. However, given the similarity of these tasks, the efforts to obtain better results by combining the merits of their algorithms are not well studied. Inspired by the recent success of federated learning, we propose a federated learning framework to obtain various types of image representations from different tasks, which are then fused together to form fine-grained image representations. The representations merge useful features from different vision-and-language grounding problems, and are thus much more powerful than the original representations alone in individual tasks. To learn such image representations, we propose the Aligning, Integrating and Mapping Network (aimNet). The aimNet is validated on three federated learning settings, which include horizontal federated learning, vertical federated learning, and federated transfer learning. Experiments of aimNet-based federated learning framework on two representative tasks, i.e., image captioning and VQA, demonstrate the effective and universal improvements of all metrics over the baselines. In image captioning, we are able to get 14% and 13% relative gain on the task-specific metrics CIDEr and SPICE, respectively. In VQA, we could also boost the performance of strong baselines by up to 3%.

Topics: AAAI

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HOW TO CITE:

Fenglin Liu||Xian Wu||Shen Ge||Wei Fan||Yuexian Zou Federated Learning for Vision-and-Language Grounding Problems Proceedings of the AAAI Conference on Artificial Intelligence (2020) 11572-11579.

Fenglin Liu||Xian Wu||Shen Ge||Wei Fan||Yuexian Zou Federated Learning for Vision-and-Language Grounding Problems AAAI 2020, 11572-11579.

Fenglin Liu||Xian Wu||Shen Ge||Wei Fan||Yuexian Zou (2020). Federated Learning for Vision-and-Language Grounding Problems. Proceedings of the AAAI Conference on Artificial Intelligence, 11572-11579.

Fenglin Liu||Xian Wu||Shen Ge||Wei Fan||Yuexian Zou. Federated Learning for Vision-and-Language Grounding Problems. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.11572-11579.

Fenglin Liu||Xian Wu||Shen Ge||Wei Fan||Yuexian Zou. 2020. Federated Learning for Vision-and-Language Grounding Problems. "Proceedings of the AAAI Conference on Artificial Intelligence". 11572-11579.

Fenglin Liu||Xian Wu||Shen Ge||Wei Fan||Yuexian Zou. (2020) "Federated Learning for Vision-and-Language Grounding Problems", Proceedings of the AAAI Conference on Artificial Intelligence, p.11572-11579

Fenglin Liu||Xian Wu||Shen Ge||Wei Fan||Yuexian Zou, "Federated Learning for Vision-and-Language Grounding Problems", AAAI, p.11572-11579, 2020.

Fenglin Liu||Xian Wu||Shen Ge||Wei Fan||Yuexian Zou. "Federated Learning for Vision-and-Language Grounding Problems". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.11572-11579.

Fenglin Liu||Xian Wu||Shen Ge||Wei Fan||Yuexian Zou. "Federated Learning for Vision-and-Language Grounding Problems". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 11572-11579.

Fenglin Liu||Xian Wu||Shen Ge||Wei Fan||Yuexian Zou. Federated Learning for Vision-and-Language Grounding Problems. AAAI[Internet]. 2020[cited 2023]; 11572-11579.


ISSN: 2374-3468


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