|
| | | | |
| | | |
| Stat |
Members: 3645
Articles: 2'501'711
Articles rated: 2609
19 April 2024 |
|
| | | | |
|
Article overview
| |
|
|
Towards Efficient and Elastic Visual Question Answering with Doubly Slimmable Transformer | | Zhou Yu
; Zitian Jin
; Jun Yu
; Mingliang Xu
; Jianping Fan
; | | Date: |
24 Mar 2022 | | Abstract: | Transformer-based approaches have shown great success in visual question
answering (VQA). However, they usually require deep and wide models to
guarantee good performance, making it difficult to deploy on
capacity-restricted platforms. It is a challenging yet valuable task to design
an elastic VQA model that supports adaptive pruning at runtime to meet the
efficiency constraints of diverse platforms. In this paper, we present the
Doubly Slimmable Transformer (DST), a general framework that can be seamlessly
integrated into arbitrary Transformer-based VQA models to train one single
model once and obtain various slimmed submodels of different widths and depths.
Taking two typical Transformer-based VQA approaches, i.e., MCAN and UNITER, as
the reference models, the obtained slimmable MCAN_DST and UNITER_DST models
outperform the state-of-the-art methods trained independently on two benchmark
datasets. In particular, one slimmed MCAN_DST submodel achieves a comparable
accuracy on VQA-v2, while being 0.38x smaller in model size and having 0.27x
fewer FLOPs than the reference MCAN model. The smallest MCAN_DST submodel has
9M parameters and 0.16G FLOPs in the inference stage, making it possible to be
deployed on edge devices. | | Source: | arXiv, 2203.12814 | | Services: | Forum | Review | PDF | Favorites | |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
browser Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)
|
| |
|
|
|
|
News, job offers and information for researchers and scientists:
| |
REGISTER