The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
Entangled Transformer for Image Captioning
Open accessAbstract
In image captioning, the typical attention mechanisms are arduous to identify the equivalent visual signals especially when predicting highly abstract words. This phenomenon is known as the semantic gap between vision and language. This problem can be overcome by providing semantic attributes that are homologous to language. Thanks to the inherent recurrent nature and gated operating mechanism, Recurrent Neural Network (RNN) and its variants are the dominating architectures in image captioning. However, when designing elaborate attention mechanisms to integrate visual inputs and semantic attributes, RNN-like variants become unflexible due to their complexities. In this paper, we investigate a Transformer-based sequence modeling framework, built only with attention layers and feedforward layers. To bridge the semantic gap, we introduce EnTangled Attention (ETA) that enables the Transformer to exploit semantic and visual information simultaneously. Furthermore, Gated Bilateral Controller (GBC) is proposed to guide the interactions between the multimodal information. We name our model as ETA-Transformer. Remarkably, ETA-Transformer achieves state-of-the-art performance on the MSCOCO image captioning dataset. The ablation studies validate the improvements of our proposed modules.
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Cites
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Meshed-Memory Transformer for Image Captioning
Cornia, M., Stefanini, M., Baraldi, L., & Cucchiara, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01059 · Cited by 1,259
AI Choreographer: Music Conditioned 3D Dance Generation with AIST++
Li, R., Yang, S., Ross, D. A., & Kanazawa, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01315 · Cited by 553
“More recently, with the development of attention mechanism [84], transformer based networks achieve top performance for visual-text [95, 77, 19, 54, 38, 76, 76], visual-audio [26, 89] cross-modal sequence to sequence generation task.”
BackgroundAttention Bottlenecks for Multimodal Fusion
Nagrani, A., Yang, S., Arnab, A., Jansen, A., Schmid, C., & Sun, C. (2021) · arXiv · doi:10.48550/arxiv.2107.00135 · Cited by 400
From Show to Tell: A Survey on Deep Learning-Based Image Captioning
Stefanini, M., Cornia, M., Baraldi, L., Cascianelli, S., Fiameni, G., & Cucchiara, R. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3148210 · Cited by 373
Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods
Crothers, E., Japkowicz, N., & Viktor, H. (2022) · arXiv · doi:10.48550/arxiv.2210.07321 · Cited by 196
“D. INTERPRETABILITY AND FAIRNESS OF DETECTION METHODS Using machine-learning models to perform machinegenerated text detection to prevent abuse creates a situation where such models are likely to have a negative impact on flagged individuals.”
Metadata
- Authors
- Li, G., Zhu, L., Liu, P., & Yang, Y.
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Volume
- 2019
- Pages
- 8927-8936
- OpenAlex
- W2990818246
- Cited by
- 375
- References
- 50
- FWCI
- 15.77
- Type
- conference paper
- Language
- en
- Open access
- green
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