Papers by Alexander Toshev
Pre-trained Language Models Do Not Help Auto-regressive Text-to-Image Generation (2024.emnlp-main)
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| Challenge: | Recent advances in image tokenizers have enabled text-to-image generation using auto-regressive methods, but these methods lack pre-trained language models for text-based models. |
| Approach: | They adapt a pre-trained language model for auto-regressive text-to-image generation and show that pre-train language models offer limited help. |
| Outcome: | The proposed model is compared with a pre-trained language model and shows that it is no more effective than random initialized models. |
STAIR: Learning Sparse Text and Image Representation in Grounded Tokens (2023.emnlp-main)
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Chen Chen, Bowen Zhang, Liangliang Cao, Jiguang Shen, Tom Gunter, Albin Jose, Alexander Toshev, Yantao Zheng, Jonathon Shlens, Ruoming Pang, Yinfei Yang
| Challenge: | State-of-the-art contrastive learning models like CLIP and ALIGN are less interpretable and suffer from inferior accuracy than dense representations. |
| Approach: | They extend CLIP and ALIGN models to build a sparse semantic representation that is interpretable and easy to integrate with existing retrieval systems. |
| Outcome: | The proposed model outperforms CLIP and ALIGN models on image and text retrieval tasks with a 4.9% and +4.3% improvement on COCO-5k textimage and imagetext retrieval respectively. |