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- [2303. 15343] Sigmoid Loss for Language Image Pre-Training
We propose a simple pairwise Sigmoid loss for Language-Image Pre-training (SigLIP) Unlike standard contrastive learning with softmax normalization, the sigmoid loss operates solely on image-text pairs and does not require a global view of the pairwise similarities for normalization
- Sigmoid Loss for Language Image Pre-Training - CVF Open Access
We propose a simple pairwise sigmoid loss for image-text pre-training Unlike standard contrastive learning with softmax normalization, the sigmoid loss operates solely on image-text pairs and does not require a global view of the pairwise similarities for normalization
- Sigmoid Loss for Language Image Pre-Training - IEEE Xplore
We propose a simple pairwise sigmoid loss for imagetext pre-training Unlike standard contrastive learning with softmax normalization, the sigmoid loss operates
- Paper page - Sigmoid Loss for Language Image Pre-Training
We propose a simple pairwise sigmoid loss for image-text pre-training Unlike standard contrastive learning with softmax normalization, the sigmoid loss operates solely on image-text pairs and does not require a global view of the pairwise similarities for normalization
- Sigmoid Loss for Language Image Pre-Training - Semantic Scholar
Sigmoid loss outperforms the softmax loss significantly with small batch sizes, and performs similarly at larger batch sizes We successfully trained an SigLiT model with up to one million batch size
- Sigmoid Loss for Language Image Pre-Training - ResearchGate
Researchers have explored this problem and made significant progress This paper surveys recent advances and new frontiers in vision-language pre-training (VLP), including image-text and
- ICCV 2023 – Sigmoid Loss for Language Image Pre-Training
by Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, Lucas Beyer The paper introduces a pairwise Sigmoid loss for Language-Image Pre-training (SigLIP), which operates on image-text pairs and allows for scaling up batch size without the need for global pairwise similarities
- [Journal club] Sigmoid Loss for Language Image Pre-Training
Alexander Kolesnikov, Lucas Beyer⋆ Google DeepMind 慶應義塾大学 杉浦孔明研究室 小槻誠太郎 X Zhai, B Mustafa, A Kolesnikov, and L Beyer, “Sigmoid Loss for Language Image Pre-Training,” in ICCV, 2023, pp 11975–11986
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