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Found 4 papers, 3 papers with code
Zoom-shot: Fast and Efficient Unsupervised Zero-Shot Transfer of CLIP to Vision Encoders with Multimodal Loss
To address this issue, we propose Zoom-shot, a novel method for transferring the zero-shot capabilities of CLIP to any pre-trained vision encoder.
SafeSea: Synthetic Data Generation for Adverse & Low Probability Maritime Conditions
Lastly, we observed that a maritime object detection model faced challenges in detecting objects in stormy sea backgrounds, emphasizing the impact of weather conditions on detection accuracy.
Diversity is Definitely Needed: Improving Model-Agnostic Zero-shot Classification via Stable Diffusion
In this work, we investigate the problem of Model-Agnostic Zero-Shot Classification (MA-ZSC), which refers to training non-specific classification architectures (downstream models) to classify real images without using any real images during training.
Does Interference Exist When Training a Once-For-All Network?
To show this, we propose a simple-yet-effective method called Random Subnet Sampling (RSS), which does not have mitigation on the interference effect.
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