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Found 3 papers, 2 papers with code
SYNC-CLIP: Synthetic Data Make CLIP Generalize Better in Data-Limited Scenarios
Prompt learning is a powerful technique for transferring Vision-Language Models (VLMs) such as CLIP to downstream tasks.
Prompt-based test-time real image dehazing: a novel pipeline
We experimentally find that given a dehazing model trained on synthetic data, by fine-tuning the statistics (i. e., mean and standard deviation) of encoding features, PTTD is able to narrow the domain gap, boosting the performance of real image dehazing.
Accurate and lightweight dehazing via multi-receptive-field non-local network and novel contrastive regularization
Thus, a multi-receptive-field non-local network (MRFNLN) consisting of the multi-stream feature attention block (MSFAB) and cross non-local block (CNLB) is presented in this paper.
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