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Turn Down the Noise: Leveraging Diffusion Models for Test-time Adaptation via Pseudo-label Ensembling
The goal of test-time adaptation is to adapt a source-pretrained model to a continuously changing target domain without relying on any source data.
Bridging the Gap: Exploring the Capabilities of Bridge-Architectures for Complex Visual Reasoning Tasks
In recent times there has been a surge of multi-modal architectures based on Large Language Models, which leverage the zero shot generation capabilities of LLMs and project image embeddings into the text space and then use the auto-regressive capacity to solve tasks such as VQA, captioning, and image retrieval.
HateProof: Are Hateful Meme Detection Systems really Robust?
Empirically, we find a noticeable performance drop of as high as 10% in the macro-F1 score for certain attacks.
SAC: Semantic Attention Composition for Text-Conditioned Image Retrieval
The ability to efficiently search for images is essential for improving the user experiences across various products.
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