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Abstract: Multi-modal models require aligned, shared embedding spaces. However, common CLIP-based approaches need large amounts of samples and do not natively support 3D or tabular data, both of which ...
The training process consists of two main stages: Stage 1: Few-shot training on the source domain to obtain a source model. Stage 2: Adaptation of the model to the target domain for optimal ...
Open-world object detection (OWOD) extends traditional object detection to identifying both known and unknown object, necessitating continuous model adaptation as new annotations emerge. Current ...
TL;DR. To address the negation issue in CLIP, we propose data generation pipelines and validate their effectiveness using public and novel benchmarks. Our approach enhances negation awareness and ...
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