$(2)$ the prompts learn a bias term during the update of token embeddings, allowing the model to adapt to the target domain.
Surprisingly, we observe that the combination of a simple knowledge distillation approach and the automatic pseudo-labeling mechanism in OWOD can achieve better performance for unknown object detection, even with a small amount of data.
We propose leveraging the VL as the ``Brain'' of the open-world detector by simply generating unknown labels.
Here, we develop a general protocol for accurate predictions of emission wavelength, radiative decay rate constant, and PL quantum yield for phosphorescent Pt(II) emitters based on the combination of first-principles quantum mechanical method, machine learning (ML) and experimental calibration.
Open-world object detection (OWOD), as a more general and challenging goal, requires the model trained from data on known objects to detect both known and unknown objects and incrementally learn to identify these unknown objects.
Image dehazing without paired haze-free images is of immense importance, as acquiring paired images often entails significant cost.