Search Results for author:
Found 7 papers, 3 papers with code
Understanding the Multi-modal Prompts of the Pre-trained Vision-Language Model
In this paper, we conduct a direct analysis of the multi-modal prompts by asking the following questions: $(i)$ How do the learned multi-modal prompts improve the recognition performance?
SKDF: A Simple Knowledge Distillation Framework for Distilling Open-Vocabulary Knowledge to Open-world Object Detector
Ablation experiments demonstrate that both of them are effective in mitigating the impact of open-world knowledge distillation on the learning of known objects.
Detecting the open-world objects with the help of the Brain
We propose leveraging the VL as the ``Brain'' of the open-world detector by simply generating unknown labels.
Predictions of photophysical properties of phosphorescent platinum(II) complexes based on ensemble machine learning approach
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.
CAT: LoCalization and IdentificAtion Cascade Detection Transformer for Open-World Object Detection
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.
Interpretable Compositional Convolutional Neural Networks
The reasonable definition of semantic interpretability presents the core challenge in explainable AI.
Efficient Unpaired Image Dehazing with Cyclic Perceptual-Depth Supervision
Image dehazing without paired haze-free images is of immense importance, as acquiring paired images often entails significant cost.
