Search Results for author: Jiaqi Fan

Found 7 papers, 2 papers with code

Understanding the Multi-modal Prompts of the Pre-trained Vision-Language Model

no code implementations18 Dec 2023 Shuailei Ma, Chen-Wei Xie, Ying WEI, Siyang Sun, Jiaqi Fan, Xiaoyi Bao, Yuxin Guo, Yun Zheng

$(2)$ the prompts learn a bias term during the update of token embeddings, allowing the model to adapt to the target domain.

Language Modelling

A Simple Knowledge Distillation Framework for Open-world Object Detection

no code implementations14 Dec 2023 Shuailei Ma, Yuefeng Wang, Ying WEI, Jiaqi Fan, Xinyu Sun, Peihao Chen, Enming Zhang

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.

Knowledge Distillation Object +3

Detecting the open-world objects with the help of the Brain

1 code implementation21 Mar 2023 Shuailei Ma, Yuefeng Wang, Ying WEI, Peihao Chen, Zhixiang Ye, Jiaqi Fan, Enming Zhang, Thomas H. Li

We propose leveraging the VL as the ``Brain'' of the open-world detector by simply generating unknown labels.

Object object-detection +1

Predictions of photophysical properties of phosphorescent platinum(II) complexes based on ensemble machine learning approach

no code implementations8 Jan 2023 Shuai Wang, ChiYung Yam, Shuguang Chen, Lihong Hu, Liping Li, Faan-Fung Hung, Jiaqi Fan, Chi-Ming Che, Guanhua Chen

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.

Ensemble Learning

CAT: LoCalization and IdentificAtion Cascade Detection Transformer for Open-World Object Detection

no code implementations CVPR 2023 Shuailei Ma, Yuefeng Wang, Jiaqi Fan, Ying WEI, Thomas H. Li, Hongli Liu, Fanbing Lv

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.

object-detection Open World Object Detection

Interpretable Compositional Convolutional Neural Networks

1 code implementation9 Jul 2021 Wen Shen, Zhihua Wei, Shikun Huang, BinBin Zhang, Jiaqi Fan, Ping Zhao, Quanshi Zhang

The reasonable definition of semantic interpretability presents the core challenge in explainable AI.

Efficient Unpaired Image Dehazing with Cyclic Perceptual-Depth Supervision

no code implementations10 Jul 2020 Chen Liu, Jiaqi Fan, Guosheng Yin

Image dehazing without paired haze-free images is of immense importance, as acquiring paired images often entails significant cost.

Image Dehazing

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