Search Results for author: Qiushan Guo

Found 12 papers, 6 papers with code

Plot2Code: A Comprehensive Benchmark for Evaluating Multi-modal Large Language Models in Code Generation from Scientific Plots

no code implementations13 May 2024 Chengyue Wu, Yixiao Ge, Qiushan Guo, Jiahao Wang, Zhixuan Liang, Zeyu Lu, Ying Shan, Ping Luo

Furthermore, we propose three automatic evaluation metrics, including code pass rate, text-match ratio, and GPT-4V overall rating, for a fine-grained assessment of the output code and rendered images.

Code Generation Descriptive

RegionGPT: Towards Region Understanding Vision Language Model

no code implementations CVPR 2024 Qiushan Guo, Shalini De Mello, Hongxu Yin, Wonmin Byeon, Ka Chun Cheung, Yizhou Yu, Ping Luo, Sifei Liu

Vision language models (VLMs) have experienced rapid advancements through the integration of large language models (LLMs) with image-text pairs, yet they struggle with detailed regional visual understanding due to limited spatial awareness of the vision encoder, and the use of coarse-grained training data that lacks detailed, region-specific captions.

Language Modelling

Multi-Level Contrastive Learning for Dense Prediction Task

1 code implementation4 Apr 2023 Qiushan Guo, Yizhou Yu, Yi Jiang, Jiannan Wu, Zehuan Yuan, Ping Luo

We extend our pretext task to supervised pre-training, which achieves a similar performance to self-supervised learning.

Contrastive Learning Self-Supervised Learning

Rethinking Resolution in the Context of Efficient Video Recognition

1 code implementation26 Sep 2022 Chuofan Ma, Qiushan Guo, Yi Jiang, Zehuan Yuan, Ping Luo, Xiaojuan Qi

Our key finding is that the major cause of degradation is not information loss in the down-sampling process, but rather the mismatch between network architecture and input scale.

Knowledge Distillation Video Recognition

Scale-Equivalent Distillation for Semi-Supervised Object Detection

no code implementations CVPR 2022 Qiushan Guo, Yao Mu, Jianyu Chen, Tianqi Wang, Yizhou Yu, Ping Luo

Further, we overcome these challenges by introducing a novel approach, Scale-Equivalent Distillation (SED), which is a simple yet effective end-to-end knowledge distillation framework robust to large object size variance and class imbalance.

Knowledge Distillation Object +3

Scale-Invariant Teaching for Semi-Supervised Object Detection

no code implementations29 Sep 2021 Qiushan Guo, Yizhou Yu, Ping Luo

Furthermore, the limited annotations in semi-supervised learning scale up the challenges: large variance of object sizes and class imbalance (i. e., the extreme ratio between background and object), hindering the performance of prior arts.

Object object-detection +1

Online Knowledge Distillation via Collaborative Learning

1 code implementation CVPR 2020 Qiushan Guo, Xinjiang Wang, Yichao Wu, Zhipeng Yu, Ding Liang, Xiaolin Hu, Ping Luo

This work presents an efficient yet effective online Knowledge Distillation method via Collaborative Learning, termed KDCL, which is able to consistently improve the generalization ability of deep neural networks (DNNs) that have different learning capacities.

Knowledge Distillation Model Compression +4

Dynamic Recursive Neural Network

no code implementations CVPR 2019 Qiushan Guo, Zhipeng Yu, Yichao Wu, Ding Liang, Haoyu Qin, Junjie Yan

This paper proposes the dynamic recursive neural network (DRNN), which simplifies the duplicated building blocks in deep neural network.

MSFD:Multi-Scale Receptive Field Face Detector

no code implementations11 Mar 2019 Qiushan Guo, Yuan Dong, Yu Guo, Hongliang Bai

We simultaneously propose an anchor assignment strategy which can cover faces with a wide range of scales to improve the recall rate of small faces and rotated faces.

Cannot find the paper you are looking for? You can Submit a new open access paper.