6 citations · 13 across the 10 of their papers we have counts for
7 papers · 1 filter
Universal Object Detection with Large Vision Model
Feng Lin, Wenze Hu, Yaowei Wang +5
Over the past few years, there has been growing interest in developing a broad, universal, and general-purpose computer vision system. Such systems have the potential to address a…
NAR-Former: Neural Architecture Representation Learning towards Holistic Attributes Prediction
Yun Yi, Haokui Zhang, Wenze Hu +2
With the wide and deep adoption of deep learning models in real applications, there is an increasing need to model and learn the representations of the neural networks themselves.…
Fcaformer: Forward Cross Attention in Hybrid Vision Transformer
Haokui Zhang, Wenze Hu, Xiaoyu Wang
Currently, one main research line in designing a more efficient vision transformer is reducing the computational cost of self attention modules by adopting sparse attention or usin…
ParCNetV2: Oversized Kernel with Enhanced Attention
Ruihan Xu, Haokui Zhang, Wenze Hu +2
Transformers have shown great potential in various computer vision tasks. By borrowing design concepts from transformers, many studies revolutionized CNNs and showed remarkable res…
Fast-ParC: Capturing Position Aware Global Feature for ConvNets and ViTs
Tao Yang, Haokui Zhang, Wenze Hu +2
Transformer models have made tremendous progress in various fields in recent years. In the field of computer vision, vision transformers (ViTs) also become strong alternatives to c…
ALBench: A Framework for Evaluating Active Learning in Object Detection
Zhanpeng Feng, Shiliang Zhang, Rinyoichi Takezoe +5
Active learning is an important technology for automated machine learning systems. In contrast to Neural Architecture Search (NAS) which aims at automating neural network architect…
ParC-Net: Position Aware Circular Convolution with Merits from ConvNets and Transformer
Haokui Zhang, Wenze Hu, Xiaoyu Wang
Recently, vision transformers started to show impressive results which outperform large convolution based models significantly. However, in the area of small models for mobile or r…