Enhanced Information Fusion Network for Crowd Counting
This work addresses crowd counting for computer vision applications, presenting an incremental improvement in feature fusion methods.
The paper tackles the problem of information redundancy in crowd counting by proposing a cross-column feature fusion network with an Information Fusion Module, achieving comparable results with state-of-the-art models in dataset transfer experiments.
In recent years, crowd counting, a technique for predicting the number of people in an image, becomes a challenging task in computer vision. In this paper, we propose a cross-column feature fusion network to solve the problem of information redundancy in columns. We introduce the Information Fusion Module (IFM) which provides a channel for information flow to help different columns to obtain significant information from another column. Through this channel, different columns exchange information with each other and extract useful features from the other column to enhance key information. Hence, there is no need for columns to pay attention to all areas in the image. Each column can be responsible for different regions, thereby reducing the burden of each column. In experiments, the generalizability of our model is more robust and the results of transferring between different datasets acheive the comparable results with the state-of-the-art models.