Contemporary state-of-the-art detectors typically employ multi-scale features to detect objects at varying scales. A common approach for extracting multi-scale features is utilizing a feature pyramid network, implemented by fusing features through one-way summation or concatenation. As a result, they cannot take into account the information from both the upper and lower adjacent layers simultaneously, thus impairing the representation of multi-scale features. In this paper, we first propose a converging and radiating pathway to enhance the representation of features by fusing features from the adjacent upper and lower layers directly. Second, to optimize the feature fusion in the proposed pyramid pathway, we replace the conventional summation operation in the fusion process with weighted summation, adaptive channel fusion (ACF), and adaptive spatial fusion (ASF) operations. Finally, a weighted converging and radiating feature pyramid network (CRFPN) is developed based on the above-described methods, achieving more competitive results than other state-of-the-art feature pyramid networks. Furthermore, we have integrated the proposed CRFPN into various detectors and backbones and experimentally verified the adaptability and generalizability of our method. The code is available at https://github.com/gmpan66/CRFPN.

With the development of Generative Adversarial Network, image-based virtual try-on methods have made great progress. However, limited work has explored the task of video-based virtual try-on while it is important in real-world applications. Most existing video-based virtual try-on methods usually require clothing templates and they can only generate blurred and low-resolution results. To address these challenges, we propose a Memory-based Video virtual Try-On Network (MV-TON), which seamlessly transfers desired clothes to a target person without using any clothing templates and generates high-resolution realistic videos. Specifically, MV-TON consists of two modules: 1) a try-on module that transfers the desired clothes from model images to frame images by pose alignment and region-wise replacing of pixels; 2) a memory refinement module that learns to embed the existing generated frames into the latent space as external memory for the following frame generation. Experimental results show the effectiveness of our method in the video virtual try-on task and its superiority over other existing methods.