Jingwen Fu

Jingwen Fu, Ming Xiao, Mikael Skoglund et al.

Due to strict rate and reliability demands, wireless image transmission remains difficult for both classical layered designs and joint source-channel coding (JSCC), especially under low latency. Diffusion-based generative decoders can deliver strong perceptual quality by leveraging learned image priors, but iterative stochastic denoising leads to high decoding delay. To enable low-latency decoding, we propose a flow-matching (FM) generative decoder under a new land-then-transport (LTT) paradigm that tightly integrates the physical wireless channel into a continuous-time probability flow. For AWGN channels, we build a Gaussian smoothing path whose noise schedule indexes effective noise levels, and derive a closed-form teacher velocity field along this path. A neural-network student vector field is trained by conditional flow matching, yielding a deterministic, channel-aware ODE decoder with complexity linear in the number of ODE steps. At inference, it only needs an estimate of the effective noise variance to set the ODE starting time. We further show that Rayleigh fading and MIMO channels can be mapped, via linear MMSE equalization and singular-value-domain processing, to AWGN-equivalent channels with calibrated starting times. Therefore, the same probability path and trained velocity field can be reused for Rayleigh and MIMO without retraining. Experiments on MNIST, Fashion-MNIST, and DIV2K over AWGN, Rayleigh, and MIMO demonstrate consistent gains over JPEG2000+LDPC, DeepJSCC, and diffusion-based baselines, while achieving good perceptual quality with only a few ODE steps. Overall, LTT provides a deterministic, physically interpretable, and computation-efficient framework for generative wireless image decoding across diverse channels.

Pengna Li, Kangyi Wu, Jingwen Fu et al.

Image-goal navigation aims to steer an agent towards the goal location specified by an image. Most prior methods tackle this task by learning a navigation policy, which extracts visual features of goal and observation images, compares their similarity and predicts actions. However, if the agent is in a different room from the goal image, it's extremely challenging to identify their similarity and infer the likely goal location, which may result in the agent wandering around. Intuitively, when humans carry out this task, they may roughly compare the current observation with the goal image, having an approximate concept of whether they are in the same room before executing the actions. Inspired by this intuition, we try to imitate human behaviour and propose a Room Expert Guided Image-Goal Navigation model (REGNav) to equip the agent with the ability to analyze whether goal and observation images are taken in the same room. Specifically, we first pre-train a room expert with an unsupervised learning technique on the self-collected unlabelled room images. The expert can extract the hidden room style information of goal and observation images and predict their relationship about whether they belong to the same room. In addition, two different fusion approaches are explored to efficiently guide the agent navigation with the room relation knowledge. Extensive experiments show that our REGNav surpasses prior state-of-the-art works on three popular benchmarks.

Jingwen Fu, Xiaoyi Zhang, Yuwang Wang et al.

The ubiquity of mobile phones makes mobile GUI understanding an important task. Most previous works in this domain require human-created metadata of screens (e.g. View Hierarchy) during inference, which unfortunately is often not available or reliable enough for GUI understanding. Inspired by the impressive success of Transformers in NLP tasks, targeting for purely vision-based GUI understanding, we extend the concepts of Words/Sentence to Pixel-Words/Screen-Sentence, and propose a mobile GUI understanding architecture: Pixel-Words to Screen-Sentence (PW2SS). In analogy to the individual Words, we define the Pixel-Words as atomic visual components (text and graphic components), which are visually consistent and semantically clear across screenshots of a large variety of design styles. The Pixel-Words extracted from a screenshot are aggregated into Screen-Sentence with a Screen Transformer proposed to model their relations. Since the Pixel-Words are defined as atomic visual components, the ambiguity between their visual appearance and semantics is dramatically reduced. We are able to make use of metadata available in training data to auto-generate high-quality annotations for Pixel-Words. A dataset, RICO-PW, of screenshots with Pixel-Words annotations is built based on the public RICO dataset, which will be released to help to address the lack of high-quality training data in this area. We train a detector to extract Pixel-Words from screenshots on this dataset and achieve metadata-free GUI understanding during inference. We conduct experiments and show that Pixel-Words can be well extracted on RICO-PW and well generalized to a new dataset, P2S-UI, collected by ourselves. The effectiveness of PW2SS is further verified in the GUI understanding tasks including relation prediction, clickability prediction, screen retrieval, and app type classification.

Jingwen Fu, Licheng Zong, Yinbing Li et al.

Object detection for robot guidance is a crucial mission for autonomous robots, which has provoked extensive attention for researchers. However, the changing view of robot movement and limited available data hinder the research in this area. To address these matters, we proposed a new vision system for robots, the model adaptation object detection system. Instead of using a single one to solve problems, We made use of different object detection neural networks to guide the robot in accordance with various situations, with the help of a meta neural network to allocate the object detection neural networks. Furthermore, taking advantage of transfer learning technology and depthwise separable convolutions, our model is easy to train and can address small dataset problems.

Jingwen Fu, Xiaoyan Zhu, Yingbin Li

Automatic defect recognition is one of the research hotspots in steel production, but most of the current methods mainly extract features manually and use machine learning classifiers to recognize defects, which cannot tackle the situation, where there are few data available to train and confine to a certain scene. Therefore, in this paper, a new approach is proposed which consists of part of pretrained VGG16 as a feature extractor and a new CNN neural network as a classifier to recognize the defect of steel strip surface based on the feature maps created by the feature extractor. Our method achieves an accuracy of 99.1% and 96.0% while the dataset contains 150 images each class and 10 images each class respectively, which is much better than previous methods.