Jeremy S. Smith

Runwei Guan, Shanliang Yao, Xiaohui Zhu et al.

Panoptic Driving Perception (PDP) is critical for the autonomous navigation of Unmanned Surface Vehicles (USVs). A PDP model typically integrates multiple tasks, necessitating the simultaneous and robust execution of various perception tasks to facilitate downstream path planning. The fusion of visual and radar sensors is currently acknowledged as a robust and cost-effective approach. However, most existing research has primarily focused on fusing visual and radar features dedicated to object detection or utilizing a shared feature space for multiple tasks, neglecting the individual representation differences between various tasks. To address this gap, we propose a pair of Asymmetric Fair Fusion (AFF) modules with favorable explainability designed to efficiently interact with independent features from both visual and radar modalities, tailored to the specific requirements of object detection and semantic segmentation tasks. The AFF modules treat image and radar maps as irregular point sets and transform these features into a crossed-shared feature space for multitasking, ensuring equitable treatment of vision and radar point cloud features. Leveraging AFF modules, we propose a novel and efficient PDP model, ASY-VRNet, which processes image and radar features based on irregular super-pixel point sets. Additionally, we propose an effective multitask learning method specifically designed for PDP models. Compared to other lightweight models, ASY-VRNet achieves state-of-the-art performance in object detection, semantic segmentation, and drivable-area segmentation on the WaterScenes benchmark. Our project is publicly available at https://github.com/GuanRunwei/ASY-VRNet.

Runwei Guan, Jianan Liu, Liye Jia et al.

Recently, visual grounding and multi-sensors setting have been incorporated into perception system for terrestrial autonomous driving systems and Unmanned Surface Vehicles (USVs), yet the high complexity of modern learning-based visual grounding model using multi-sensors prevents such model to be deployed on USVs in the real-life. To this end, we design a low-power multi-task model named NanoMVG for waterway embodied perception, guiding both camera and 4D millimeter-wave radar to locate specific object(s) through natural language. NanoMVG can perform both box-level and mask-level visual grounding tasks simultaneously. Compared to other visual grounding models, NanoMVG achieves highly competitive performance on the WaterVG dataset, particularly in harsh environments and boasts ultra-low power consumption for long endurance.

Sihao Li, Kyeong Soo Kim, Zhe Tang et al.

In this paper, we present a new solution to the problem of large-scale multi-building and multi-floor indoor localization based on linked neural networks, where each neural network is dedicated to a sub-problem and trained under a hierarchical stage-wise training framework. When the measured data from sensors have a hierarchical representation as in multi-building and multi-floor indoor localization, it is important to exploit the hierarchical nature in data processing to provide a scalable solution. In this regard, the hierarchical stage-wise training framework extends the original stage-wise training framework to the case of multiple linked networks by training a lower-hierarchy network based on the prior knowledge gained from the training of higher-hierarchy networks. The experimental results with the publicly-available UJIIndoorLoc multi-building and multi-floor Wi-Fi RSSI fingerprint database demonstrate that the linked neural networks trained under the proposed hierarchical stage-wise training framework can achieve a three-dimensional localization error of 8.19 m, which, to the best of the authors' knowledge, is the most accurate result ever obtained for neural network-based models trained and evaluated with the full datasets of the UJIIndoorLoc database, and that, when applied to a model based on hierarchical convolutional neural networks, the proposed training framework can also significantly reduce the three-dimensional localization error from 11.78 m to 8.71 m.

Runwei Guan, Haocheng Zhao, Shanliang Yao et al.

Urban water-surface robust perception serves as the foundation for intelligent monitoring of aquatic environments and the autonomous navigation and operation of unmanned vessels, especially in the context of waterway safety. It is worth noting that current multi-sensor fusion and multi-task learning models consume substantial power and heavily rely on high-power GPUs for inference. This contributes to increased carbon emissions, a concern that runs counter to the prevailing emphasis on environmental preservation and the pursuit of sustainable, low-carbon urban environments. In light of these concerns, this paper concentrates on low-power, lightweight, multi-task panoptic perception through the fusion of visual and 4D radar data, which is seen as a promising low-cost perception method. We propose a framework named Achelous++ that facilitates the development and comprehensive evaluation of multi-task water-surface panoptic perception models. Achelous++ can simultaneously execute five perception tasks with high speed and low power consumption, including object detection, object semantic segmentation, drivable-area segmentation, waterline segmentation, and radar point cloud semantic segmentation. Furthermore, to meet the demand for developers to customize models for real-time inference on low-performance devices, a novel multi-modal pruning strategy known as Heterogeneous-Aware SynFlow (HA-SynFlow) is proposed. Besides, Achelous++ also supports random pruning at initialization with different layer-wise sparsity, such as Uniform and Erdos-Renyi-Kernel (ERK). Overall, our Achelous++ framework achieves state-of-the-art performance on the WaterScenes benchmark, excelling in both accuracy and power efficiency compared to other single-task and multi-task models. We release and maintain the code at https://github.com/GuanRunwei/Achelous.

Zhe Tang, Sihao Li, Zichen Huang et al.

Internet of Things (IoT) devices are deployed in the filed, there is an enormous amount of untapped potential in local computing on those IoT devices. Harnessing this potential for indoor localization, therefore, becomes an exciting research area. Conventionally, the training and deployment of indoor localization models are based on centralized servers with substantial computational resources. This centralized approach faces several challenges, including the database's inability to accommodate the dynamic and unpredictable nature of the indoor electromagnetic environment, the model retraining costs, and the susceptibility of centralized servers to security breaches. To mitigate these challenges we aim to amalgamate the offline and online phases of traditional indoor localization methods using a real-time-trainable and decentralized IoT indoor localization model based on Sparse Gaussian Process with Reduced-dimensional Inputs (SGP-RI), where the number and dimension of the input data are reduced through reference point and wireless access point filtering, respectively. The experimental results based on a multi-building and multi-floor static database as well as a single-building and single-floor dynamic database, demonstrate that the proposed SGP-RI model with less than half the training samples as inducing inputs can produce comparable localization performance to the standard Gaussian Process model with the whole training samples. The SGP-RI model enables the decentralization of indoor localization, facilitating its deployment to resource-constrained IoT devices, and thereby could provide enhanced security and privacy, reduced costs, and network dependency. Also, the model's capability of real-time training makes it possible to quickly adapt to the time-varying indoor electromagnetic environment.

Runwei Guan, Rongsheng Hu, Zhuhao Zhou et al.

In reality, images often exhibit multiple degradations, such as rain and fog at night (triple degradations). However, in many cases, individuals may not want to remove all degradations, for instance, a blurry lens revealing a beautiful snowy landscape (double degradations). In such scenarios, people may only desire to deblur. These situations and requirements shed light on a new challenge in image restoration, where a model must perceive and remove specific degradation types specified by human commands in images with multiple degradations. We term this task Referring Flexible Image Restoration (RFIR). To address this, we first construct a large-scale synthetic dataset called RFIR, comprising 153,423 samples with the degraded image, text prompt for specific degradation removal and restored image. RFIR consists of five basic degradation types: blur, rain, haze, low light and snow while six main sub-categories are included for varying degrees of degradation removal. To tackle the challenge, we propose a novel transformer-based multi-task model named TransRFIR, which simultaneously perceives degradation types in the degraded image and removes specific degradation upon text prompt. TransRFIR is based on two devised attention modules, Multi-Head Agent Self-Attention (MHASA) and Multi-Head Agent Cross Attention (MHACA), where MHASA and MHACA introduce the agent token and reach the linear complexity, achieving lower computation cost than vanilla self-attention and cross-attention and obtaining competitive performances. Our TransRFIR achieves state-of-the-art performances compared with other counterparts and is proven as an effective architecture for image restoration. We release our project at https://github.com/GuanRunwei/FIR-CP.

Liye Jia, Runwei Guan, Haocheng Zhao et al.

3D object detection is crucial for Autonomous Driving (AD) and Advanced Driver Assistance Systems (ADAS). However, most 3D detectors prioritize detection accuracy, often overlooking network inference speed in practical applications. In this paper, we propose RadarNeXt, a real-time and reliable 3D object detector based on the 4D mmWave radar point clouds. It leverages the re-parameterizable neural networks to catch multi-scale features, reduce memory cost and accelerate the inference. Moreover, to highlight the irregular foreground features of radar point clouds and suppress background clutter, we propose a Multi-path Deformable Foreground Enhancement Network (MDFEN), ensuring detection accuracy while minimizing the sacrifice of speed and excessive number of parameters. Experimental results on View-of-Delft and TJ4DRadSet datasets validate the exceptional performance and efficiency of RadarNeXt, achieving 50.48 and 32.30 mAPs with the variant using our proposed MDFEN. Notably, our RadarNeXt variants achieve inference speeds of over 67.10 FPS on the RTX A4000 GPU and 28.40 FPS on the Jetson AGX Orin. This research demonstrates that RadarNeXt brings a novel and effective paradigm for 3D perception based on 4D mmWave radar.

Runwei Guan, Liye Jia, Fengyufan Yang et al.

The perception of waterways based on human intent is significant for autonomous navigation and operations of Unmanned Surface Vehicles (USVs) in water environments. Inspired by visual grounding, we introduce WaterVG, the first visual grounding dataset designed for USV-based waterway perception based on human prompts. WaterVG encompasses prompts describing multiple targets, with annotations at the instance level including bounding boxes and masks. Notably, WaterVG includes 11,568 samples with 34,987 referred targets, whose prompts integrates both visual and radar characteristics. The pattern of text-guided two sensors equips a finer granularity of text prompts with visual and radar features of referred targets. Moreover, we propose a low-power visual grounding model, Potamoi, which is a multi-task model with a well-designed Phased Heterogeneous Modality Fusion (PHMF) mode, including Adaptive Radar Weighting (ARW) and Multi-Head Slim Cross Attention (MHSCA). Exactly, ARW extracts required radar features to fuse with vision for prompt alignment. MHSCA is an efficient fusion module with a remarkably small parameter count and FLOPs, elegantly fusing scenario context captured by two sensors with linguistic features, which performs expressively on visual grounding tasks. Comprehensive experiments and evaluations have been conducted on WaterVG, where our Potamoi archives state-of-the-art performances compared with counterparts.

Shiyang Yan, Yuan Xie, Fangyu Wu et al.

Automatically generating the descriptions of an image, i.e., image captioning, is an important and fundamental topic in artificial intelligence, which bridges the gap between computer vision and natural language processing. Based on the successful deep learning models, especially the CNN model and Long Short-Term Memories (LSTMs) with attention mechanism, we propose a hierarchical attention model by utilizing both of the global CNN features and the local object features for more effective feature representation and reasoning in image captioning. The generative adversarial network (GAN), together with a reinforcement learning (RL) algorithm, is applied to solve the exposure bias problem in RNN-based supervised training for language problems. In addition, through the automatic measurement of the consistency between the generated caption and the image content by the discriminator in the GAN framework and RL optimization, we make the finally generated sentences more accurate and natural. Comprehensive experiments show the improved performance of the hierarchical attention mechanism and the effectiveness of our RL-based optimization method. Our model achieves state-of-the-art results on several important metrics in the MSCOCO dataset, using only greedy inference.

Shiyang Yan, Jeremy S. Smith, Wenjin Lu et al.

Recurrent Neural Networks (RNNs) have been widely used in natural language processing and computer vision. Among them, the Hierarchical Multi-scale RNN (HM-RNN), a kind of multi-scale hierarchical RNN proposed recently, can learn the hierarchical temporal structure from data automatically. In this paper, we extend the work to solve the computer vision task of action recognition. However, in sequence-to-sequence models like RNN, it is normally very hard to discover the relationships between inputs and outputs given static inputs. As a solution, attention mechanism could be applied to extract the relevant information from input thus facilitating the modeling of input-output relationships. Based on these considerations, we propose a novel attention network, namely Hierarchical Multi-scale Attention Network (HM-AN), by combining the HM-RNN and the attention mechanism and apply it to action recognition. A newly proposed gradient estimation method for stochastic neurons, namely Gumbel-softmax, is exploited to implement the temporal boundary detectors and the stochastic hard attention mechanism. To amealiate the negative effect of sensitive temperature of the Gumbel-softmax, an adaptive temperature training method is applied to better the system performance. The experimental results demonstrate the improved effect of HM-AN over LSTM with attention on the vision task. Through visualization of what have been learnt by the networks, it can be observed that both the attention regions of images and the hierarchical temporal structure can be captured by HM-AN.

Fang-Yu Wu, Shi-Yang Yan, Jeremy S. Smith et al.

Traffic scene recognition is an important and challenging issue in Intelligent Transportation Systems (ITS). Recently, Convolutional Neural Network (CNN) models have achieved great success in many applications, including scene classification. The remarkable representational learning capability of CNN remains to be further explored for solving real-world problems. Vector of Locally Aggregated Descriptors (VLAD) encoding has also proved to be a powerful method in catching global contextual information. In this paper, we attempted to solve the traffic scene recognition problem by combining the features representational capabilities of CNN with the VLAD encoding scheme. More specifically, the CNN features of image patches generated by a region proposal algorithm are encoded by applying VLAD, which subsequently represent an image in a compact representation. To catch the spatial information, spatial pyramids are exploited to encode CNN features. We experimented with a dataset of 10 categories of traffic scenes, with satisfactory categorization performances.

Shiyang Yan, Jeremy S. Smith, Wenjin Lu et al.

Recently, the soft attention mechanism, which was originally proposed in language processing, has been applied in computer vision tasks like image captioning. This paper presents improvements to the soft attention model by combining a convolutional LSTM with a hierarchical system architecture to recognize action categories in videos. We call this model the Convolutional Hierarchical Attention Model (CHAM). The model applies a convolutional operation inside the LSTM cell and an attention map generation process to recognize actions. The hierarchical architecture of this model is able to explicitly reason on multi-granularities of action categories. The proposed architecture achieved improved results on three publicly available datasets: the UCF sports dataset, the Olympic sports dataset and the HMDB51 dataset.