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1. A High-Resolution Dataset for Instance Detection with Multi-View Instance Capture

   Shen, Qianqian; Zhao, Yunhan; Kwon, Nahyun; Kim, Jeeeun; Li, Yanan; Kong, Shu (May 2024, Neural Information Processing Systems Foundation, Inc. (NeurIPS))

   Instance detection (InsDet) is a long-lasting problem in robotics and computer vision, aiming to detect object instances (predefined by some visual examples) in a cluttered scene. Despite its practical significance, its advancement is overshadowed by Object Detection, which aims to detect objects belonging to some predefined classes. One major reason is that current InsDet datasets are too small in scale by today's standards. For example, the popular InsDet dataset GMU (published in 2016) has only 23 instances, far less than COCO (80 classes), a well-known object detection dataset published in 2014. We are motivated to introduce a new InsDet dataset and protocol. First, we define a realistic setup for InsDet: training data consists of multi-view instance captures, along with diverse scene images allowing synthesizing training images by pasting instance images on them with free box annotations. Second, we release a real-world database, which contains multi-view capture of 100 object instances, and high-resolution (6k\texttimes{} 8k) testing images. Third, we extensively study baseline methods for InsDet on our dataset, analyze their performance and suggest future work. Somewhat surprisingly, using the off-the-shelf class-agnostic segmentation model (Segment Anything Model, SAM) and the self-supervised feature representation DINOv2 performs the best, achieving >10 AP better than end-to-end trained InsDet models that repurpose object detectors (e.g., FasterRCNN and RetinaNet). 

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2. A high-resolution dataset for instance detection with multi-view object capture

   Shen, Qianqian; Zhao, Yunhan; Kwon, Nahyun; Kim, Jeeeun; Li, Yanan; Kong, Shu (December 2023, Advances in neural information processing systems)

   Instance detection (InsDet) is a long-lasting problem in robotics and computer vision, aiming to detect object instances (predefined by some visual examples) in a cluttered scene. Despite its practical significance, its advancement is overshadowed by Object Detection, which aims to detect objects belonging to some predefined classes. One major reason is that current InsDet datasets are too small in scale by today’s standards. For example, the popular InsDet dataset GMU (published in 2016) has only 23 instances, far less than COCO (80 classes), a well-known object detection dataset published in 2014. We are motivated to introduce a new InsDet dataset and protocol. First, we define a realistic setup for InsDet: training data consists of multi-view instance captures, along with diverse scene images allowing synthesizing training images by pasting instance images on them with free box annotations. Second, we release a real-world database, which contains multi-view capture of 100 object instances, and high-resolution (6k×8k) testing images. Third, we extensively study baseline methods for InsDet on our dataset, analyze their performance and suggest future work. Somewhat surprisingly, using the off-the-shelf class-agnostic segmentation model (Segment Anything Model, SAM) and the self-supervised feature representation DINOv2 performs the best, achieving >10 AP better than end-to-end trained InsDet models that repurpose object detectors (e.g., FasterRCNN and RetinaNet). 

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3. Towards Dynamic Crowd Mobility Learning and Meta Model Updates for A Smart Connected Campus

   Yang, X.; He, S.; Tabatabaie, M.; Wang, B. (October 2022, International Conference on Embedded Wireless Systems and Networks EWSN)

   In this paper, we propose MetaMobi, a novel spatio-temporal multi-dots connectivity-aware modeling and Meta model update approach for crowd Mobility learning. MetaMobi analyzes real-world Wi-Fi association data collected from our campus wireless infrastructure, with the goal towards enabling a smart connected campus. Specifically, MetaMobi aims at addressing the following two major challenges with existing crowd mobility sensing system designs: (a) how to handle the spatially, temporally, and contextually varying features in large-scale human crowd mobility distributions; and (b) how to adapt to the impacts of such crowd mobility patterns as well as the dynamic changes in crowd sensing infrastructures. To handle the first challenge, we design a novel multi-dots connectivity-aware learning approach, which jointly learns the crowd flow time series of multiple buildings with fusion of spatial graph connectivities and temporal attention mechanisms. Furthermore, to overcome the adaptivity issues due to changes in the crowd sensing infrastructures (e.g., installation of new ac- cess points), we further design a novel meta model update approach with Bernoulli dropout, which mitigates the over- fitting behaviors of the model given few-shot distributions of new crowd mobility datasets. Extensive experimental evaluations based on the real-world campus wireless dataset (including over 76 million Wi-Fi association and disassociation records) demonstrate the accuracy, effectiveness, and adaptivity of MetaMobi in forecasting the campus crowd flows, with 30% higher accuracy compared to the state-of-the-art approaches. 

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4. SimAug: Learning Robust Representations from Simulation for Trajectory Prediction

   https://doi.org/0.1007/978-3-030-58601-0_17  

   Liang, Junwei; Jiang, Lu; Hauptmann, Alexander (November 2020, Computer Vision – ECCV 2020)

   Vedaldi, Andrea; Bischof, Horst; Brox, Thomas; Frahm, Jan-Michael (Ed.)

   This paper focuses on the problem of predicting future trajectories of people in unseen scenarios and camera views. We propose a method to efficiently utilize multi-view 3D simulation data for training. Our approach finds the hardest camera view to mix up with adversarial data from the original camera view in training, thus enabling the model to learn robust representations that can generalize to unseen camera views. We refer to our method as SimAug. We show that SimAug achieves best results on three out-of-domain real-world benchmarks, as well as getting state-of-the-art in the Stanford Drone and the VIRAT/ActEV dataset with in-domain training data. We will release our models and code. 

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5. Generalized deep learning model for photovoltaic module segmentation from satellite and aerial imagery

   https://doi.org/10.1016/j.solener.2024.112539  

   García, Gustavo; Aparcedo, Alejandro; Nayak, Gaurav Kumar; Ahmed, Tanvir; Shah, Mubarak; Li, Mengjie (May 2024, Solar Energy)

   As solar photovoltaic (PV) has emerged as a dominant player in the energy market, there has been an exponential surge in solar deployment and investment within this sector. With the rapid growth of solar energy adoption, accurate and efficient detection of PV panels has become crucial for effective solar energy mapping and planning. This paper presents the application of the Mask2Former model for segmenting PV panels from a diverse, multi-resolution dataset of satellite and aerial imagery. Our primary objective is to harness Mask2Former’s deep learning capabilities to achieve precise segmentation of PV panels in real-world scenarios. We fine-tune the pre-existing Mask2Former model on a carefully curated multi-resolution dataset and a crowdsourced dataset of satellite and aerial images, showcasing its superiority over other deep learning models like U-Net and DeepLabv3+. Most notably, Mask2Former establishes a new state-of-the-art in semantic segmentation by achieving over 95% IoU scores. Our research contributes significantly to the advancement solar energy mapping and sets a benchmark for future studies in this field. 

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