Direction-aware Spatial Context Features for Shadow Detection

Shadow detection is a fundamental and challenging task, since it requires an understanding of global image semantics and there are various backgrounds around shadows. This paper presents a novel network for shadow detection by analyzing image context in a direction-aware manner. To achieve this, we first formulate the direction-aware attention mechanism in a spatial recurrent neural network (RNN) by introducing attention weights when aggregating spatial context features in the RNN. By learning these weights through training, we can recover direction-aware spatial context (DSC) for detecting shadows. This design is developed into the DSC module and embedded in a CNN to learn DSC features at different levels. Moreover, a weighted cross entropy loss is designed to make the training more effective. We employ two common shadow detection benchmark datasets and perform various experiments to evaluate our network. Experimental results show that our network outperforms state-of-the-art methods and achieves 97% accuracy and 38% reduction on balance error rate.Comment: Accepted for oral presentation in CVPR 2018. The journal version of this paper is arXiv:1805.0463

Asymmetric synthesis of gonytolide A: strategic use of an aryl halide blocking group for oxidative coupling

The first synthesis of the chromanone lactone dimer gonytolide A has been achieved employing vanadium(V)-mediated oxidative coupling of the monomer gonytolide C. An o-bromine blocking group strategy was employed to favor para- para coupling and to enable kinetic resolution of (±)-gonytolide C. Asymmetric conjugate reduction enabled practical kinetic resolution of a chiral, racemic precursor and the asymmetric synthesis of (+)-gonytolide A and its atropisomer.We thank the National Institutes of Health (R35 GM-118173) for research support. Work at the BU-CMD is supported by NIH R24 Grant GM-111625. We thank Prof. Scott Miller and Dr. Anthony Metrano (Yale University) for helpful discussions and preliminary experiments. We thank the Uehara Memorial Foundation for a postdoctoral fellowship to T.I., the American Cancer Society for a postdoctoral fellowship to K.D.R. (PF-16-235-01-CDD), Dr. Jeffrey Bacon (Boston University) for X-ray crystal structure analyses, and Prof. Haruhisa Kikuchi (Tohoku University) for providing a natural sample of gonytolide A. NMR (CHE-0619339) and MS (CHE-0443618) facilities at Boston University are supported by the NSF. (R35 GM-118173 - National Institutes of Health; GM-111625 - NIH; Uehara Memorial Foundation; PF-16-235-01-CDD - American Cancer Society; CHE-0619339 - NSF; CHE-0443618 - NSF

SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection

Vision-based vehicle detection approaches achieve incredible success in recent years with the development of deep convolutional neural network (CNN). However, existing CNN based algorithms suffer from the problem that the convolutional features are scale-sensitive in object detection task but it is common that traffic images and videos contain vehicles with a large variance of scales. In this paper, we delve into the source of scale sensitivity, and reveal two key issues: 1) existing RoI pooling destroys the structure of small scale objects, 2) the large intra-class distance for a large variance of scales exceeds the representation capability of a single network. Based on these findings, we present a scale-insensitive convolutional neural network (SINet) for fast detecting vehicles with a large variance of scales. First, we present a context-aware RoI pooling to maintain the contextual information and original structure of small scale objects. Second, we present a multi-branch decision network to minimize the intra-class distance of features. These lightweight techniques bring zero extra time complexity but prominent detection accuracy improvement. The proposed techniques can be equipped with any deep network architectures and keep them trained end-to-end. Our SINet achieves state-of-the-art performance in terms of accuracy and speed (up to 37 FPS) on the KITTI benchmark and a new highway dataset, which contains a large variance of scales and extremely small objects.Comment: Accepted by IEEE Transactions on Intelligent Transportation Systems (T-ITS

Joint Waveform and Clustering Design for Coordinated Multi-point DFRC Systems

To improve both sensing and communication performances, this paper proposes a coordinated multi-point (CoMP) transmission design for a dual-functional radar-communication (DFRC) system. In the proposed CoMP-DFRC system, the central processor (CP) coordinates multiple base stations (BSs) to transmit both the communication signal and the dedicated probing signal. The communication performance and the sensing performance are both evaluated by the signal-to-interference-plus-noise ratio (SINR). Given the limited backhaul capacity, we study the waveform and clustering design from both the radar-centric perspective and the communication-centric perspective. Dinkelbach’s transform is adopted to handle the single-ratio fractional objective for the radar-centric problem. For the communication-centric problem, we adopt quadratic transform to convexitify the multi-ratio fractional objective. Then, the rank-one constraint of communication beamforming vector is relaxed by semidefinite relaxation (SDR), and the tightness of SDR is further proved to guarantee the optimal waveform design with fixed clustering. For dynamic clustering, equivalent continuous functions are used to represent the non-continuous clustering variables. Successive convex approximation (SCA) is further utilized to convexitify the equivalent functions. Simulation results are provided to verify the effectiveness of all proposed designs

EasyHeC: Accurate and Automatic Hand-eye Calibration via Differentiable Rendering and Space Exploration

Hand-eye calibration is a critical task in robotics, as it directly affects the efficacy of critical operations such as manipulation and grasping. Traditional methods for achieving this objective necessitate the careful design of joint poses and the use of specialized calibration markers, while most recent learning-based approaches using solely pose regression are limited in their abilities to diagnose inaccuracies. In this work, we introduce a new approach to hand-eye calibration called EasyHeC, which is markerless, white-box, and offers comprehensive coverage of positioning accuracy across the entire robot configuration space. We introduce two key technologies: differentiable rendering-based camera pose optimization and consistency-based joint space exploration, which enables accurate end-to-end optimization of the calibration process and eliminates the need for the laborious manual design of robot joint poses. Our evaluation demonstrates superior performance in synthetic and real-world datasets, enhancing downstream manipulation tasks by providing precise camera poses for locating and interacting with objects. The code is available at the project page: https://ootts.github.io/easyhec.Comment: Project page: https://ootts.github.io/easyhe