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Found 10 papers, 2 papers with code
Fisheye Camera and Ultrasonic Sensor Fusion For Near-Field Obstacle Perception in Bird's-Eye-View
Therefore, we present, to our knowledge, the first end-to-end multimodal fusion model tailored for efficient obstacle perception in a bird's-eye-view (BEV) perspective, utilizing fisheye cameras and ultrasonic sensors.
Revisiting Modality Imbalance In Multimodal Pedestrian Detection
Multimodal learning, particularly for pedestrian detection, has recently received emphasis due to its capability to function equally well in several critical autonomous driving scenarios such as low-light, night-time, and adverse weather conditions.
Deep Multi-Task Networks For Occluded Pedestrian Pose Estimation
The proposed framework has improved state-of-the-art performances of pose estimation, pedestrian detection, and instance segmentation.
Ranked #18 on
Pose Estimation
on COCO test-dev
UnShadowNet: Illumination Critic Guided Contrastive Learning For Shadow Removal
Shadows are frequently encountered natural phenomena that significantly hinder the performance of computer vision perception systems in practical settings, e. g., autonomous driving.
Spatio-Contextual Deep Network Based Multimodal Pedestrian Detection For Autonomous Driving
Fusion of these two encoded features takes place inside a multimodal feature embedding module (MuFEm) consisting of several groups of a pair of Graph Attention Network and a feature fusion unit.
Approximate Nearest Neighbour Search on Privacy-aware Encoding of User Locations to Identify Susceptible Infections in Simulated Epidemics
In this paper, we investigate how effectively and efficiently can such a list of susceptible people be found given a list of infected persons and their locations.
An End-to-End Framework for Unsupervised Pose Estimation of Occluded Pedestrians
To tackle this problem for training the network, we make use of a pose estimation dataset, MS-COCO, and employ unsupervised adversarial instance-level domain adaptation for estimating the entire pose of occluded pedestrians.
Scale-Invariant Multi-Oriented Text Detection in Wild Scene Images
Automatic detection of scene texts in the wild is a challenging problem, particularly due to the difficulties in handling (i) occlusions of varying percentages, (ii) widely different scales and orientations, (iii) severe degradations in the image quality etc.
Seek and You Will Find: A New Optimized Framework for Efficient Detection of Pedestrian
Studies of object detection and localization, particularly pedestrian detection have received considerable attention in recent times due to its several prospective applications such as surveillance, driving assistance, autonomous cars, etc.
Document Image Classification with Intra-Domain Transfer Learning and Stacked Generalization of Deep Convolutional Neural Networks
In this work, a region-based Deep Convolutional Neural Network framework is proposed for document structure learning.
Ranked #23 on
Document Image Classification
on RVL-CDIP
