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Found 8 papers, 0 papers with code
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.
Improving self-supervised pretraining models for epileptic seizure detection from EEG data
Traditional supervised learning algorithms are often limited by the amount of labeled data, especially in the medical domain, where labeling is costly in terms of human processing and specialized experts needed to label them.
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.
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.
