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Found 16 papers, 7 papers with code
CueCAn: Cue Driven Contextual Attention For Identifying Missing Traffic Signs on Unconstrained Roads
MTSVD is challenging compared to the previous works in two aspects i) The traffic signs are generally not present in the vicinity of their cues, ii) The traffic signs cues are diverse and unique.
IDD-3D: Indian Driving Dataset for 3D Unstructured Road Scenes
Autonomous driving and assistance systems rely on annotated data from traffic and road scenarios to model and learn the various object relations in complex real-world scenarios.
TRoVE: Transforming Road Scene Datasets into Photorealistic Virtual Environments
We show that using annotations and visual cues from existing datasets, we can facilitate automated multi-modal data generation, mimicking real scene properties with high-fidelity, along with mechanisms to diversify samples in a physically meaningful way.
Detecting, Tracking and Counting Motorcycle Rider Traffic Violations on Unconstrained Roads
In many Asian countries with unconstrained road traffic conditions, driving violations such as not wearing helmets and triple-riding are a significant source of fatalities involving motorcycles.
Automatic Quantification and Visualization of Street Trees
We obtain TCDCA of 96. 77% on the test videos, with a remarkable improvement of 22. 58% over baseline, and demonstrate that our counting module's performance is close to human level.
To miss-attend is to misalign! Residual Self-Attentive Feature Alignment for Adapting Object Detectors
Qualitative results demonstrate the ability of ILLUME to attend important object instances required for alignment.
Attention Guided Cosine Margin For Overcoming Class-Imbalance in Few-Shot Road Object Detection
To overcome these pitfalls in metric learning based FSOD techniques, we introduce Attention Guided Cosine Margin (AGCM) that facilitates the creation of tighter and well separated class-specific feature clusters in the classification head of the object detector.
Meta Guided Metric Learner for Overcoming Class Confusion in Few-Shot Road Object Detection
Our method outperforms State-of-the-Art (SoTA) approaches in FSOD on the India Driving Dataset (IDD) by upto 11 mAP points while suffering from the least class confusion of 20% given only 10 examples of each novel road object.
Multi-Domain Incremental Learning for Semantic Segmentation
Recent efforts in multi-domain learning for semantic segmentation attempt to learn multiple geographical datasets in a universal, joint model.
Few-Shot Batch Incremental Road Object Detection via Detector Fusion
Incremental few-shot learning has emerged as a new and challenging area in deep learning, whose objective is to train deep learning models using very few samples of new class data, and none of the old class data.
Few-Shot Learning for Road Object Detection
Few-shot learning is a problem of high interest in the evolution of deep learning.
Measures of Complexity for Large Scale Image Datasets
Large scale image datasets are a growing trend in the field of machine learning.
Enhancing Object Detection in Adverse Conditions using Thermal Imaging
Autonomous driving relies on deriving understanding of objects and scenes through images.
IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments
It also reflects label distributions of road scenes significantly different from existing datasets, with most classes displaying greater within-class diversity.
One-Click Annotation with Guided Hierarchical Object Detection
Our approach also proposes a new method of using object detectors making it suitable for data annotation task.
Learning End-to-end Autonomous Driving using Guided Auxiliary Supervision
We run our experiments to validate the MT-LfD framework in CARLA, an open-source urban driving simulator.
