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Found 11 papers, 3 papers with code
BatSort: Enhanced Battery Classification with Transfer Learning for Battery Sorting and Recycling
Battery recycling is a critical process for minimizing environmental harm and resource waste for used batteries.
Few-Shot Point Cloud Semantic Segmentation via Contrastive Self-Supervision and Multi-Resolution Attention
Moreover, a case study on practical CAM/CAD segmentation is presented to demonstrate the effectiveness of our approach for real-world applications.
Incremental Few-Shot Learning via Implanting and Compressing
Specifically, in the \textbf{Implanting} step, we propose to mimic the data distribution of novel classes with the assistance of data-abundant base set, so that a model could learn semantically-rich features that are beneficial for discriminating between the base and other unseen classes.
Towards Generalized and Incremental Few-Shot Object Detection
We conduct experiments on both Pascal VOC and MS-COCO, which demonstrate that our method can effectively solve the problem of incremental few-shot detection and significantly improve the detection accuracy on both base and novel classes.
Few-Shot Object Detection via Classification Refinement and Distractor Retreatment
The failure modes of FSOD are investigated that the performance degradation is mainly due to the classification incapability (false positives), which motivates us to address it from a novel aspect of hard example mining.
On Solar Photovoltaic Parameter Estimation: Global Optimality Analysis and a Simple Efficient Differential Evolution Method
More attractively, the simple DE algorithm takes only a fraction of the runtime of state-of-the-art metaheuristic methods and is particularly preferable in time-sensitive applications.
e-TLD: Event-based Framework for Dynamic Object Tracking
On the other hand, when the object re-enters the field-of-view of the camera, a data-driven, global sliding window detector locates the object for subsequent tracking.
Incremental Few-Shot Object Detection for Robotics
Incremental few-shot learning is highly expected for practical robotics applications.
A deep Convolutional Neural Network for topology optimization with strong generalization ability
The performance of the proposed method was evaluated by comparing its efficiency and accuracy with SIMP on a series of typical optimization problems.
DART: Distribution Aware Retinal Transform for Event-based Cameras
We introduce a generic visual descriptor, termed as distribution aware retinal transform (DART), that encodes the structural context using log-polar grids for event cameras.
Real-Time Optical flow-based Video Stabilization for Unmanned Aerial Vehicles
This paper describes the development of a novel algorithm to tackle the problem of real-time video stabilization for unmanned aerial vehicles (UAVs).
