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Found 12 papers, 5 papers with code
Scheduling Drone and Mobile Charger via Hybrid-Action Deep Reinforcement Learning
To address this issue, we present a hybrid-action deep reinforcement learning framework, called HaDMC, which uses a standard policy learning algorithm to generate latent continuous actions.
An Efficient MLP-based Point-guided Segmentation Network for Ore Images with Ambiguous Boundary
The precise segmentation of ore images is critical to the successful execution of the beneficiation process.
Efficient Segmentation with Texture in Ore Images Based on Box-supervised Approach
At the same time, the ore distribution is stacked, and it is difficult to identify the complete features.
Spatial-information Guided Adaptive Context-aware Network for Efficient RGB-D Semantic Segmentation
Efficient RGB-D semantic segmentation has received considerable attention in mobile robots, which plays a vital role in analyzing and recognizing environmental information.
Ranked #57 on
Semantic Segmentation
on NYU Depth v2
Efficient Visual Fault Detection for Freight Train Braking System via Heterogeneous Self Distillation in the Wild
Efficient visual fault detection of freight trains is a critical part of ensuring the safe operation of railways under the restricted hardware environment.
Faster OreFSDet : A Lightweight and Effective Few-shot Object Detector for Ore Images
For the ore particle size detection, obtaining a sizable amount of high-quality ore labeled data is time-consuming and expensive.
Visual Fault Detection of Multi-scale Key Components in Freight Trains
Fault detection for key components in the braking system of freight trains is critical for ensuring railway transportation safety.
An adjoint-free algorithm for conditional nonlinear optimal perturbations (CNOPs) via sampling
In this paper, we propose a sampling algorithm based on state-of-the-art statistical machine learning techniques to obtain conditional nonlinear optimal perturbations (CNOPs), which is different from traditional (deterministic) optimization methods. 1 Specifically, the traditional approach is unavailable in practice, which requires numerically computing the gradient (first-order information) such that the computation cost is expensive, since it needs a large number of times to run numerical models.
A Lightweight NMS-free Framework for Real-time Visual Fault Detection System of Freight Trains
In this paper, we propose a lightweight NMS-free framework to achieve real-time detection and high accuracy simultaneously.
Attention-based Dual Supervised Decoder for RGBD Semantic Segmentation
Encoder-decoder models have been widely used in RGBD semantic segmentation, and most of them are designed via a two-stream network.
Ranked #13 on
Semantic Segmentation
on SUN-RGBD
(using extra training data)
Adaptive Fusion Affinity Graph with Noise-free Online Low-rank Representation for Natural Image Segmentation
To reduce time complexity while improving performance, a sparse representation of global nodes based on noise-free online low-rank representation is used to obtain a global graph at each scale.
Temporal Spatial-Adaptive Interpolation with Deformable Refinement for Electron Microscopic Images
The TSA interpolation module aggregates temporal contexts and then adaptively samples the spatial-related features with the proposed residual spatial adaptive block.
