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Found 20 papers, 8 papers with code
Hardware Phi-1.5B: A Large Language Model Encodes Hardware Domain Specific Knowledge
The creation of this first pretrained, hardware domain specific large language model marks a significant advancement, offering improved performance in hardware design and verification tasks and illustrating a promising path forward for AI applications in the semiconductor sector.
VaLID: Variable-Length Input Diffusion for Novel View Synthesis
This is because the source-view images and corresponding poses are processed separately and injected into the model at different stages.
Self-supervised OCT Image Denoising with Slice-to-Slice Registration and Reconstruction
Strong speckle noise is inherent to optical coherence tomography (OCT) imaging and represents a significant obstacle for accurate quantitative analysis of retinal structures which is key for advances in clinical diagnosis and monitoring of disease.
TFNet: Exploiting Temporal Cues for Fast and Accurate LiDAR Semantic Segmentation
In this paper, we present TFNet, a range-image-based LiDAR semantic segmentation method that utilizes temporal information to address this issue.
Ranked #1 on
Semantic Segmentation
on SemanticPOSS
Semantic RGB-D Image Synthesis
In this paper, we therefore propose a generator for multi-modal data that separates modal-independent information of the semantic layout from the modal-dependent information that is needed to generate an RGB and a depth image, respectively.
Microscopy Image Segmentation via Point and Shape Regularized Data Synthesis
Current deep learning-based approaches for the segmentation of microscopy images heavily rely on large amount of training data with dense annotation, which is highly costly and laborious in practice.
Crop mapping in the small sample/no sample case: an approach using a two-level cascade classifier and integrating domain knowledge
To solve this problem, a crop mapping method in the small sample/no sample case that integrating domain knowledge and using a cascaded classification framework that combine a weak classifier learned from samples with strong features and a strong classifier trained by samples with weak feature was proposed.
Dual Pyramid Generative Adversarial Networks for Semantic Image Synthesis
The goal of semantic image synthesis is to generate photo-realistic images from semantic label maps.
Toward An Optimal Selection of Dialogue Strategies: A Target-Driven Approach for Intelligent Outbound Robots
With the growth of the economy and society, enterprises, especially in the FinTech industry, have increasing demands of outbound calls for customers such as debt collection, marketing, anti-fraud calls, and so on.
Improved Counting and Localization from Density Maps for Object Detection in 2D and 3D Microscopy Imaging
Our results show improved performance in counting and localization of objects in 2D and 3D microscopy data.
Point-supervised Segmentation of Microscopy Images and Volumes via Objectness Regularization
Annotation is a major hurdle in the semantic segmentation of microscopy images and volumes due to its prerequisite expertise and effort.
Spatial-Temporal Consistency Network for Low-Latency Trajectory Forecasting
Trajectory forecasting is a crucial step for autonomous vehicles and mobile robots in order to navigate and interact safely.
Pose Refinement Graph Convolutional Network for Skeleton-based Action Recognition
To this end, the network first refines the poses before they are further processed to recognize the action.
Multi-scale Interaction for Real-time LiDAR Data Segmentation on an Embedded Platform
Real-time semantic segmentation of LiDAR data is crucial for autonomously driving vehicles, which are usually equipped with an embedded platform and have limited computational resources.
Ranked #2 on
Real-Time 3D Semantic Segmentation
on SemanticKITTI
Rethinking 3D LiDAR Point Cloud Segmentation
Many point-based semantic segmentation methods have been designed for indoor scenarios, but they struggle if they are applied to point clouds that are captured by a LiDAR sensor in an outdoor environment.
MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation
Despite the capabilities of these approaches in capturing temporal dependencies, their predictions suffer from over-segmentation errors.
Ranked #5 on
Action Segmentation
on Assembly101
MiniSeg: An Extremely Minimum Network for Efficient COVID-19 Segmentation
On the other hand, fast training/testing and low computational cost are also necessary for quick deployment and development of COVID-19 screening systems, but traditional deep learning methods are usually computationally intensive.
Exploring Frame Segmentation Networks for Temporal Action Localization
The proposed FSN can make dense predictions at frame-level for a video clip using both spatial and temporal context information.
Joint Association Graph Screening and Decomposition for Large-scale Linear Dynamical Systems
In particular, our method can pre-determine and remove unnecessary edges based on the joint graphical structure, referred to as JAG screening, and can decompose a large network into smaller subnetworks in a robust manner, referred to as JAG decomposition.
