Search Results for author:
Found 11 papers, 1 papers with code
GOV-NeSF: Generalizable Open-Vocabulary Neural Semantic Fields
Recent advancements in vision-language foundation models have significantly enhanced open-vocabulary 3D scene understanding.
Open Vocabulary Semantic Segmentation
Scene Understanding
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NeuSG: Neural Implicit Surface Reconstruction with 3D Gaussian Splatting Guidance
In this work, we propose a neural implicit surface reconstruction pipeline with guidance from 3D Gaussian Splatting to recover highly detailed surfaces.
Improving Autonomous Vehicle Mapping and Navigation in Work Zones Using Crowdsourcing Vehicle Trajectories
Navigating CAVs in such areas heavily relies on how the vehicle defines drivable areas based on perception information.
The Dark Side of Dynamic Routing Neural Networks: Towards Efficiency Backdoor Injection
Recent advancements in deploying deep neural networks (DNNs) on resource-constrained devices have generated interest in input-adaptive dynamic neural networks (DyNNs).
Incremental Neural Implicit Representation with Uncertainty-Filtered Knowledge Distillation
In view of this, we explore the task of incremental learning for NIRs in this work.
NAS-Bench-Zero: A Large Scale Dataset for Understanding Zero-Shot Neural Architecture Search
Based on these new discoveries, we propose i) a novel hybrid zero-shot proxy which outperforms existing ones by a large margin and is transferable among popular search spaces; ii) a new index for better measuring the true performance of ZS-NAS proxies in constrained NAS.
Binarized Neural Architecture Search for Efficient Object Recognition
In this paper, binarized neural architecture search (BNAS), with a search space of binarized convolutions, is introduced to produce extremely compressed models to reduce huge computational cost on embedded devices for edge computing.
Anti-Bandit Neural Architecture Search for Model Defense
Deep convolutional neural networks (DCNNs) have dominated as the best performers in machine learning, but can be challenged by adversarial attacks.
Cogradient Descent for Bilinear Optimization
Conventional learning methods simplify the bilinear model by regarding two intrinsically coupled factors independently, which degrades the optimization procedure.
CP-NAS: Child-Parent Neural Architecture Search for Binary Neural Networks
To this end, a Child-Parent (CP) model is introduced to a differentiable NAS to search the binarized architecture (Child) under the supervision of a full-precision model (Parent).
Binarized Neural Architecture Search
A variant, binarized neural architecture search (BNAS), with a search space of binarized convolutions, can produce extremely compressed models.
