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Found 11 papers, 6 papers with code
Cross-Modal Conditioned Reconstruction for Language-guided Medical Image Segmentation
Recent developments underscore the potential of textual information in enhancing learning models for a deeper understanding of medical visual semantics.
Target-Grounded Graph-Aware Transformer for Aerial Vision-and-Dialog Navigation
This report details the methods of the winning entry of the AVDN Challenge in ICCV CLVL 2023.
ETPNav: Evolving Topological Planning for Vision-Language Navigation in Continuous Environments
To develop a robust VLN-CE agent, we propose a new navigation framework, ETPNav, which focuses on two critical skills: 1) the capability to abstract environments and generate long-range navigation plans, and 2) the ability of obstacle-avoiding control in continuous environments.
BEVBert: Multimodal Map Pre-training for Language-guided Navigation
Concretely, we build a local metric map to explicitly aggregate incomplete observations and remove duplicates, while modeling navigation dependency in a global topological map.
Ranked #2 on
Visual Navigation
on R2R
Neighbor Regularized Bayesian Optimization for Hyperparameter Optimization
Bayesian Optimization (BO) is a common solution to search optimal hyperparameters based on sample observations of a machine learning model.
1st Place Solutions for RxR-Habitat Vision-and-Language Navigation Competition (CVPR 2022)
Our model consists of three modules: the candidate waypoints predictor (CWP), the history enhanced planner and the tryout controller.
Landmark-RxR: Solving Vision-and-Language Navigation with Fine-Grained Alignment Supervision
In Vision-and-Language Navigation (VLN) task, an agent is asked to navigate inside 3D indoor environments following given instructions.
Neighbor-view Enhanced Model for Vision and Language Navigation
Specifically, our NvEM utilizes a subject module and a reference module to collect contexts from neighbor views.
Ranked #82 on
Vision and Language Navigation
on VLN Challenge
Time-dependent unbounded Hamiltonian simulation with vector norm scaling
We demonstrate that under suitable assumptions of the Hamiltonian and the initial vector, if the error is measured in terms of the vector norm, the computational cost may not increase at all as the norm of the Hamiltonian increases using Trotter type methods.
Quantum Physics Numerical Analysis Numerical Analysis
Hyperspectral Imaging Technology and Transfer Learning Utilized in Identification Haploid Maize Seeds
The experimental results show that, CNN model which is pre-trained by visible light image database can be applied to the near-infrared hyperspectral imaging-based identification of maize seeds, and high accurate identification rate can be achieved.
Maize Haploid Identification via LSTM-CNN and Hyperspectral Imaging Technology
In our study, the first attempt to design a high-accurate identification model of maize haploid seeds from diploid ones based on optimum waveband selection of the LSTM-CNN algorithm is realized via deep learning and hyperspectral imaging technology, with accuracy reaching 97% in the determining optimum waveband of 1367. 6-1526. 4nm.
