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Found 27 papers, 10 papers with code
Towards free-response paradigm: a theory on decision-making in spiking neural networks
The energy-efficient and brain-like information processing abilities of Spiking Neural Networks (SNNs) have attracted considerable attention, establishing them as a crucial element of brain-inspired computing.
Any2Point: Empowering Any-modality Large Models for Efficient 3D Understanding
The adapter incorporates prior spatial knowledge from the source modality to guide the local feature aggregation of 3D tokens, compelling the semantic adaption of any-modality transformers.
HPL-ESS: Hybrid Pseudo-Labeling for Unsupervised Event-based Semantic Segmentation
In this paper, we propose a novel hybrid pseudo-labeling framework for unsupervised event-based semantic segmentation, HPL-ESS, to alleviate the influence of noisy pseudo labels.
Towards Agile Robots: Intuitive Robot Position Speculation with Neural Networks
The RPSN incorporates a differentiable inverse kinematic algorithm and a neural network.
LR-CNN: Lightweight Row-centric Convolutional Neural Network Training for Memory Reduction
However, training its complex network is very space-consuming, since a lot of intermediate data are preserved across layers, especially when processing high-dimension inputs with a big batch size.
X4D-SceneFormer: Enhanced Scene Understanding on 4D Point Cloud Videos through Cross-modal Knowledge Transfer
The field of 4D point cloud understanding is rapidly developing with the goal of analyzing dynamic 3D point cloud sequences.
Calibration-free quantitative phase imaging in multi-core fiber endoscopes using end-to-end deep learning
Quantitative phase imaging (QPI) through multi-core fibers (MCFs) has been an emerging in vivo label-free endoscopic imaging modality with minimal invasiveness.
GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting
This strategy is essential to extend 3D Gaussian representation to reconstruct the whole scene rather than synthesize a static object in existing methods.
Kinematic-aware Prompting for Generalizable Articulated Object Manipulation with LLMs
Generalizable articulated object manipulation is essential for home-assistant robots.
AI Nushu: An Exploration of Language Emergence in Sisterhood -Through the Lens of Computational Linguistics
This paper presents "AI Nushu," an emerging language system inspired by Nushu (women's scripts), the unique language created and used exclusively by ancient Chinese women who were thought to be illiterate under a patriarchal society.
Point-PEFT: Parameter-Efficient Fine-Tuning for 3D Pre-trained Models
To this end, we introduce Point-PEFT, a novel framework for adapting point cloud pre-trained models with minimal learnable parameters.
The Compatibility between the Pangu Weather Forecasting Model and Meteorological Operational Data
Recently, multiple data-driven models based on machine learning for weather forecasting have emerged.
Toward spike-based stochastic neural computing
Inspired by the highly irregular spiking activity of cortical neurons, stochastic neural computing is an attractive theory for explaining the operating principles of the brain and the ability to represent uncertainty by intelligent agents.
One-Shot High-Fidelity Talking-Head Synthesis with Deformable Neural Radiance Field
Talking head generation aims to generate faces that maintain the identity information of the source image and imitate the motion of the driving image.
Towards Nonlinear-Motion-Aware and Occlusion-Robust Rolling Shutter Correction
This paper addresses the problem of rolling shutter correction in complex nonlinear and dynamic scenes with extreme occlusion.
ViewRefer: Grasp the Multi-view Knowledge for 3D Visual Grounding with GPT and Prototype Guidance
In this paper, we propose ViewRefer, a multi-view framework for 3D visual grounding exploring how to grasp the view knowledge from both text and 3D modalities.
Propagate And Calibrate: Real-time Passive Non-line-of-sight Tracking
In contrast, we propose a purely passive method to track a person walking in an invisible room by only observing a relay wall, which is more in line with real application scenarios, e. g., security.
Fully Self-Supervised Depth Estimation from Defocus Clue
We show that our framework circumvents the needs for the depth and AIF image ground-truth, and receives superior predictions, thus closing the gap between the theoretical success of DFD works and their applications in the real world.
ViewRefer: Grasp the Multi-view Knowledge for 3D Visual Grounding
In this paper, we propose ViewRefer, a multi-view framework for 3D visual grounding exploring how to grasp the view knowledge from both text and 3D modalities.
Meteorological Satellite Images Prediction Based on Deep Multi-scales Extrapolation Fusion
First, we downsample the original satellite images dataset with large size to several images datasets with smaller resolutions, then we use a deep spatiotemporal sequences prediction method to generate the multi-scales prediction images with different resolutions separately.
UFO: Unified Feature Optimization
UFO aims to benefit each single task with a large-scale pretraining on all tasks.
Learning Symbolic Operators: A Neurosymbolic Solution for Autonomous Disassembly of Electric Vehicle Battery
The booming of electric vehicles demands efficient battery disassembly for recycling to be environment-friendly.
Implicit Sample Extension for Unsupervised Person Re-Identification
Specifically, we generate support samples from actual samples and their neighbouring clusters in the embedding space through a progressive linear interpolation (PLI) strategy.
Unsupervised Multi-Source Domain Adaptation for Person Re-Identification
Although achieving great success, most of them only use limited data from a single-source domain for model pre-training, making the rich labeled data insufficiently exploited.
Are We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM
We also design benchmarking metrics for lifelong SLAM, with which the robustness and accuracy of pose estimation are evaluated separately.
A CNN-RNN Architecture for Multi-Label Weather Recognition
To address this problem, we make the first attempt to view weather recognition as a multi-label classification task, i. e., assigning an image more than one labels according to the displayed weather conditions.
