Search Results for author:
Found 8 papers, 4 papers with code
Memory-based Adapters for Online 3D Scene Perception
To this end, we propose an adapter-based plug-and-play module for the backbone of 3D scene perception model, which constructs memory to cache and aggregate the extracted RGB-D features to empower offline models with temporal learning ability.
MCUFormer: Deploying Vision Transformers on Microcontrollers with Limited Memory
Due to the high price and heavy energy consumption of GPUs, deploying deep models on IoT devices such as microcontrollers makes significant contributions for ecological AI.
Anyview: Generalizable Indoor 3D Object Detection with Variable Frames
Existing methods only consider fixed frames of input data for a single detector, such as monocular RGB-D images or point clouds reconstructed from dense multi-view RGB-D images.
Embodied Task Planning with Large Language Models
Equipping embodied agents with commonsense is important for robots to successfully complete complex human instructions in general environments.
Towards Accurate Data-free Quantization for Diffusion Models
On the contrary, we design group-wise quantization functions for activation discretization in different timesteps and sample the optimal timestep for informative calibration image generation, so that our quantized diffusion model can reduce the discretization errors with negligible computational overhead.
3D Small Object Detection with Dynamic Spatial Pruning
Specifically, we theoretically derive a dynamic spatial pruning (DSP) strategy to prune the redundant spatial representation of 3D scene in a cascade manner according to the distribution of objects.
Binarizing Sparse Convolutional Networks for Efficient Point Cloud Analysis
In this paper, we propose binary sparse convolutional networks called BSC-Net for efficient point cloud analysis.
Back to Reality: Weakly-supervised 3D Object Detection with Shape-guided Label Enhancement
In this paper, we propose a weakly-supervised approach for 3D object detection, which makes it possible to train a strong 3D detector with position-level annotations (i. e. annotations of object centers).
