Search Results for author:
Found 12 papers, 2 papers with code
Mipha: A Comprehensive Overhaul of Multimodal Assistant with Small Language Models
Multimodal Large Language Models (MLLMs) have showcased impressive skills in tasks related to visual understanding and reasoning.
Ranked #68 on
Visual Question Answering
on MM-Vet
Language-Conditioned Robotic Manipulation with Fast and Slow Thinking
The language-conditioned robotic manipulation aims to transfer natural language instructions into executable actions, from simple pick-and-place to tasks requiring intent recognition and visual reasoning.
Object-Centric Instruction Augmentation for Robotic Manipulation
Humans interpret scenes by recognizing both the identities and positions of objects in their observations.
DTF-Net: Category-Level Pose Estimation and Shape Reconstruction via Deformable Template Field
We introduce a pose regression module that shares the deformation features and template codes from the fields to estimate the accurate 6D pose of each object in the scene.
RDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completion
Raw depth images captured in indoor scenarios frequently exhibit extensive missing values due to the inherent limitations of the sensors and environments.
CMG-Net: An End-to-End Contact-Based Multi-Finger Dexterous Grasping Network
Our work significantly outperforms the state-of-the-art for three-finger robotic hands.
CP$^3$: Channel Pruning Plug-in for Point-based Networks
CP$^3$ is elaborately designed to leverage the characteristics of point clouds and PNNs in order to enable 2D channel pruning methods for PNNs.
CP3: Channel Pruning Plug-In for Point-Based Networks
Directly implementing the 2D CNN channel pruning methods to PNNs undermine the performance of PNNs because of the different representations of 2D images and 3D point clouds as well as the network architecture disparity.
Label-Guided Auxiliary Training Improves 3D Object Detector
Detecting 3D objects from point clouds is a practical yet challenging task that has attracted increasing attention recently.
RGB-Depth Fusion GAN for Indoor Depth Completion
In this paper, we design a novel two-branch end-to-end fusion network, which takes a pair of RGB and incomplete depth images as input to predict a dense and completed depth map.
Make A Long Image Short: Adaptive Token Length for Vision Transformers
The TLA enables the ReViT to process the image with the minimum sufficient number of tokens during inference.
Training BatchNorm Only in Neural Architecture Search and Beyond
Critically, there is no effort to understand 1) why training BatchNorm only can find the perform-well architectures with the reduced supernet-training time, and 2) what is the difference between the train-BN-only supernet and the standard-train supernet.
