Search Results for author:
Found 16 papers, 9 papers with code
Gyroscope-Assisted Motion Deblurring Network
Yet, their practical usage in real-world deblurring, especially motion blur, remains limited due to the lack of pixel-aligned training triplets (background, blurred image, and blur heat map) and restricted information inherent in blurred images.
Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security
Next, we discuss several key challenges to achieve intelligent, efficient and secure Personal LLM Agents, followed by a comprehensive survey of representative solutions to address these challenges.
LF-ViT: Reducing Spatial Redundancy in Vision Transformer for Efficient Image Recognition
To address this, we present the Localization and Focus Vision Transformer (LF-ViT).
Spreeze: High-Throughput Parallel Reinforcement Learning Framework
While existing parallel RL frameworks encompass a variety of RL algorithms and parallelization techniques, the excessively burdensome communication frameworks hinder the attainment of the hardware's limit for final throughput and training effects on a single desktop.
PneumoLLM: Harnessing the Power of Large Language Model for Pneumoconiosis Diagnosis
Yet, this approach often requires optimization of extensive learnable parameters in the text branch and the dialogue head, potentially diminishing the LLMs' efficacy, especially with limited training data.
Improved Dense Nested Attention Network Based on Transformer for Infrared Small Target Detection
To address this issue, we propose a novel method for detecting infrared small targets called improved dense nested attention network (IDNANet), which is based on the transformer architecture.
TMA: Temporal Motion Aggregation for Event-based Optical Flow
In this paper, we argue that temporal continuity is a vital element of event-based optical flow and propose a novel Temporal Motion Aggregation (TMA) approach to unlock its potential.
Edge-Cloud Cooperation for DNN Inference via Reinforcement Learning and Supervised Learning
A reinforcement learning (RL)-based DNN compression approach is used to generate the lightweight model suitable for the edge from the heavyweight model.
BMD: A General Class-balanced Multicentric Dynamic Prototype Strategy for Source-free Domain Adaptation
Source-free Domain Adaptation (SFDA) aims to adapt a pre-trained source model to the unlabeled target domain without accessing the well-labeled source data, which is a much more practical setting due to the data privacy, security, and transmission issues.
Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysis
Point cloud completion is a generation and estimation issue derived from the partial point clouds, which plays a vital role in the applications in 3D computer vision.
Edge-Cloud Collaborated Object Detection via Difficult-Case Discriminator
To address this challenge, we propose a small-big model framework that deploys a big model in the cloud and a small model on the edge devices.
ACM-Net: Action Context Modeling Network for Weakly-Supervised Temporal Action Localization
Traditional methods mainly focus on foreground and background frames separation with only a single attention branch and class activation sequence.
Ranked #5 on
Weakly Supervised Action Localization
on THUMOS14
Weakly Supervised Action Localization
Weakly-supervised Temporal Action Localization
+1
Lightweight Convolutional Neural Network with Gaussian-based Grasping Representation for Robotic Grasping Detection
Extensive experiments on two public grasping datasets, Cornell and Jacquard demonstrate the state-of-the-art performance of our method in balancing accuracy and inference speed.
Ranked #1 on
Robotic Grasping
on Jacquard dataset
Reinforcement Learning Based Temporal Logic Control with Soft Constraints Using Limit-deterministic Generalized Buchi Automata
This paper studies the control synthesis of motion planning subject to uncertainties.
PointINet: Point Cloud Frame Interpolation Network
Generally, the frame rates of mechanical LiDAR sensors are 10 to 20 Hz, which is much lower than other commonly used sensors like cameras.
MoNet: Motion-based Point Cloud Prediction Network
3D point clouds accurately model 3D information of surrounding environment and are crucial for intelligent vehicles to perceive the scene.
