Search Results for author:
Found 18 papers, 14 papers with code
A Survey of Attacks on Large Vision-Language Models: Resources, Advances, and Future Trends
Compared to traditional Large Language Models (LLMs), LVLMs present great potential and challenges due to its closer proximity to the multi-resource real-world applications and the complexity of multi-modal processing.
Diffusion-Aided Joint Source Channel Coding For High Realism Wireless Image Transmission
In the following diffusion step, DiffJSCC uses the derived multimodal features, together with channel state information such as the signal-to-noise ratio (SNR), as conditions to guide the denoising diffusion process, which converts the initial random noise to the final reconstruction.
Diving with Penguins: Detecting Penguins and their Prey in Animal-borne Underwater Videos via Deep Learning
In this paper, we publish an animal-borne underwater video dataset of penguins and introduce a ready-to-deploy deep learning system capable of robustly detecting penguins (mAP50@98. 0%) and also instances of fish (mAP50@73. 3%).
Siamese Learning-based Monarch Butterfly Localization
A new GPS-less, daily localization method is proposed with deep learning sensor fusion that uses daylight intensity and temperature sensor data for Monarch butterfly tracking.
Efficient Computation Sharing for Multi-Task Visual Scene Understanding
Solving multiple visual tasks using individual models can be resource-intensive, while multi-task learning can conserve resources by sharing knowledge across different tasks.
NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning
First, GNNs can learn higher-order structural information by stacking more layers but can not deal with large depth due to the over-smoothing issue.
Efficient Deep Visual and Inertial Odometry with Adaptive Visual Modality Selection
In recent years, deep learning-based approaches for visual-inertial odometry (VIO) have shown remarkable performance outperforming traditional geometric methods.
Multi-Target Active Object Tracking with Monte Carlo Tree Search and Target Motion Modeling
In this work, we are dedicated to multi-target active object tracking (AOT), where there are multiple targets as well as multiple cameras in the environment.
LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent Reinforcement Learning
In this way, agents dealing with the same subtask share their learning of specific abilities and different subtasks correspond to different specific abilities.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed Agents
To the best of our knowledge, this work is the first to study the unexpected crashes in the multi-agent system.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement Learning
In this way, CTDS balances the full utilization of global observation during training and the feasibility of decentralized execution for online inference.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
Coordinate-Aligned Multi-Camera Collaboration for Active Multi-Object Tracking
In our approach, we regard each camera as an agent and address AMOT with a multi-agent reinforcement learning solution.
MCMARL: Parameterizing Value Function via Mixture of Categorical Distributions for Multi-Agent Reinforcement Learning
Specifically, we model both individual Q-values and global Q-value with categorical distribution.
Node Dependent Local Smoothing for Scalable Graph Learning
Recent works reveal that feature or label smoothing lies at the core of Graph Neural Networks (GNNs).
Deep Joint Source-Channel Coding for Wireless Image Transmission with Adaptive Rate Control
To the best of our knowledge, this is the first deep JSCC scheme that can automatically adjust its rate using a single network model.
OFDM-guided Deep Joint Source Channel Coding for Wireless Multipath Fading Channels
We investigate joint source channel coding (JSCC) for wireless image transmission over multipath fading channels.
Deep Joint Source Channel Coding for WirelessImage Transmission with OFDM
We present a deep learning based joint source channel coding (JSCC) scheme for wireless image transmission over multipath fading channels with non-linear signal clipping.
Migrating Monarch Butterfly Localization Using Multi-Sensor Fusion Neural Networks
Details of Monarch butterfly migration from the U. S. to Mexico remain a mystery due to lack of a proper localization technology to accurately localize and track butterfly migration.
