Search Results for author:
Found 21 papers, 7 papers with code
A SAM-guided Two-stream Lightweight Model for Anomaly Detection
In this paper, considering these two critical factors, we propose a SAM-guided Two-stream Lightweight Model for unsupervised anomaly detection (STLM) that not only aligns with the two practical application requirements but also harnesses the robust generalization capabilities of SAM.
LKCA: Large Kernel Convolutional Attention
We revisit the relationship between attention mechanisms and large kernel ConvNets in visual transformers and propose a new spatial attention named Large Kernel Convolutional Attention (LKCA).
Large Scale Training of Graph Neural Networks for Optimal Markov-Chain Partitioning Using the Kemeny Constant
In this work, we propose several GNN-based architectures to tackle the graph partitioning problem for Markov Chains described as kinetic networks.
Mitigate Replication and Copying in Diffusion Models with Generalized Caption and Dual Fusion Enhancement
While diffusion models demonstrate a remarkable capability for generating high-quality images, their tendency to `replicate' training data raises privacy concerns.
Toward a Deeper Understanding: RetNet Viewed through Convolution
A straightforward way to locally adapt the self-attention matrix can be realized by an element-wise learnable weight mask (ELM), for which our preliminary results show promising results.
Never Explore Repeatedly in Multi-Agent Reinforcement Learning
In the realm of multi-agent reinforcement learning, intrinsic motivations have emerged as a pivotal tool for exploration.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Understanding Segment Anything Model: SAM is Biased Towards Texture Rather than Shape
Different from label-oriented recognition tasks, the SAM is trained to predict a mask for covering the object shape based on a promt.
Learning Diverse Risk Preferences in Population-based Self-play
In this paper, we aim to introduce diversity from the perspective that agents could have diverse risk preferences in the face of uncertainty.
A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering
This is an ongoing project and we intend to update the manuscript on a regular basis.
Generative AI meets 3D: A Survey on Text-to-3D in AIGC Era
AI generated content) has made remarkable progress in the past few years, among which text-guided content generation is the most practical one since it enables the interaction between human instruction and AIGC.
When ChatGPT for Computer Vision Will Come? From 2D to 3D
On top of that, this work presents an outlook on the development of AIGC in 3D from the data perspective.
One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era
Overall, this work is the first to survey ChatGPT with a comprehensive review of its underlying technology, applications, and challenges.
A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?
As ChatGPT goes viral, generative AI (AIGC, a. k. a AI-generated content) has made headlines everywhere because of its ability to analyze and create text, images, and beyond.
SAL-ViT: Towards Latency Efficient Private Inference on ViT using Selective Attention Search with a Learnable Softmax Approximation
Then, given our observation that external attention (EA) presents lower PI latency than widely-adopted self-attention (SA) at the cost of accuracy, we present a selective attention search (SAS) method to integrate the strength of EA and SA.
Towards Global Optimality in Cooperative MARL with the Transformation And Distillation Framework
Decentralized execution is one core demand in cooperative multi-agent reinforcement learning (MARL).
Multi-agent Reinforcement Learning
Policy Gradient Methods
+2
Containerized Distributed Value-Based Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning tasks put a high demand on the volume of training samples.
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning
Moreover, we extend ICQ to multi-agent tasks by decomposing the joint-policy under the implicit constraint.
Celebrating Diversity in Shared Multi-Agent Reinforcement Learning
Recently, deep multi-agent reinforcement learning (MARL) has shown the promise to solve complex cooperative tasks.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
Modeling the Interaction between Agents in Cooperative Multi-Agent Reinforcement Learning
Value-based methods of multi-agent reinforcement learning (MARL), especially the value decomposition methods, have been demonstrated on a range of challenging cooperative tasks.
A Multitask Deep Learning Approach for User Depression Detection on Sina Weibo
In recent years, due to the mental burden of depression, the number of people who endanger their lives has been increasing rapidly.
SOAC: The Soft Option Actor-Critic Architecture
In these tasks, our approach learns a diverse set of options, each of whose state-action space has strong coherence.
