Search Results for author:
Found 20 papers, 16 papers with code
CogVLM2: Visual Language Models for Image and Video Understanding
Beginning with VisualGLM and CogVLM, we are continuously exploring VLMs in pursuit of enhanced vision-language fusion, efficient higher-resolution architecture, and broader modalities and applications.
Ranked #5 on
Visual Question Answering
on MM-Vet
CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer
We present CogVideoX, a large-scale text-to-video generation model based on diffusion transformer, which can generate 10-second continuous videos aligned with text prompt, with a frame rate of 16 fps and resolution of 768 * 1360 pixels.
A Survey on Self-play Methods in Reinforcement Learning
Self-play, characterized by agents' interactions with copies or past versions of itself, has recently gained prominence in reinforcement learning.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Priorformer: A UGC-VQA Method with content and distortion priors
User Generated Content (UGC) videos are susceptible to complicated and variant degradations and contents, which prevents the existing blind video quality assessment (BVQA) models from good performance since the lack of the adapability of distortions and contents.
Soft-QMIX: Integrating Maximum Entropy For Monotonic Value Function Factorization
In this paper, we propose an enhancement to QMIX by incorporating an additional local Q-value learning method within the maximum entropy RL framework.
LVBench: An Extreme Long Video Understanding Benchmark
To address this gap, we introduce LVBench, a benchmark specifically designed for long video understanding.
LLMArena: Assessing Capabilities of Large Language Models in Dynamic Multi-Agent Environments
Recent advancements in large language models (LLMs) have revealed their potential for achieving autonomous agents possessing human-level intelligence.
AutoSAT: Automatically Optimize SAT Solvers via Large Language Models
Heuristics are crucial in SAT solvers, but no heuristic rules are suitable for all SAT problems.
OpenRL: A Unified Reinforcement Learning Framework
We present OpenRL, an advanced reinforcement learning (RL) framework designed to accommodate a diverse array of tasks, from single-agent challenges to complex multi-agent systems.
Robustness and Generalizability of Deepfake Detection: A Study with Diffusion Models
The rise of deepfake images, especially of well-known personalities, poses a serious threat to the dissemination of authentic information.
Diverse Policies Converge in Reward-free Markov Decision Processe
Under such a framework, we also propose a provably efficient diversity reinforcement learning algorithm.
SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks
The Swift module is a small encoder-decoder LM fine-tuned on the oracle agent's action trajectories, while the Sage module employs LLMs such as GPT-4 for subgoal planning and grounding.
TiZero: Mastering Multi-Agent Football with Curriculum Learning and Self-Play
Multi-agent football poses an unsolved challenge in AI research.
Learning Graph-Enhanced Commander-Executor for Multi-Agent Navigation
Goal-conditioned hierarchical reinforcement learning (HRL) provides a promising direction to tackle this challenge by introducing a hierarchical structure to decompose the search space, where the low-level policy predicts primitive actions in the guidance of the goals derived from the high-level policy.
Hierarchical Reinforcement Learning
Multi-agent Reinforcement Learning
+2
DGPO: Discovering Multiple Strategies with Diversity-Guided Policy Optimization
We propose Diversity-Guided Policy Optimization (DGPO), an on-policy algorithm that discovers multiple strategies for solving a given task.
TiKick: Towards Playing Multi-agent Football Full Games from Single-agent Demonstrations
To the best of our knowledge, Tikick is the first learning-based AI system that can take over the multi-agent Google Research Football full game, while previous work could either control a single agent or experiment on toy academic scenarios.
Ranking Cost: Building An Efficient and Scalable Circuit Routing Planner with Evolution-Based Optimization
In this work, we propose a new algorithm for circuit routing, named Ranking Cost, which innovatively combines search-based methods (i. e., A* algorithm) and learning-based methods (i. e., Evolution Strategies) to form an efficient and trainable router.
Ranking Cost: One-Stage Circuit Routing by Directly Optimizing Global Objective Function
In our method, we introduce a new set of variables called cost maps, which can help the A* router to find out proper paths to achieve the global object.
SVQN: Sequential Variational Soft Q-Learning Networks
Partially Observable Markov Decision Processes (POMDPs) are popular and flexible models for real-world decision-making applications that demand the information from past observations to make optimal decisions.
Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters
Such "in-the-tail" data is notoriously hard to observe, making both training and testing difficult.
