1 citations · 3 across the 8 of their papers we have counts for
8 papers
EventMemAgent: Hierarchical Event-Centric Memory for Online Video Understanding with Adaptive Tool Use
Siwei Wen, Zhangcheng Wang, Xingjian Zhang +2
Online video understanding requires models to perform continuous perception and long-range reasoning within potentially infinite visual streams. Its fundamental challenge lies in t…
An Investigation of Batch Normalization in Off-Policy Actor-Critic Algorithms
Li Wang, Sudun, Xingjian Zhang +2
Batch Normalization (BN) has played a pivotal role in the success of deep learning by improving training stability, mitigating overfitting, and enabling more effective optimization…
EDGE-GRPO: Entropy-Driven GRPO with Guided Error Correction for Advantage Diversity
Xingjian Zhang, Siwei Wen, Wenjun Wu +1
Large Language Models (LLMs) have made remarkable progress in enhancing step-by-step reasoning through reinforcement learning. However, the Group Relative Policy Optimization (GRPO…
CoE-Ops: Collaboration of LLM-based Experts for AIOps Question-Answering
Jinkun Zhao, Yuanshuai Wang, Xingjian Zhang +6
With the rapid evolution of artificial intelligence, AIOps has emerged as a prominent paradigm in DevOps. Lots of work has been proposed to improve the performance of different AIO…
TinyLLaVA-Video-R1: Towards Smaller LMMs for Video Reasoning
Xingjian Zhang, Siwei Wen, Wenjun Wu +1
Recently, improving the reasoning ability of large multimodal models (LMMs) through reinforcement learning has made great progress. However, most existing works are based on highly…
TinyLLaVA-Video: Towards Smaller LMMs for Video Understanding with Group Resampler
Xingjian Zhang, Xi Weng, Yihao Yue +3
Video behavior recognition and scene understanding are fundamental tasks in multimodal intelligence, serving as critical building blocks for numerous real-world applications. Throu…
Bench-CoE: a Framework for Collaboration of Experts from Benchmark
Yuanshuai Wang, Xingjian Zhang, Jinkun Zhao +5
Large Language Models (LLMs) are key technologies driving intelligent systems to handle multiple tasks. To meet the demands of various tasks, an increasing number of LLMs-driven ex…
TinyLLaVA Factory: A Modularized Codebase for Small-scale Large Multimodal Models
Junlong Jia, Ying Hu, Xi Weng +8
We present TinyLLaVA Factory, an open-source modular codebase for small-scale large multimodal models (LMMs) with a focus on simplicity of code implementations, extensibility of ne…