4 citations · 5 across the 5 of their papers we have counts for
5 papers
ReMA: A Training-Free Plug-and-Play Mixing Augmentation for Video Behavior Recognition
Feng-Qi Cui, Jinyang Huang, Sirui Zhao +4
Video behavior recognition demands stable and discriminative representations under complex spatiotemporal variations. However, prevailing data augmentation strategies for videos re…
Multi-turn Training with Basic Human Feedback Helps Little on LLM Reasoning
Qiang Liu, Wuganjing Song, Zhenzhou Lin +4
The reasoning capabilities of Large Language Models (LLMs) are typically developed through the single-turn reinforcement learning, whereas real-world applications often involve mul…
HiDream-I1: A High-Efficient Image Generative Foundation Model with Sparse Diffusion Transformer
Qi Cai, Jingwen Chen, Yang Chen +19
Recent advancements in image generative foundation models have prioritized quality improvements but often at the cost of increased computational complexity and inference latency. T…
On-Device Language Models: A Comprehensive Review
Jiajun Xu, Zhiyuan Li, Wei Chen +4
The advent of large language models (LLMs) revolutionized natural language processing applications, and running LLMs on edge devices has become increasingly attractive for reasons…
CodeGraph: Enhancing Graph Reasoning of LLMs with Code
Qiaolong Cai, Zhaowei Wang, Shizhe Diao +2
With the increasing popularity of large language models (LLMs), reasoning on basic graph algorithm problems is an essential intermediate step in assessing their abilities to proces…