2 citations · 4 across the 15 of their papers we have counts for
15 papers
DeVA: Decoupled Video-Action Model with physical guidance for robot policy learning
Mengqi Zhang, Sahil Khose, Simar Kareer +3
Generalizable robot manipulation requires policies that can anticipate how visual scenes evolve while executing language instructions. While recent Vision-Language-Action models be…
The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence
Aili Chen, Aonian Li, Baichuan Zhou +215
We introduce the MiniMax-M2 series, a family of Mixture-of-Experts language models built around the principle that mini activations can unleash maximum real-world intelligence. The…
Thinking with Comics: Enhancing Multimodal Reasoning through Structured Visual Storytelling
Andong Chen, Wenxin Zhu, Qiuyu Ding +3
Chain-of-Thought reasoning has driven large language models to extend from thinking with text to thinking with images and videos. However, different modalities still have clear lim…
CogEvo-Edu: Cognitive Evolution Educational Multi-Agent Collaborative System
Yefeng Wu, Yuchen Song, Yecheng Zhao +2
Large language models (LLMs) are increasingly deployed as conversational tutors in STEM education, yet most systems still rely on a single LLM with a static retrieval-augmented gen…
From Perception to Reasoning: Deep Thinking Empowers Multimodal Large Language Models
Wenxin Zhu, Andong Chen, Yuchen Song +4
With the remarkable success of Multimodal Large Language Models (MLLMs) in perception tasks, enhancing their complex reasoning capabilities has emerged as a critical research focus…
CGF-DETR: Cross-Gated Fusion DETR for Enhanced Pneumonia Detection in Chest X-rays
Yefeng Wu, Yuchen Song, Ling Wu +2
Pneumonia remains a leading cause of morbidity and mortality worldwide, necessitating accurate and efficient automated detection systems. While recent transformer-based detectors l…
Culture In a Frame: CB as a Comic-Based Benchmark for Multimodal Culturally Awareness
Yuchen Song, Andong Chen, Wenxin Zhu +4
Cultural awareness capabilities have emerged as a critical capability for Multimodal Large Language Models (MLLMs). However, current benchmarks lack progressed difficulty in their…
Enhancing Fairness in Autoencoders for Node-Level Graph Anomaly Detection
Shouju Wang, Yuchen Song, Sheng'en Li +1
Graph anomaly detection (GAD) has become an increasingly important task across various domains. With the rapid development of graph neural networks (GNNs), GAD methods have achieve…
Dialect Normalization using Large Language Models and Morphological Rules
Antonios Dimakis, John Pavlopoulos, Antonios Anastasopoulos
Natural language understanding systems struggle with low-resource languages, including many dialects of high-resource ones. Dialect-to-standard normalization attempts to tackle thi…
M3: 3D-Spatial MultiModal Memory
Xueyan Zou, Yuchen Song, Ri-Zhao Qiu +4
We present 3D Spatial MultiModal Memory (M3), a multimodal memory system designed to retain information about medium-sized static scenes through video sources for visual perception…
Evaluating o1-Like LLMs: Unlocking Reasoning for Translation through Comprehensive Analysis
Andong Chen, Yuchen Song, Wenxin Zhu +4
The o1-Like LLMs are transforming AI by simulating human cognitive processes, but their performance in multilingual machine translation (MMT) remains underexplored. This study exam…
Make Imagination Clearer! Stable Diffusion-based Visual Imagination for Multimodal Machine Translation
Andong Chen, Yuchen Song, Kehai Chen +3
Visual information has been introduced for enhancing machine translation (MT), and its effectiveness heavily relies on the availability of large amounts of bilingual parallel sente…
Noro: Noise-Robust One-shot Voice Conversion with Hidden Speaker Representation Learning
Haorui He, Yuchen Song, Yuancheng Wang +6
The effectiveness of one-shot voice conversion (VC) decreases in real-world scenarios where reference speeches, which are often sourced from the internet, contain various disturban…
WildLMa: Long Horizon Loco-Manipulation in the Wild
Ri-Zhao Qiu, Yuchen Song, Xuanbin Peng +8
'In-the-wild' mobile manipulation aims to deploy robots in diverse real-world environments, which requires the robot to (1) have skills that generalize across object configurations…
Learning Generalizable Feature Fields for Mobile Manipulation
Ri-Zhao Qiu, Yafei Hu, Yuchen Song +8
An open problem in mobile manipulation is how to represent objects and scenes in a unified manner so that robots can use both for navigation and manipulation. The latter requires c…