1 citations · 3 across the 14 of their papers we have counts for
15 papers
Decoupling Stability and Plasticity for Multi-Modal Test-Time Adaptation
Yongbo He, Zirun Guo, Tao Jin
Adapting pretrained multi-modal models to evolving test-time distributions, known as multi-modal test-time adaptation, presents a significant challenge. Existing methods frequently…
Thinking with Programming Vision: Towards a Unified View for Thinking with Images
Zirun Guo, Minjie Hong, Feng Zhang +2
Multimodal large language models (MLLMs) that think with images can interactively use tools to reason about visual inputs, but current approaches often rely on a narrow set of tool…
Generative Reasoning Recommendation via LLMs
Minjie Hong, Zetong Zhou, Zirun Guo +5
Despite their remarkable reasoning capabilities across diverse domains, large language models (LLMs) face fundamental challenges in natively functioning as generative reasoning rec…
LLM-I: LLMs are Naturally Interleaved Multimodal Creators
Zirun Guo, Feng Zhang, Kai Jia +1
We propose LLM-Interleaved (LLM-I), a flexible and dynamic framework that reframes interleaved image-text generation as a tool-use problem. LLM-I is designed to overcome the "one-t…
APO: Enhancing Reasoning Ability of MLLMs via Asymmetric Policy Optimization
Minjie Hong, Zirun Guo, Yan Xia +4
Multimodal Large Language Models (MLLMs) are powerful at integrating diverse data, but they often struggle with complex reasoning. While Reinforcement learning (RL) can boost reaso…
Observe-R1: Unlocking Reasoning Abilities of MLLMs with Dynamic Progressive Reinforcement Learning
Zirun Guo, Minjie Hong, Tao Jin
Reinforcement Learning (RL) has shown promise in improving the reasoning abilities of Large Language Models (LLMs). However, the specific challenges of adapting RL to multimodal da…
Diff-Prompt: Diffusion-Driven Prompt Generator with Mask Supervision
Weicai Yan, Wang Lin, Zirun Guo +5
Prompt learning has demonstrated promising results in fine-tuning pre-trained multimodal models. However, the performance improvement is limited when applied to more complex and fi…
ConceptGuard: Continual Personalized Text-to-Image Generation with Forgetting and Confusion Mitigation
Zirun Guo, Tao Jin
Diffusion customization methods have achieved impressive results with only a minimal number of user-provided images. However, existing approaches customize concepts collectively, w…
Smoothing the Shift: Towards Stable Test-Time Adaptation under Complex Multimodal Noises
Zirun Guo, Tao Jin
Test-Time Adaptation (TTA) aims to tackle distribution shifts using unlabeled test data without access to the source data. In the context of multimodal data, there are more complex…
Efficient Prompting for Continual Adaptation to Missing Modalities
Zirun Guo, Shulei Wang, Wang Lin +3
Missing modality issues are common in real-world applications, arising from factors such as equipment failures and privacy concerns. When fine-tuning pre-trained models on downstre…
Low-rank Prompt Interaction for Continual Vision-Language Retrieval
Weicai Yan, Ye Wang, Wang Lin +3
Research on continual learning in multi-modal tasks has been receiving increasing attention. However, most existing work overlooks the explicit cross-modal and cross-task interacti…
A Wander Through the Multimodal Landscape: Efficient Transfer Learning via Low-rank Sequence Multimodal Adapter
Zirun Guo, Xize Cheng, Yangyang Wu +1
Efficient transfer learning methods such as adapter-based methods have shown great success in unimodal models and vision-language models. However, existing methods have two main ch…
Bridging the Gap for Test-Time Multimodal Sentiment Analysis
Zirun Guo, Tao Jin, Wenlong Xu +2
Multimodal sentiment analysis (MSA) is an emerging research topic that aims to understand and recognize human sentiment or emotions through multiple modalities. However, in real-wo…
Classifier-guided Gradient Modulation for Enhanced Multimodal Learning
Zirun Guo, Tao Jin, Jingyuan Chen +1
Multimodal learning has developed very fast in recent years. However, during the multimodal training process, the model tends to rely on only one modality based on which it could l…
Multimodal Prompt Learning with Missing Modalities for Sentiment Analysis and Emotion Recognition
Zirun Guo, Tao Jin, Zhou Zhao
The development of multimodal models has significantly advanced multimodal sentiment analysis and emotion recognition. However, in real-world applications, the presence of various…