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Xinyu Chen

14 accepted papers

2026

Cross-domain Dual-stream Feature Disentanglement for Brain Disorder Prediction with Sparsely Labeled PET

CVPR 2026

Positron Emission Tomography (PET) can be used for the early diagnosis of various brain disorders. However, the annotation of PET scans requires the involvement of specialized nuclear medicine experts, making accurately annotated PET data extremely scarce. MRI-based cross-modal domain adaptation met

Cited by 0SourceScholar
2026

MetaGPT: A Large Vision-Language Model for Meme Metaphor Understanding

AAAI 2026technical

Meme is an expressive medium that often conveys rich emotions and intentions. Recent studies have confirmed the critical role of metaphors in meme understanding. However, existing metaphor research heavily relies on manual annotations, and mainstream vision-language models (VLMs) still struggle with

Cited by 0SourcePDFScholar
2026

Minimum-Length Conformal Prediction Sets for Ordinal Classification

AAAI 2026technical

Ordinal classification has been widely applied in many high-stakes applications, e.g., medical imaging and diagnosis, where reliable uncertainty quantification (UQ) is essential for decision making. Conformal prediction (CP) is a general UQ framework that provides statistically valid guarantees, whi

Cited by 0SourcePDFScholar
2026

MixFP4: Extending NVFP4 to Mixed Micro-Format via Scale-Bit Reuse and Tensor Core Co-design

ICML 2026poster

As large language models continue to scale, fine-grained, block-scaled low-precision formats such as NVFP4 and MXFP4 are increasingly adopted for their substantial throughput and memory benefits. In this regime, floating-point and integer quantizers exhibit complementary strengths in matching block-…

Cited by 0SourceScholar
2026

PULSE: Generative Phase Evolution for Non-Stationary Time Series Forecasting

ICML 2026poster

Time series forecasting under non-stationarity faces a fundamental tension between capturing stable representations and adapting to distribution shifts. Existing methods implicitly rely on static historical assumptions, leading to a critical failure mode we term Phase Amnesia, where models become bl…

Cited by 0SourceScholar
2025

Employing Discourse Coherence Enhancement to Improve Cross-Document Event and Entity Coreference Resolution

ACL 2025long

Cross-Document Coreference Resolution (CDCR) aims to identify and group together mentions of a specific event or entity that occur across multiple documents. In contrast to the within-document tasks, in which event and entity mentions are linked by rich and coherent contexts, cross-document mentions…

2025

Enhancing LLM-Based Social Bot via an Adversarial Learning Framework

EMNLP 2025

Developing Large Language Model (LLM) agents that exhibit human-like behavior, encompassing not only individual heterogeneity rooted in unique user profiles but also adaptive response to socially connected neighbors, is a significant research challenge. Social media platforms, with their diverse use

2025

FreeControl: Efficient, Training-Free Structural Control via One-Step Attention Extraction

NeurIPS 2025poster

Controlling the spatial and semantic structure of diffusion-generated images remains a challenge. Existing methods like ControlNet rely on handcrafted condition maps and retraining, limiting flexibility and generalization. Inversion-based approaches offer stronger alignment but incur high inference…

Cited by 0SourceScholar
2025

Multi-Cache Enhanced Prototype Learning for Test-Time Generalization of Vision-Language Models

ICCV 2025poster

In zero-shot setting, test-time adaptation adjusts pre-trained models using unlabeled data from the test phase to enhance performance on unknown test distributions. Existing cache-enhanced TTA methods rely on a low-entropy criterion to select samples for prototype construction, assuming intra-class…

Cited by 0SourcePDFScholar
2025

Safe RLHF-V: Safe Reinforcement Learning from Multi-modal Human Feedback

NeurIPS 2025poster

Multimodal large language models (MLLMs) are essential for building general-purpose AI assistants; however, they pose increasing safety risks. How can we ensure safety alignment of MLLMs to prevent undesired behaviors? Going further, it is critical to explore how to fine-tune MLLMs to preserve capab…

Cited by 0SourceScholar
2025

VideoVista-CulturalLingo: 360° Horizons-Bridging Cultures, Languages, and Domains in Video Comprehension

ACL 2025long

Assessing the video comprehension capabilities of multimodal AI systems can effectively measure their understanding and reasoning abilities. Most video evaluation benchmarks are limited to a single language, typically English, and predominantly feature videos rooted in Western cultural contexts. In…

Cited by 0SourcePDFScholar
2024

Cognitive Visual-Language Mapper: Advancing Multimodal Comprehension with Enhanced Visual Knowledge Alignment

ACL 2024long

Evaluating and Rethinking the current landscape of Large Multimodal Models (LMMs), we observe that widely-used visual-language projection approaches (e.g., Q-former or MLP) focus on the alignment of image-text descriptions yet ignore the visual knowledge-dimension alignment, i.e., connecting visuals…