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Yulin Luo

7 accepted papers

2026

Decomposing the Neurons: Activation Sparsity via Mixture of Experts for Continual Test Time Adaptation

AAAI 2026technical

Continual Test-Time Adaptation (CTTA), which aims to adapt the pre-trained model to ever-evolving target domains, emerges as an important task for vision models. As current vision models appear to be heavily biased towards texture, continuously adapting the model from one domain distribution to anot

Cited by 0SourcePDFScholar
2025

Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want

ICLR 2025poster

In this paper, we present the Draw-and-Understand framework, exploring how to integrate visual prompting understanding capabilities into Multimodal Large Language Models (MLLMs). Visual prompts allow users to interact through multi-modal instructions, enhancing the models' interactivity and fine-gra…

2025

RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation

RSS 2025poster

Developing robust and general-purpose manipulation policies is a key goal in robotics. To achieve effective generalization, it is essential to construct comprehensive datasets that encompass a large number of demonstration trajectories and diverse tasks. Unlike vision or language data, which can be…

Cited by 20PDFScholar
2025

SEEA-R1: Tree-Structured Reinforcement Fine-Tuning for Self-Evolving Embodied Agents

NeurIPS 2025poster

Self-evolution, the ability of agents to autonomously improve their reasoning and behavior, is essential for the embodied domain with long-horizon, real-world tasks. Despite current advancements in reinforcement fine-tuning (RFT) showing strong performance in enhancing reasoning in LLMs, its potenti…

Cited by 0SourcecodeScholar
2025

UniCTokens: Boosting Personalized Understanding and Generation via Unified Concept Tokens

NeurIPS 2025poster

Personalized models have demonstrated remarkable success in understanding and generating concepts provided by users. However, existing methods use separate concept tokens for understanding and generation, treating these tasks in isolation. This may result in limitations for generating images with co…

Cited by 0SourcecodeScholar
2024

Efficient Deweahter Mixture-of-Experts with Uncertainty-Aware Feature-Wise Linear Modulation

AAAI 2024technical

The Mixture-of-Experts (MoE) approach has demonstrated outstanding scalability in multi-task learning including low-level upstream tasks such as concurrent removal of multiple adverse weather effects. However, the conventional MoE architecture with parallel Feed Forward Network (FFN) experts leads t…

Cited by 21SourcePDFScholar
2024

LLM as Dataset Analyst: Subpopulation Structure Discovery with Large Language Model

ECCV 2024poster

"The distribution of subpopulations is an important property hidden within a dataset. Uncovering and analyzing the subpopulation distribution within datasets provides a comprehensive understanding of the datasets, standing as a powerful tool beneficial to various downstream tasks, including Dataset…