247 citations · 445 across the 12 of their papers we have counts for
12 papers
FBCIR: Balancing Cross-Modal Focuses in Composed Image Retrieval
Chenchen Zhao, Jianhuan Zhuo, Muxi Chen +6
Composed image retrieval (CIR) requires multi-modal models to jointly reason over visual content and semantic modifications presented in text-image input pairs. While current CIR m…
\textit{FocaLogic}: Logic-Based Interpretation of Visual Model Decisions
Chenchen Zhao, Muxi Chen, Qiang Xu
Interpretability of modern visual models is crucial, particularly in high-stakes applications. However, existing interpretability methods typically suffer from either reliance on w…
FailureAtlas:Mapping the Failure Landscape of T2I Models via Active Exploration
Muxi Chen, Zhaohua Zhang, Chenchen Zhao +8
Static benchmarks have provided a valuable foundation for comparing Text-to-Image (T2I) models. However, their passive design offers limited diagnostic power, struggling to uncover…
GRAPE: Let GRPO Supervise Query Rewriting by Ranking for Retrieval
Zhaohua Zhang, Jianhuan Zhuo, Muxi Chen +8
The CLIP model has established itself as a cornerstone of large-scale retrieval systems. However, its performance often degrades under distributional shifts such as multilingual, l…
Concept-SAE: A Controllable and Invertible Concept Interface for Sparse Autoencoders
Jianrong Ding, Muxi Chen, Chenchen Zhao +1
Standard Sparse Autoencoders (SAEs) excel at discovering a dictionary of a model's learned features, providing a powerful lens for passive feature discovery. However, this passive…
HiBug2: Efficient and Interpretable Error Slice Discovery for Comprehensive Model Debugging
Muxi Chen, Chenchen Zhao, Qiang Xu
Despite the significant success of deep learning models in computer vision, they often exhibit systematic failures on specific data subsets, known as error slices. Identifying and…
The Breeze 2 Herd of Models: Traditional Chinese LLMs Based on Llama with Vision-Aware and Function-Calling Capabilities
MediaTek Research, :, Chan-Jan Hsu +6
Llama-Breeze2 (hereinafter referred to as Breeze2) is a suite of advanced multi-modal language models, available in 3B and 8B parameter configurations, specifically designed to enh…
Evaluating Text-to-Image Generative Models: An Empirical Study on Human Image Synthesis
Muxi Chen, Yi Liu, Jian Yi +5
In this paper, we present an empirical study introducing a nuanced evaluation framework for text-to-image (T2I) generative models, applied to human image synthesis. Our framework c…
FrAug: Frequency Domain Augmentation for Time Series Forecasting
Muxi Chen, Zhijian Xu, Ailing Zeng +1
Data augmentation (DA) has become a de facto solution to expand training data size for deep learning. With the proliferation of deep models for time series analysis, various time s…
An Empirical Study on the Efficacy of Deep Active Learning for Image Classification
Yu Li, Muxi Chen, Yannan Liu +2
Deep Active Learning (DAL) has been advocated as a promising method to reduce labeling costs in supervised learning. However, existing evaluations of DAL methods are based on diffe…
Are Transformers Effective for Time Series Forecasting?
Ailing Zeng, Muxi Chen, Lei Zhang +1
Recently, there has been a surge of Transformer-based solutions for the long-term time series forecasting (LTSF) task. Despite the growing performance over the past few years, we q…
SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction
Minhao Liu, Ailing Zeng, Muxi Chen +4
One unique property of time series is that the temporal relations are largely preserved after downsampling into two sub-sequences. By taking advantage of this property, we propose…