123 citations · 166 across the 14 of their papers we have counts for
14 papers
Efficient Matrix Implementation for Rotary Position Embedding
Chen Minqi, Zhongqi Yue, Shihao Zhang +5
Rotary Position Embedding (RoPE) has become a core component of modern Transformer architectures across language, vision, and 3D domains. However, existing implementations rely on…
Thinking with Images as Continuous Actions: Numerical Visual Chain-of-Thought
Kesen Zhao, Beier Zhu, Junbao Zhou +3
Recent MLLMs increasingly rely on visual chain-of-thought to perform region-grounded reasoning over images. However, existing approaches ground regions via either textified coordin…
AnyEdit: Mastering Unified High-Quality Image Editing for Any Idea
Qifan Yu, Wei Chow, Zhongqi Yue +7
Instruction-based image editing aims to modify specific image elements with natural language instructions. However, current models in this domain often struggle to accurately execu…
Few-shot Learner Parameterization by Diffusion Time-steps
Zhongqi Yue, Pan Zhou, Richang Hong +2
Even when using large multi-modal foundation models, few-shot learning is still challenging -- if there is no proper inductive bias, it is nearly impossible to keep the nuanced cla…
Exploring Diffusion Time-steps for Unsupervised Representation Learning
Zhongqi Yue, Jiankun Wang, Qianru Sun +3
Representation learning is all about discovering the hidden modular attributes that generate the data faithfully. We explore the potential of Denoising Diffusion Probabilistic Mode…
Proposal-Level Unsupervised Domain Adaptation for Open World Unbiased Detector
Xuanyi Liu, Zhongqi Yue, Xian-Sheng Hua
Open World Object Detection (OWOD) combines open-set object detection with incremental learning capabilities to handle the challenge of the open and dynamic visual world. Existing…
Invariant Feature Regularization for Fair Face Recognition
Jiali Ma, Zhongqi Yue, Kagaya Tomoyuki +4
Fair face recognition is all about learning invariant feature that generalizes to unseen faces in any demographic group. Unfortunately, face datasets inevitably capture the imbalan…
Make the U in UDA Matter: Invariant Consistency Learning for Unsupervised Domain Adaptation
Zhongqi Yue, Hanwang Zhang, Qianru Sun
Domain Adaptation (DA) is always challenged by the spurious correlation between domain-invariant features (e.g., class identity) and domain-specific features (e.g., environment) th…
Random Boxes Are Open-world Object Detectors
Yanghao Wang, Zhongqi Yue, Xian-Sheng Hua +1
We show that classifiers trained with random region proposals achieve state-of-the-art Open-world Object Detection (OWOD): they can not only maintain the accuracy of the known obje…
Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detection
Hui Lv, Zhongqi Yue, Qianru Sun +3
Weakly Supervised Video Anomaly Detection (WSVAD) is challenging because the binary anomaly label is only given on the video level, but the output requires snippet-level prediction…
Self-Supervised Learning Disentangled Group Representation as Feature
Tan Wang, Zhongqi Yue, Jianqiang Huang +2
A good visual representation is an inference map from observations (images) to features (vectors) that faithfully reflects the hidden modularized generative factors (semantics). In…
Transporting Causal Mechanisms for Unsupervised Domain Adaptation
Zhongqi Yue, Qianru Sun, Xian-Sheng Hua +1
Existing Unsupervised Domain Adaptation (UDA) literature adopts the covariate shift and conditional shift assumptions, which essentially encourage models to learn common features a…
Counterfactual Zero-Shot and Open-Set Visual Recognition
Zhongqi Yue, Tan Wang, Hanwang Zhang +2
We present a novel counterfactual framework for both Zero-Shot Learning (ZSL) and Open-Set Recognition (OSR), whose common challenge is generalizing to the unseen-classes by only t…
Interventional Few-Shot Learning
Zhongqi Yue, Hanwang Zhang, Qianru Sun +1
We uncover an ever-overlooked deficiency in the prevailing Few-Shot Learning (FSL) methods: the pre-trained knowledge is indeed a confounder that limits the performance. This findi…