1 citations · 1 across the 10 of their papers we have counts for
11 papers
The Hyperspherical Geometry of CLIP Latent Space: A Semantic Mixture Model
Zijie Yu, Gaowen Liu, Ramana Rao Kompella +2
Contrastive Language-Image Pretraining (CLIP) representations form a semantic embedding space governed by cosine similarity, reflecting an intrinsic hyperspherical geometry. Howeve…
Do LLMs Recognize Your Latent Preferences? A Benchmark for Latent Information Discovery in Personalized Interaction
Ioannis Tsaknakis, Bingqing Song, Shuyu Gan +5
Large Language Models (LLMs) excel at producing broadly relevant text, but this generality becomes a limitation when user-specific preferences are required, such as recommending re…
ConQuER: Modular Architectures for Control and Bias Mitigation in IQP Quantum Generative Models
Xiaocheng Zou, Shijin Duan, Charles Fleming +4
Quantum generative models based on instantaneous quantum polynomial (IQP) circuits show great promise in learning complex distributions while maintaining classical trainability. Ho…
Orientation-anchored Hyper-Gaussian for 4D Reconstruction from Casual Videos
Junyi Wu, Jiachen Tao, Haoxuan Wang +3
We present Orientation-anchored Gaussian Splatting (OriGS), a novel framework for high-quality 4D reconstruction from casually captured monocular videos. While recent advances exte…
MetaSeal: Defending Against Image Attribution Forgery Through Content-Dependent Cryptographic Watermarks
Tong Zhou, Ruyi Ding, Gaowen Liu +5
The rapid growth of digital and AI-generated images has amplified the need for secure and verifiable methods of image attribution. While digital watermarking offers more robust pro…
Targeted Forgetting of Image Subgroups in CLIP Models
Zeliang Zhang, Gaowen Liu, Charles Fleming +2
Foundation models (FMs) such as CLIP have demonstrated impressive zero-shot performance across various tasks by leveraging large-scale, unsupervised pre-training. However, they oft…
A Neurosymbolic Agent System for Compositional Visual Reasoning
Yichang Xu, Gaowen Liu, Ramana Rao Kompella +5
The advancement in large language models (LLMs) and large vision models has fueled the rapid progress in multi-modal vision-language reasoning capabilities. However, existing visio…
ProDiF: Protecting Domain-Invariant Features to Secure Pre-Trained Models Against Extraction
Tong Zhou, Shijin Duan, Gaowen Liu +4
Pre-trained models are valuable intellectual property, capturing both domain-specific and domain-invariant features within their weight spaces. However, model extraction attacks th…
Towards Vector Optimization on Low-Dimensional Vector Symbolic Architecture
Shijin Duan, Yejia Liu, Gaowen Liu +3
Vector Symbolic Architecture (VSA) is emerging in machine learning due to its efficiency, but they are hindered by issues of hyperdimensionality and accuracy. As a promising mitiga…
Safeguarding Text-to-Image Generation via Inference-Time Prompt-Noise Optimization
Jiangweizhi Peng, Zhiwei Tang, Gaowen Liu +2
Text-to-Image (T2I) diffusion models are widely recognized for their ability to generate high-quality and diverse images based on text prompts. However, despite recent advances, th…
Self-Adapting Large Visual-Language Models to Edge Devices across Visual Modalities
Kaiwen Cai, Zhekai Duan, Gaowen Liu +2
Recent advancements in Vision-Language (VL) models have sparked interest in their deployment on edge devices, yet challenges in handling diverse visual modalities, manual annotatio…