5 citations · 7 across the 7 of their papers we have counts for
8 papers
KidSpeak: A General Multi-purpose LLM for Kids' Speech Recognition and Screening
Rohan Sharma, Dancheng Liu, Jingchen Sun +4
With the rapid advancement of conversational and diffusion-based AI, there is a growing adoption of AI in educational services, ranging from grading and assessment tools to persona…
GUI-AIMA: Aligning Intrinsic Multimodal Attention with a Context Anchor for GUI Grounding
Shijie Zhou, Viet Dac Lai, Hao Tan +4
Graphical user interface (GUI) grounding is a key capability for computer-use agents, mapping natural-language instructions to actionable regions on the screen. Existing Multimodal…
Multimodal LLMs as Customized Reward Models for Text-to-Image Generation
Shijie Zhou, Ruiyi Zhang, Huaisheng Zhu +5
We introduce LLaVA-Reward, an efficient reward model designed to automatically evaluate text-to-image (T2I) generations across multiple perspectives, leveraging pretrained multimod…
A High-Quality Text-Rich Image Instruction Tuning Dataset via Hybrid Instruction Generation
Shijie Zhou, Ruiyi Zhang, Yufan Zhou +1
Large multimodal models still struggle with text-rich images because of inadequate training data. Self-Instruct provides an annotation-free way for generating instruction data, but…
Enhancing Diffusion Posterior Sampling for Inverse Problems by Integrating Crafted Measurements
Shijie Zhou, Huaisheng Zhu, Rohan Sharma +4
Diffusion models have emerged as a powerful foundation model for visual generations. With an appropriate sampling process, it can effectively serve as a generative prior for solvin…
Discriminative Adversarial Unlearning
Rohan Sharma, Shijie Zhou, Kaiyi Ji +1
We introduce a novel machine unlearning framework founded upon the established principles of the min-max optimization paradigm. We capitalize on the capabilities of strong Membersh…
Link Prediction on Heterophilic Graphs via Disentangled Representation Learning
Shijie Zhou, Zhimeng Guo, Charu Aggarwal +2
Link prediction is an important task that has wide applications in various domains. However, the majority of existing link prediction approaches assume the given graph follows homo…
Label-Wise Graph Convolutional Network for Heterophilic Graphs
Enyan Dai, Shijie Zhou, Zhimeng Guo +1
Graph Neural Networks (GNNs) have achieved remarkable performance in modeling graphs for various applications. However, most existing GNNs assume the graphs exhibit strong homophil…