5 citations · 14 across the 10 of their papers we have counts for
12 papers
Food4All: An Agentic Framework and Benchmark for Food Resource Navigation with Adaptive User Understanding
Yiyang Li, Weixiang Sun, Tianyi Ma +3
Food assistance referral requires conversational agents to translate underspecified, often noisy help-seeking dialogues into locally valid resource recommendations. We present Food…
LLMs4All: A Review of Large Language Models Across Academic Disciplines
Yanfang Ye, Zheyuan Zhang, Tianyi Ma +26
Cutting-edge Artificial Intelligence (AI) techniques keep reshaping our view of the world. For example, Large Language Models (LLMs) based applications such as ChatGPT have shown t…
SAMed-2: Selective Memory Enhanced Medical Segment Anything Model
Zhiling Yan, Sifan Song, Dingjie Song +11
Recent "segment anything" efforts show promise by learning from large-scale data, but adapting such models directly to medical images remains challenging due to the complexity of m…
IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain
Hong Huang, Weixiang Sun, Zhijian Wu +4
Recently, the rapid advancements of vision-language models, such as CLIP, leads to significant progress in zero-/few-shot anomaly detection (ZFSAD) tasks. However, most existing CL…
EfficientLLM: Efficiency in Large Language Models
Zhengqing Yuan, Weixiang Sun, Yixin Liu +13
Large Language Models (LLMs) have driven significant progress, yet their growing parameter counts and context windows incur prohibitive compute, energy, and monetary costs. We intr…
Social Science Meets LLMs: How Reliable Are Large Language Models in Social Simulations?
Yue Huang, Zhengqing Yuan, Yujun Zhou +8
Large Language Models (LLMs) are increasingly employed for simulations, enabling applications in role-playing agents and Computational Social Science (CSS). However, the reliabilit…
TTT-Unet: Enhancing U-Net with Test-Time Training Layers for Biomedical Image Segmentation
Rong Zhou, Zhengqing Yuan, Zhiling Yan +7
Biomedical image segmentation is crucial for accurately diagnosing and analyzing various diseases. However, Convolutional Neural Networks (CNNs) and Transformers, the most commonly…
AdvLogo: Adversarial Patch Attack against Object Detectors based on Diffusion Models
Boming Miao, Chunxiao Li, Yao Zhu +4
With the rapid development of deep learning, object detectors have demonstrated impressive performance; however, vulnerabilities still exist in certain scenarios. Current research…
Biomedical SAM 2: Segment Anything in Biomedical Images and Videos
Zhiling Yan, Weixiang Sun, Rong Zhou +8
Medical image segmentation and video object segmentation are essential for diagnosing and analyzing diseases by identifying and measuring biological structures. Recent advances in…
Bora: Biomedical Generalist Video Generation Model
Weixiang Sun, Xiaocao You, Ruizhe Zheng +5
Generative models hold promise for revolutionizing medical education, robot-assisted surgery, and data augmentation for medical AI development. Diffusion models can now generate re…
Mora: Enabling Generalist Video Generation via A Multi-Agent Framework
Zhengqing Yuan, Yixin Liu, Yihan Cao +10
Text-to-video generation has made significant strides, but replicating the capabilities of advanced systems like OpenAI Sora remains challenging due to their closed-source nature.…
Medical Unlearnable Examples: Securing Medical Data from Unauthorized Training via Sparsity-Aware Local Masking
Weixiang Sun, Yixin Liu, Zhiling Yan +2
The rapid expansion of AI in healthcare has led to a surge in medical data generation and storage, boosting medical AI development. However, fears of unauthorized use, like trainin…