1 citations · 2 across the 13 of their papers we have counts for
13 papers
Visual Prompt-Agnostic Evolution
Junze Wang, Lei Fan, Dezheng Zhang +5
Visual Prompt Tuning (VPT) adapts a frozen Vision Transformer (ViT) to downstream tasks by inserting a small number of learnable prompt tokens into the token sequence at each layer…
Cross-Stain Contrastive Learning for Paired Immunohistochemistry and Histopathology Slide Representation Learning
Yizhi Zhang, Lei Fan, Zhulin Tao +4
Universal, transferable whole-slide image (WSI) representations are central to computational pathology. Incorporating multiple markers (e.g., immunohistochemistry, IHC) alongside H…
ADNet: A Large-Scale and Extensible Multi-Domain Benchmark for Anomaly Detection Across 380 Real-World Categories
Hai Ling, Jia Guo, Zhulin Tao +6
Anomaly detection (AD) aims to identify defects using normal-only training data. Existing anomaly detection benchmarks (e.g., MVTec-AD with 15 categories) cover only a narrow range…
Dinomaly2: A Unified Framework for Unsupervised Image Anomaly Detection
Jia Guo, Shuai Lu, Lei Fan +9
Unsupervised anomaly detection (UAD) has evolved from specialized single-class models to unified multi-class models, yet existing multi-class models significantly underperform the…
SAGE: A Visual Language Model for Anomaly Detection via Fact Enhancement and Entropy-aware Alignment
Guoxin Zang, Xue Li, Donglin Di +4
While Vision-Language Models (VLMs) have shown promising progress in general multimodal tasks, they often struggle in industrial anomaly detection and reasoning, particularly in de…
LLaPa: A Vision-Language Model Framework for Counterfactual-Aware Procedural Planning
Shibo Sun, Xue Li, Donglin Di +6
While large language models (LLMs) have advanced procedural planning for embodied AI systems through strong reasoning abilities, the integration of multimodal inputs and counterfac…
Spatially Gene Expression Prediction using Dual-Scale Contrastive Learning
Mingcheng Qu, Yuncong Wu, Donglin Di +4
Spatial transcriptomics (ST) provides crucial insights into tissue micro-environments, but is limited to its high cost and complexity. As an alternative, predicting gene expression…
Memory-Augmented Incomplete Multimodal Survival Prediction via Cross-Slide and Gene-Attentive Hypergraph Learning
Mingcheng Qu, Guang Yang, Donglin Di +4
Multimodal pathology-genomic analysis is critical for cancer survival prediction. However, existing approaches predominantly integrate formalin-fixed paraffin-embedded (FFPE) slide…
Hypergraph Tversky-Aware Domain Incremental Learning for Brain Tumor Segmentation with Missing Modalities
Junze Wang, Lei Fan, Weipeng Jing +4
Existing methods for multimodal MRI segmentation with missing modalities typically assume that all MRI modalities are available during training. However, in clinical practice, some…
Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance
Mingcheng Qu, Guang Yang, Donglin Di +4
Multimodal pathology-genomic analysis has become increasingly prominent in cancer survival prediction. However, existing studies mainly utilize multi-instance learning to aggregate…
Boundary-Guided Learning for Gene Expression Prediction in Spatial Transcriptomics
Mingcheng Qu, Yuncong Wu, Donglin Di +4
Spatial transcriptomics (ST) has emerged as an advanced technology that provides spatial context to gene expression. Recently, deep learning-based methods have shown the capability…
MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects
Lei Fan, Dongdong Fan, Zhiguang Hu +5
We present MANTA, a visual-text anomaly detection dataset for tiny objects. The visual component comprises over 137.3K images across 38 object categories spanning five typical doma…
Salvaging the Overlooked: Leveraging Class-Aware Contrastive Learning for Multi-Class Anomaly Detection
Lei Fan, Junjie Huang, Donglin Di +4
For anomaly detection (AD), early approaches often train separate models for individual classes, yielding high performance but posing challenges in scalability and resource managem…