19 citations · 38 across the 56 of their papers we have counts for
26 papers · 1 filter
EverybodyDance: Bipartite Graph-Based Identity Correspondence for Multi-Character Animation
Haotian Ling, Zequn Chen, Qiuying Chen +6
Consistent pose-driven character animation has achieved remarkable progress in single-character scenarios. However, extending these advances to multi-character settings is non-triv…
EFDiT: Efficient Fine-grained Image Generation Using Diffusion Transformer Models
Kun Wang, Donglin Di, Tonghua Su +1
Diffusion models are highly regarded for their controllability and the diversity of images they generate. However, class-conditional generation methods based on diffusion models of…
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…
Global-Local Aware Scene Text Editing
Fuxiang Yang, Tonghua Su, Donglin Di +4
Scene Text Editing (STE) involves replacing text in a scene image with new target text while preserving both the original text style and background texture. Existing methods suffer…
GAOT: Generating Articulated Objects Through Text-Guided Diffusion Models
Hao Sun, Lei Fan, Donglin Di +1
Articulated object generation has seen increasing advancements, yet existing models often lack the ability to be conditioned on text prompts. To address the significant gap between…
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…
MoCA: Identity-Preserving Text-to-Video Generation via Mixture of Cross Attention
Qi Xie, Yongjia Ma, Donglin Di +2
Achieving ID-preserving text-to-video (T2V) generation remains challenging despite recent advances in diffusion-based models. Existing approaches often fail to capture fine-grained…
MoSA: Motion-Coherent Human Video Generation via Structure-Appearance Decoupling
Haoyu Wang, Hao Tang, Donglin Di +5
Existing video generation models predominantly emphasize appearance fidelity while exhibiting limited ability to synthesize complex human motions, such as whole-body movements, lon…
PhysGM: Large Physical Gaussian Model for Feed-Forward 4D Synthesis
Chunji Lv, Zequn Chen, Donglin Di +5
Despite advances in physics-based 3D motion synthesis, current methods face key limitations: reliance on pre-reconstructed 3D Gaussian Splatting (3DGS) built from dense multi-view…
DiTalker: A Unified DiT-based Framework for High-Quality and Speaking Styles Controllable Portrait Animation
He Feng, Yongjia Ma, Donglin Di +3
Portrait animation aims to synthesize talking videos from a static reference face, conditioned on audio and style frame cues (e.g., emotion and head poses), while ensuring precise…
QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation
Jiahui Yang, Yongjia Ma, Donglin Di +6
Existing text-to-image models often rely on parameter fine-tuning techniques such as Low-Rank Adaptation (LoRA) to customize visual attributes. However, when combining multiple LoR…
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…
ChronoTailor: Harnessing Attention Guidance for Fine-Grained Video Virtual Try-On
Jinjuan Wang, Wenzhang Sun, Ming Li +7
Video virtual try-on aims to seamlessly replace the clothing of a person in a source video with a target garment. Despite significant progress in this field, existing approaches st…
On Weak-to-Strong Generalization and f-Divergence
Wei Yao, Gengze Xu, Huayi Tang +4
Weak-to-strong generalization (W2SG) has emerged as a promising paradigm for stimulating the capabilities of strong pre-trained models by leveraging supervision from weaker supervi…
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…
Cluster-Driven Expert Pruning for Mixture-of-Experts Large Language Models
Hongcheng Guo, Juntao Yao, Boyang Wang +5
Mixture-of-Experts (MoE) architectures have emerged as a promising paradigm for scaling large language models (LLMs) with sparse activation of task-specific experts. Despite their…
Adams Bashforth Moulton Solver for Inversion and Editing in Rectified Flow
Yongjia Ma, Donglin Di, Xuan Liu +4
Rectified flow models have achieved remarkable performance in image and video generation tasks. However, existing numerical solvers face a trade-off between fast sampling and high…
A Self-supervised Motion Representation for Portrait Video Generation
Qiyuan Zhang, Chenyu Wu, Wenzhang Sun +4
Recent advancements in portrait video generation have been noteworthy. However, existing methods rely heavily on human priors and pre-trained generative models, Motion representati…
UniCP: A Unified Caching and Pruning Framework for Efficient Video Generation
Wenzhang Sun, Qirui Hou, Donglin Di +3
Diffusion Transformers (DiT) excel in video generation but encounter significant computational challenges due to the quadratic complexity of attention. Notably, attention differenc…