Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileAcademic lineage
Possible advisorsa guess from early papers, not confirmed
- Suggested from co-authorship
Is this you? Claim this profile to confirm or dismiss it.
Works68 from public data
- PointConv: Deep Convolutional Networks on 3D Point Clouds1,931
The dynamic filter is extended to a new convolution operation, named PointConv, which can be applied on point clouds to build deep convolutional networks and is able to achieve state-of-the-art on challenging semantic segmentation benchmarks on 3D point clouds.
- 923
- MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing902
MasaCtrl converts existing self-attention in diffusion models into mutual self-attention, so that it can query correlated local contents and textures from source images for consistency, and proposes a mask-guided mutual self-attention strategy.
- PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding410
PhotoMaker is introduced, an efficient personalized text-to-image generation method, which mainly encodes an arbitrary number of input ID images into a stack ID embed-ding for preserving ID information, and an ID-oriented data construction pipeline to assemble the training data.
- LayoutDiffusion: Controllable Diffusion Model for Layout-to-Image Generation369
A diffusion model named LayoutDiffusion is proposed that can obtain higher generation quality and greater controllability than the previous works and is proposed to construct a structural image patch with region information and transform the patched image into a special layout to fuse with the normal layout in a unified form.
- Deep Air Learning: Interpolation, Prediction, and Feature Analysis of Fine-Grained Air Quality274
The proposed approach utilizes the information pertaining to the unlabeled spatio-temporal data to improve the performance of the interpolation and the prediction, and performs feature selection and association analysis to reveal the main relevant features to the variation of the air quality.
- Open-book Video Captioning with Retrieve-Copy-Generate Network118
A novel Retrieve-Copy-Generate network is proposed, where a pluggable video-to-text retriever is constructed to retrieve sentences as hints from the training corpus effectively, and a copy-mechanism generator is introduced to extract expressions from multi-retrieved sentences dynamically.
- RecDCL: Dual Contrastive Learning for Recommendation82
This work investigates how to employ both batch-wise CL (BCL) and feature-wise CL (FCL) for recommendation and proposes a dual contrastive learning recommendation framework---RecDCL, which can consistently outperform the state-of theart GNNs-based and SSL-based models.
- StyleAdapter: A Unified Stylized Image Generation Model52
The StyleAdapter is proposed, a unified stylized image generation model capable of producing a variety of stylized images that match both the content of a given prompt and the style of reference images, without the need for per-style fine-tuning.
- PosterLLaVa: Constructing a Unified Multi-modal Layout Generator with LLM46
This research introduces a unified framework for automated graphic layout generation, leveraging the multi-modal large language model (MLLM) to accommodate diverse design tasks and develops an automated text-to-poster system that generates editable SVG posters based on users' design intentions, bridging the gap between layout generation and real-world graphic design applications.
- Semantic-Guided Relation Propagation Network for Few-shot Action Recognition44
A novel semantic-guided relation propagation network (SRPN), which leverages semantic information together with visual information for few-shot action recognition and outperforms several state-of-the-art methods with a significant margin.
- 38
- 37
- 35
- CustomNet: Zero-shot Object Customization with Variable-Viewpoints in Text-to-Image Diffusion Models34
The CustomNet is introduced, a novel object customization approach that explicitly incorporates 3D novel view synthesis capabilities into the object customization process, and facilitates zero-shot object customization without test-time optimization, offering simultaneous control over the viewpoints, location, and background.
- Embedding deep networks into visual explanations31
The explanation module works by embedding a high-dimensional deep network layer nonlinearly into a low-dimensional explanation space while retaining faithfulness, so that the original deep learning predictions can be constructed from the few concepts extracted by the explanation module.
- Multi-Task Medical Concept Normalization Using Multi-View Convolutional Neural Network30
A general framework which introduces a tensor generator and a novel multi-view convolutional neural network with multi-task shared structure to tackle the two tasks simultaneously to address non-standard expressions and short-text problem.
- 29
- SphereDiffusion: Spherical Geometry-Aware Distortion Resilient Diffusion Model26
A novel framework of SphereDiffusion is introduced to address the unique challenges of spherical distortion and geometry characteristics, for better generating high-quality and precisely controllable spherical panoramic images.
- Do We Really Need Temporal Convolutions in Action Segmentation?24
This paper presents a pure Transformer-based model without temporal convolutions in action segmentation, called Temporal U-Transformer, and proposes a boundary-aware loss based on the distribution of similarity scores between frames from attention modules to improve the ability to recognize boundaries.
- 24
- Mining partially annotated images24
The Semi-Supervised Correspondence Hierarchical Dirichlet Process (SSCHDP) is proposed, which lies in exploiting the information pertaining to partially annotated images or even unannotated images to achieve semi-supervised learning under the HDP structure.
- 21
- 19
- VideoMaker: Zero-shot Customized Video Generation with the Inherent Force of Video Diffusion Models18
A novel framework that leverages VDM's inherent force to enable high-quality zero-shot customized video generation and devise an innovative bidirectional interaction between subject features and generated content through spatial self-attention within VDM, ensuring that VDM has better subject fidelity while maintaining the diversity of the generated video.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.