Search Results for author:
Found 12 papers, 8 papers with code
Enhancing Monotonic Modeling with Spatio-Temporal Adaptive Awareness in Diverse Marketing
OFOS platforms offer dynamic allocation incentives to users and merchants through diverse marketing campaigns to encourage payments while maintaining the platforms' budget efficiency.
SF-V: Single Forward Video Generation Model
Diffusion-based video generation models have demonstrated remarkable success in obtaining high-fidelity videos through the iterative denoising process.
AVID: Any-Length Video Inpainting with Diffusion Model
Given a video, a masked region at its initial frame, and an editing prompt, it requires a model to do infilling at each frame following the editing guidance while keeping the out-of-mask region intact.
DeFormer: Integrating Transformers with Deformable Models for 3D Shape Abstraction from a Single Image
Accurate 3D shape abstraction from a single 2D image is a long-standing problem in computer vision and graphics.
Taming Self-Training for Open-Vocabulary Object Detection
This work identifies two challenges of using self-training in OVD: noisy PLs from VLMs and frequent distribution changes of PLs.
Topology-Preserving Automatic Labeling of Coronary Arteries via Anatomy-aware Connection Classifier
Automatic labeling of coronary arteries is an essential task in the practical diagnosis process of cardiovascular diseases.
Improving Tuning-Free Real Image Editing with Proximal Guidance
Null-text inversion (NTI) optimizes null embeddings to align the reconstruction and inversion trajectories with larger CFG scales, enabling real image editing with cross-attention control.
OmniLabel: A Challenging Benchmark for Language-Based Object Detection
With more than 28K unique object descriptions on over 25K images, OmniLabel provides a challenging benchmark with diverse and complex object descriptions in a naturally open-vocabulary setting.
SINE: SINgle Image Editing with Text-to-Image Diffusion Models
We propose a novel model-based guidance built upon the classifier-free guidance so that the knowledge from the model trained on a single image can be distilled into the pre-trained diffusion model, enabling content creation even with one given image.
Exploiting Unlabeled Data with Vision and Language Models for Object Detection
We propose a novel method that leverages the rich semantics available in recent vision and language models to localize and classify objects in unlabeled images, effectively generating pseudo labels for object detection.
Ranked #19 on
Open Vocabulary Object Detection
on MSCOCO
(using extra training data)
Global Matching with Overlapping Attention for Optical Flow Estimation
In this paper, inspired by the traditional matching-optimization methods where matching is introduced to handle large displacements before energy-based optimizations, we introduce a simple but effective global matching step before the direct regression and develop a learning-based matching-optimization framework, namely GMFlowNet.
Ranked #4 on
Optical Flow Estimation
on KITTI 2015
Enriching Medcial Terminology Knowledge Bases via Pre-trained Language Model and Graph Convolutional Network
Afterwards, both semantic and structure embeddings are combined to measure the relevancy between the terminology and the entity.
