Search Results for author:
Found 11 papers, 9 papers with code
ControlCap: Controllable Region-level Captioning
The multimodal model is constrained to generate captions within a few sub-spaces containing the control words, which increases the opportunity of hitting less frequent captions, alleviating the caption degeneration issue.
Ranked #1 on
Dense Captioning
on Visual Genome
VMamba: Visual State Space Model
Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have long been the predominant backbone networks for visual representation learning.
Continual Learning for Image Segmentation with Dynamic Query
Image segmentation based on continual learning exhibits a critical drop of performance, mainly due to catastrophic forgetting and background shift, as they are required to incorporate new classes continually.
PrivImage: Differentially Private Synthetic Image Generation using Diffusion Models with Semantic-Aware Pretraining
Then, this function assists in querying the semantic distribution of the sensitive dataset, facilitating the selection of data from the public dataset with analogous semantics for pre-training.
DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Models
To showcase the power of the proposed approach, we generate datasets with rich dense pixel-wise labels for a wide range of downstream tasks, including semantic segmentation, instance segmentation, and depth estimation.
Generative Prompt Model for Weakly Supervised Object Localization
During training, GenPromp converts image category labels to learnable prompt embeddings which are fed to a generative model to conditionally recover the input image with noise and learn representative embeddings.
Ranked #1 on
Weakly-Supervised Object Localization
on CUB-200-2011
(Top-1 Localization Accuracy metric, using extra
training data)
A Large Cross-Modal Video Retrieval Dataset with Reading Comprehension
Most existing cross-modal language-to-video retrieval (VR) research focuses on single-modal input from video, i. e., visual representation, while the text is omnipresent in human environments and frequently critical to understand video.
FlowText: Synthesizing Realistic Scene Text Video with Optical Flow Estimation
This paper introduces a novel video text synthesis technique called FlowText, which utilizes optical flow estimation to synthesize a large amount of text video data at a low cost for training robust video text spotters.
ICDAR 2023 Video Text Reading Competition for Dense and Small Text
In this competition report, we establish a video text reading benchmark, DSText, which focuses on dense and small text reading challenges in the video with various scenarios.
DiffuMask: Synthesizing Images with Pixel-level Annotations for Semantic Segmentation Using Diffusion Models
In contrast, synthetic data can be freely available using a generative model (e. g., DALL-E, Stable Diffusion).
Explore Faster Localization Learning For Scene Text Detection
Generally pre-training and long-time training computation are necessary for obtaining a good-performance text detector based on deep networks.
