Search Results for author:
Found 9 papers, 6 papers with code
DEADiff: An Efficient Stylization Diffusion Model with Disentangled Representations
The Q-Formers are trained using paired images rather than the identical target, in which the reference image and the ground-truth image are with the same style or semantics.
DreamIdentity: Improved Editability for Efficient Face-identity Preserved Image Generation
While large-scale pre-trained text-to-image models can synthesize diverse and high-quality human-centric images, an intractable problem is how to preserve the face identity for conditioned face images.
Design Booster: A Text-Guided Diffusion Model for Image Translation with Spatial Layout Preservation
Specifically, our method co-encodes images and text into a new domain during the training phase.
Crossing the Gap: Domain Generalization for Image Captioning
In this paper, we introduce a new setting called Domain Generalization for Image Captioning (DGIC), where the data from the target domain is unseen in the learning process.
ABINet++: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Spotting
In this paper, we argue that the limited capacity of language models comes from 1) implicit language modeling; 2) unidirectional feature representation; and 3) language model with noise input.
Ranked #4 on
Text Spotting
on SCUT-CTW1500
CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition
In this paper, we propose a novel module called Multi-Domain Character Distance Perception (MDCDP) to establish a visually and semantically related position embedding.
Ranked #11 on
Scene Text Recognition
on ICDAR2015
From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network
Such operation guides the vision model to use not only the visual texture of characters, but also the linguistic information in visual context for recognition when the visual cues are confused (e. g. occlusion, noise, etc.).
PERT: A Progressively Region-based Network for Scene Text Removal
However, there exists two problems: 1) the implicit erasure guidance causes the excessive erasure to non-text areas; 2) the one-stage erasure lacks the exhaustive removal of text region.
Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition
Additionally, based on the ensemble of iterative predictions, we propose a self-training method which can learn from unlabeled images effectively.
