Search Results for author:
Found 25 papers, 16 papers with code
Improving Text-guided Object Inpainting with Semantic Pre-inpainting
In this paper, we propose to decompose the typical single-stage object inpainting into two cascaded processes: 1) semantic pre-inpainting that infers the semantic features of desired objects in a multi-modal feature space; 2) high-fieldity object generation in diffusion latent space that pivots on such inpainted semantic features.
FreeEnhance: Tuning-Free Image Enhancement via Content-Consistent Noising-and-Denoising Process
Technically, FreeEnhance is a two-stage process that firstly adds random noise to the input image and then capitalizes on a pre-trained image diffusion model (i. e., Latent Diffusion Models) to denoise and enhance the image details.
Hi3D: Pursuing High-Resolution Image-to-3D Generation with Video Diffusion Models
Despite having tremendous progress in image-to-3D generation, existing methods still struggle to produce multi-view consistent images with high-resolution textures in detail, especially in the paradigm of 2D diffusion that lacks 3D awareness.
DreamMesh: Jointly Manipulating and Texturing Triangle Meshes for Text-to-3D Generation
In the fine stage, DreamMesh jointly manipulates the mesh and refines the texture map, leading to high-quality triangle meshes with high-fidelity textured materials.
CWT-Net: Super-resolution of Histopathology Images Using a Cross-scale Wavelet-based Transformer
Super-resolution (SR) aims to enhance the quality of low-resolution images and has been widely applied in medical imaging.
Decoder Pre-Training with only Text for Scene Text Recognition
Extensive experiments across various STR decoders and language recognition tasks underscore the broad applicability and remarkable performance of DPTR, providing a novel insight for STR pre-training.
AdvQDet: Detecting Query-Based Adversarial Attacks with Adversarial Contrastive Prompt Tuning
During this process, the queries made to the target model are intermediate adversarial examples crafted at the previous attack step, which share high similarities in the pixel space.
Out of Length Text Recognition with Sub-String Matching
In this paper, we term this task Out of Length (OOL) text recognition.
V3Det Challenge 2024 on Vast Vocabulary and Open Vocabulary Object Detection: Methods and Results
2) Open Vocabulary Object Detection: This track goes a step further, requiring algorithms to detect objects from an open set of categories, including unknown objects.
Learning to Rank Patches for Unbiased Image Redundancy Reduction
The results demonstrate that LTRP outperforms both supervised and other self-supervised methods due to the fair assessment of image content.
Instruction-Guided Scene Text Recognition
We propose a novel instruction-guided scene text recognition (IGTR) paradigm that formulates STR as an instruction learning problem and understands text images by predicting character attributes, e. g., character frequency, position, etc.
3DStyle-Diffusion: Pursuing Fine-grained Text-driven 3D Stylization with 2D Diffusion Models
In this work, we propose a new 3DStyle-Diffusion model that triggers fine-grained stylization of 3D meshes with additional controllable appearance and geometric guidance from 2D Diffusion models.
Context Perception Parallel Decoder for Scene Text Recognition
We first present an empirical study of AR decoding in STR, and discover that the AR decoder not only models linguistic context, but also provides guidance on visual context perception.
Ranked #1 on
Scene Text Recognition
on CUTE80
(using extra training data)
LRANet: Towards Accurate and Efficient Scene Text Detection with Low-Rank Approximation Network
Next, we propose a dual assignment scheme for speed acceleration.
MRN: Multiplexed Routing Network for Incremental Multilingual Text Recognition
In this paper, we propose the Incremental MLTR (IMLTR) task in the context of incremental learning (IL), where different languages are introduced in batches.
Ranked #1 on
Incremental Learning
on MLT17
TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition
In this work, we introduce TPS++, an attention-enhanced TPS transformation that incorporates the attention mechanism to text rectification for the first time.
Ranked #1 on
Scene Text Recognition
on SVT-P
Bi-Directional Feature Fusion Generative Adversarial Network for Ultra-High Resolution Pathological Image Virtual Re-Staining
In order to eliminate the square effect, we design a bi-directional feature fusion generative adversarial network (BFF-GAN) with a global branch and a local branch.
Resolving Task Confusion in Dynamic Expansion Architectures for Class Incremental Learning
The dynamic expansion architecture is becoming popular in class incremental learning, mainly due to its advantages in alleviating catastrophic forgetting.
Prototypical Residual Networks for Anomaly Detection and Localization
Anomaly detection and localization are widely used in industrial manufacturing for its efficiency and effectiveness.
Ranked #4 on
Supervised Anomaly Detection
on MVTec AD
(using extra training data)
SVTR: Scene Text Recognition with a Single Visual Model
Dominant scene text recognition models commonly contain two building blocks, a visual model for feature extraction and a sequence model for text transcription.
Ranked #16 on
Scene Text Recognition
on ICDAR2013
Self-distillation Augmented Masked Autoencoders for Histopathological Image Classification
In this paper, we introduce MAE and verify the effect of visible patches for histopathological image understanding.
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 #12 on
Scene Text Recognition
on ICDAR2015
ACE-Net: Biomedical Image Segmentation with Augmented Contracting and Expansive Paths
Nowadays U-net-like FCNs predominate various biomedical image segmentation applications and attain promising performance, largely due to their elegant architectures, e. g., symmetric contracting and expansive paths as well as lateral skip-connections.
NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition
Considering scene image has large variation in text and background, we further design a modality-transform block to effectively transform 2D input images to 1D sequences, combined with the encoder to extract more discriminative features.
Binarized Mode Seeking for Scalable Visual Pattern Discovery
Second, we further extend bMS to a more general form, namely contrastive binary mean shift (cbMS), which maximizes the contrastive density in binary space, for finding informative patterns that are both frequent and discriminative for the dataset.
