Search Results for author:
Found 16 papers, 10 papers with code
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.
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.
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.
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
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.
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 #15 on
Scene Text Recognition
on ICDAR2013
Prototypical Residual Networks for Anomaly Detection and Localization
Anomaly detection and localization are widely used in industrial manufacturing for its efficiency and effectiveness.
Ranked #2 on
Supervised Anomaly Detection
on MVTec AD
(using extra training data)
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.
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.
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
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
LRANet: Towards Accurate and Efficient Scene Text Detection with Low-Rank Approximation Network
Next, we propose a dual assignment scheme for speed acceleration.
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)
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.
Instruction-Guided Scene Text Recognition
Multi-modal models have shown appealing performance in visual tasks recently, as instruction-guided training has evoked the ability to understand fine-grained visual content.
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.
