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Found 27 papers, 6 papers with code
Fragile Model Watermark for integrity protection: leveraging boundary volatility and sensitive sample-pairing
Neural networks have increasingly influenced people's lives.
Brush Your Text: Synthesize Any Scene Text on Images via Diffusion Model
To tackle this problem, we propose Diff-Text, which is a training-free scene text generation framework for any language.
Efficient Object Detection in Optical Remote Sensing Imagery via Attention-based Feature Distillation
To address the aforementioned challenges, we propose Attention-based Feature Distillation (AFD), a new KD approach that distills both local and global information from the teacher detector.
DSAM-GN:Graph Network based on Dynamic Similarity Adjacency Matrices for Vehicle Re-identification
Finally, the nodes and similarity adjacency matrices are fed into graph networks to extract more discriminative features for vehicle Re-ID.
Zero-Shot Object Goal Visual Navigation With Class-Independent Relationship Network
This method combines target detection information with the relative semantic similarity between the target and the navigation target, and constructs a brand new state representation based on similarity ranking, this state representation does not include target feature or environment feature, effectively decoupling the agent's navigation ability from target features.
Distance Weighted Trans Network for Image Completion
The challenge of image generation has been effectively modeled as a problem of structure priors or transformation.
Adaptive White-Box Watermarking with Self-Mutual Check Parameters in Deep Neural Networks
Fragile watermarking is a technique used to identify tampering in AI models.
Weakly Supervised Scene Text Generation for Low-resource Languages
A large number of annotated training images is crucial for training successful scene text recognition models.
A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipation
At present, it is the largest semi-supervised VAD dataset with the largest number of scenes and classes of anomalies, the longest duration, and the only one considering the scene-dependent anomaly.
Scene Text Recognition with Image-Text Matching-guided Dictionary
Inspired by ITC, the SITM network combines the visual features and the text features of all candidates to identify the candidate with the minimum distance in the feature space.
VGTS: Visually Guided Text Spotting for Novel Categories in Historical Manuscripts
In the field of historical manuscript research, scholars frequently encounter novel symbols in ancient texts, investing considerable effort in their identification and documentation.
Co-Occurrence Matters: Learning Action Relation for Temporal Action Localization
Existing works in this field mainly suffer from two weaknesses: (1) They often neglect the multi-label case and only focus on temporal modeling.
DGFont++: Robust Deformable Generative Networks for Unsupervised Font Generation
Moreover, we introduce contrastive self-supervised learning to learn a robust style representation for fonts by understanding the similarity and dissimilarities of fonts.
Context Recovery and Knowledge Retrieval: A Novel Two-Stream Framework for Video Anomaly Detection
For the context recovery stream, we propose a spatiotemporal U-Net which can fully utilize the motion information to predict the future frame.
Ranked #1 on
Anomaly Detection
on Corridor
UC-OWOD: Unknown-Classified Open World Object Detection
In this work, we propose a novel OWOD problem called Unknown-Classified Open World Object Detection (UC-OWOD).
SGBANet: Semantic GAN and Balanced Attention Network for Arbitrarily Oriented Scene Text Recognition
For target images (scene text images), the Semantic Generator Module generates simple semantic features that share the same feature distribution with support images (clear text images).
A Deep Learning Approach for Predicting Two-dimensional Soil Consolidation Using Physics-Informed Neural Networks (PINN)
To address the above problem, in this paper, we propose a deep learning method using physics-informed neural networks (PINN) to predict the excess pore water pressure of two-dimensional soil consolidation.
Learnable Locality-Sensitive Hashing for Video Anomaly Detection
In this paper, we propose a novel distance-based VAD method to take advantage of all the available normal data efficiently and flexibly.
Meta-OLE: Meta-learned Orthogonal Low-Rank Embedding
We introduce Meta-OLE, a new geometry-regularized method for fast adaptation to novel tasks in few-shot image classification.
DG-Font: Deformable Generative Networks for Unsupervised Font Generation
Font generation is a challenging problem especially for some writing systems that consist of a large number of characters and has attracted a lot of attention in recent years.
Efficient Spatialtemporal Context Modeling for Action Recognition
Inspired from 2D criss-cross attention used in segmentation task, we propose a recurrent 3D criss-cross attention (RCCA-3D) module to model the dense long-range spatiotemporal contextual information in video for action recognition.
Automatic Historical Feature Generation through Tree-based Method in Ads Prediction
Historical features are important in ads click-through rate (CTR) prediction, because they account for past engagements between users and ads.
Reveal of Domain Effect: How Visual Restoration Contributes to Object Detection in Aquatic Scenes
According to our analysis, five key discoveries are reported: 1) Domain quality has an ignorable effect on within-domain convolutional representation and detection accuracy; 2) low-quality domain leads to higher generalization ability in cross-domain detection; 3) low-quality domain can hardly be well learned in a domain-mixed learning process; 4) degrading recall efficiency, restoration cannot improve within-domain detection accuracy; 5) visual restoration is beneficial to detection in the wild by reducing the domain shift between training data and real-world scenes.
Bilinear Constraint based ADMM for Mixed Poisson-Gaussian Noise Removal
In this paper, we propose new operator-splitting algorithms for the total variation regularized infimal convolution (TV-IC) model [4] in order to remove mixed Poisson-Gaussian(MPG) noise.
FACLSTM: ConvLSTM with Focused Attention for Scene Text Recognition
Scene text recognition has recently been widely treated as a sequence-to-sequence prediction problem, where traditional fully-connected-LSTM (FC-LSTM) has played a critical role.
Channel Attention and Multi-level Features Fusion for Single Image Super-Resolution
Convolutional neural networks (CNNs) have demonstrated superior performance in super-resolution (SR).
Handwritten digit string recognition by combination of residual network and RNN-CTC
Recurrent neural network (RNN) and connectionist temporal classification (CTC) have showed successes in many sequence labeling tasks with the strong ability of dealing with the problems where the alignment between the inputs and the target labels is unknown.
