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Found 19 papers, 10 papers with code
Metamorpheus: Interactive, Affective, and Creative Dream Narration Through Metaphorical Visual Storytelling
It also remains unknown how technologies such as generative AI models can facilitate the meaning making process, and ultimately support affective mindfulness.
Unsupervised Learning Method for the Wave Equation Based on Finite Difference Residual Constraints Loss
The wave equation is an important physical partial differential equation, and in recent years, deep learning has shown promise in accelerating or replacing traditional numerical methods for solving it.
Hierarchical Contrastive Learning for Pattern-Generalizable Image Corruption Detection
Detecting corrupted regions by learning the contrastive distinctions rather than the semantic patterns of corruptions, our model has well generalization ability across different corruption patterns.
TRM-UAP: Enhancing the Transferability of Data-Free Universal Adversarial Perturbation via Truncated Ratio Maximization
Aiming at crafting a single universal adversarial perturbation (UAP) to fool CNN models for various data samples, universal attack enables a more efficient and accurate evaluation for the robustness of CNN models.
Efficient Visual Computing with Camera RAW Snapshots
Conventional cameras capture image irradiance on a sensor and convert it to RGB images using an image signal processor (ISP).
Learning Generalizable Latent Representations for Novel Degradations in Super Resolution
The real-world degradations can be beyond the simulation scope by the handcrafted degradations, which are referred to as novel degradations.
Learning Sequence Representations by Non-local Recurrent Neural Memory
The key challenge of sequence representation learning is to capture the long-range temporal dependencies.
Global-Local Stepwise Generative Network for Ultra High-Resolution Image Restoration
While the research on image background restoration from regular size of degraded images has achieved remarkable progress, restoring ultra high-resolution (e. g., 4K) images remains an extremely challenging task due to the explosion of computational complexity and memory usage, as well as the deficiency of annotated data.
ViT-P: Rethinking Data-efficient Vision Transformers from Locality
We make vision transformers as data-efficient as convolutional neural networks by introducing multi-focal attention bias.
U2-Former: A Nested U-shaped Transformer for Image Restoration
While Transformer has achieved remarkable performance in various high-level vision tasks, it is still challenging to exploit the full potential of Transformer in image restoration.
Ranked #16 on
Image Dehazing
on SOTS Outdoor
Deep Domain Adaptation for Pavement Crack Detection
In this paper, we propose a Deep Domain Adaptation-based Crack Detection Network (DDACDN), which learns domain invariant features by taking advantage of the source domain knowledge to predict the multi-category crack location information in the target domain, where only image-level labels are available.
Generative Memory-Guided Semantic Reasoning Model for Image Inpainting
Most existing methods for image inpainting focus on learning the intra-image priors from the known regions of the current input image to infer the content of the corrupted regions in the same image.
Deep-Masking Generative Network: A Unified Framework for Background Restoration from Superimposed Images
In this work we present the Deep-Masking Generative Network (DMGN), which is a unified framework for background restoration from the superimposed images and is able to cope with different types of noise.
Unsupervised Shadow Removal Using Target Consistency Generative Adversarial Network
Unsupervised shadow removal aims to learn a non-linear function to map the original image from shadow domain to non-shadow domain in the absence of paired shadow and non-shadow data.
Application of Word2vec in Phoneme Recognition
At the same time, in order to solve the problem of overfitting in the 61 phoneme recognition model on TIMIT dataset, we propose a new training method.
FORECAST-CLSTM: A New Convolutional LSTM Network for Cloudage Nowcasting
With the highly demand of large-scale and real-time weather service for public, a refinement of short-time cloudage prediction has become an essential part of the weather forecast productions.
TrackNet: Simultaneous Object Detection and Tracking and Its Application in Traffic Video Analysis
We propose a novel network structure named trackNet that can directly detect a 3D tube enclosing a moving object in a video segment by extending the faster R-CNN framework.
A context-based geoprocessing framework for optimizing meetup location of multiple moving objects along road networks
Given different types of constraints on human life, people must make decisions that satisfy social activity needs.
Local Contrast Learning
We focus on one-shot classification by deep learning approach based on a small quantity of training samples.
