Search Results for author:
Found 23 papers, 9 papers with code
Knowledge Distillation for Road Detection based on cross-model Semi-Supervised Learning
The proposed semi-supervised learning-based knowledge distillation (SSLKD) approach demonstrates a notable improvement in the performance of the student model, in the application of road segmentation, surpassing the effectiveness of traditional semi-supervised learning methods.
DiverseNet: Decision Diversified Semi-supervised Semantic Segmentation Networks for Remote Sensing Imagery
There is still a lack of lightweight and efficient perturbation methods to promote the diversity of features and the precision of pseudo labels during training.
SAMVG: A Multi-stage Image Vectorization Model with the Segment-Anything Model
Vector graphics are widely used in graphical designs and have received more and more attention.
Confidence-Guided Semi-supervised Learning in Land Cover Classification
Especially in the application of land cover classification, pixel-level manual labelling in large-scale imagery is labour-intensive, time-consuming and expensive.
Towards Artistic Image Aesthetics Assessment: a Large-scale Dataset and a New Method
To fill the gap in the field of artistic image aesthetics assessment (AIAA), we first introduce a large-scale AIAA dataset: Boldbrush Artistic Image Dataset (BAID), which consists of 60, 337 artistic images covering various art forms, with more than 360, 000 votes from online users.
Provably Uncertainty-Guided Universal Domain Adaptation
It fully utilizes the relationship between a target sample and its neighbors in the source domain to avoid the influence of domain misalignment.
Exploiting Inter-Sample Affinity for Knowability-Aware Universal Domain Adaptation
Specifically, we introduce a knowability-based labeling scheme which can be divided into two steps: 1) Knowability-guided detection of known and unknown samples based on the intrinsic structure of the neighborhoods of samples, where we leverage the first singular vectors of the affinity matrices to obtain the knowability of every target sample.
Quality Metric Guided Portrait Line Drawing Generation from Unpaired Training Data
In this paper, we propose a novel method to automatically transform face photos to portrait drawings using unpaired training data with two new features; i. e., our method can (1) learn to generate high quality portrait drawings in multiple styles using a single network and (2) generate portrait drawings in a "new style" unseen in the training data.
Large-Capacity Image Steganography Based on Invertible Neural Networks
Many attempts have been made to hide information in images, where the main challenge is how to increase the payload capacity without the container image being detected as containing a message.
Mesh Saliency: An Independent Perceptual Measure or a Derivative of Image Saliency?
While mesh saliency aims to predict regional importance of 3D surfaces in agreement with human visual perception and is well researched in computer vision and graphics, latest work with eye-tracking experiments shows that state-of-the-art mesh saliency methods remain poor at predicting human fixations.
NPRportrait 1.0: A Three-Level Benchmark for Non-Photorealistic Rendering of Portraits
Despite the recent upsurge of activity in image-based non-photorealistic rendering (NPR), and in particular portrait image stylisation, due to the advent of neural style transfer, the state of performance evaluation in this field is limited, especially compared to the norms in the computer vision and machine learning communities.
Image-based Portrait Engraving
This paper describes a simple image-based method that applies engraving stylisation to portraits using ordered dithering.
Shape retrieval of non-rigid 3d human models
In addition, further participants have also taken part, and we provide extra analysis of the retrieval results.
LaplacianNet: Learning on 3D Meshes with Laplacian Encoding and Pooling
3D models are commonly used in computer vision and graphics.
Scoot: A Perceptual Metric for Facial Sketches
In this paper, we design a perceptual metric, called Structure Co-Occurrence Texture (Scoot), which simultaneously considers the block-level spatial structure and co-occurrence texture statistics.
Simultaneous Subspace Clustering and Cluster Number Estimating based on Triplet Relationship
In this paper we propose a unified framework to simultaneously discover the number of clusters and group the data points into them using subspace clustering.
Weakly Supervised Coupled Networks for Visual Sentiment Analysis
The second branch utilizes both the holistic and localized information by coupling the sentiment map with deep features for robust classification.
Clinical Skin Lesion Diagnosis Using Representations Inspired by Dermatologist Criteria
Accordingly, we design six medical representations considering different criteria for the recognition of skin lesions, and construct a diagnosis system for clinical skin disease images.
Face Sketch Synthesis Style Similarity:A New Structure Co-occurrence Texture Measure
However, human perception of the similarity of two sketches will consider both structure and texture as essential factors and is not sensitive to slight ("pixel-level") mismatches.
Pose2Seg: Detection Free Human Instance Segmentation
We demonstrate that our pose-based framework can achieve better accuracy than the state-of-art detection-based approach on the human instance segmentation problem, and can moreover better handle occlusion.
Ranked #1 on
Human Instance Segmentation
on OCHuman
Automatic Semantic Style Transfer using Deep Convolutional Neural Networks and Soft Masks
This paper presents an automatic image synthesis method to transfer the style of an example image to a content image.
FLIC: Fast Linear Iterative Clustering with Active Search
It also has drawbacks on convergence rate as a result of both the fixed search region and separately doing the assignment step and the update step.
Detecting Violent and Abnormal Crowd activity using Temporal Analysis of Grey Level Co-occurrence Matrix (GLCM) Based Texture Measures
We utilise computer vision techniques to develop an automated method of abnormal crowd detection that can aid a human operator in the detection of violent behaviour.
