33 Result(s)
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Article
Adversarial defence by learning differentiated feature representation in deep ensemble
Deep learning models have been shown to be vulnerable to critical attacks under adversarial conditions. Attackers are able to generate powerful adversarial examples by searching for adversarial perturbations, ...
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Chapter and Conference Paper
Isolation and Integration: A Strong Pre-trained Model-Based Paradigm for Class-Incremental Learning
Continual learning aims to effectively learn from streaming data, adapting to emerging new classes without forgetting old ones. Conventional models without pre-training are constructed from the ground up, suff...
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Chapter and Conference Paper
Explore and Enhance the Generalization of Anomaly DeepFake Detection
In recent years, Anomaly DeepFake Detection (ADFD) has made significant breakthroughs in terms of generalization when meeting various unknown tampers. These detection methods primarily enhance generalization b...
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Chapter and Conference Paper
Self-supervised Contrastive Feature Refinement for Few-Shot Class-Incremental Learning
Few-Shot Class-Incremental Learning (FSCIL) is to learn novel classes with few data points incrementally, without forgetting old classes. It is very hard to capture the underlying patterns and traits of the fe...
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Article
Low-dose CT image restoration based on noise prior regression network
Low-dose CT image (LDCT) restoration is a challenging task attracting the interest of researchers extensively. However, reducing the radiation dose may lead to increased noise and artifacts. Over the past year...
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Chapter and Conference Paper
Towards Interactive Facial Image Inpainting by Text or Exemplar Image
Facial image inpainting aims to fill visually realistic and semantically new pixels for masked or missing pixels in a face image. Although current methods have made progress in achieving high visual quality, t...
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Chapter and Conference Paper
Latent Partition Implicit with Surface Codes for 3D Representation
Deep implicit functions have shown remarkable shape modeling ability in various 3D computer vision tasks. One drawback is that it is hard for them to represent a 3D shape as multiple parts. Current solutions l...
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Chapter and Conference Paper
Optimal Transport for Label-Efficient Visible-Infrared Person Re-Identification
Visible-infrared person re-identification (VI-ReID) has been a key enabler for night intelligent monitoring system. However, the extensive laboring efforts significantly limit its applications. In this paper, ...
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Chapter and Conference Paper
Mutually Reinforcing Structure with Proposal Contrastive Consistency for Few-Shot Object Detection
Few-shot object detection is based on the base set with abundant labeled samples to detect novel categories with scarce samples. The majority of former solutions are mainly based on meta-learning or transfer-l...
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Chapter and Conference Paper
Optimization over Disentangled Encoding: Unsupervised Cross-Domain Point Cloud Completion via Occlusion Factor Manipulation
Recently, studies considering domain gaps in shape completion attracted more attention, due to the undesirable performance of supervised methods on real scans. They only noticed the gap in input scans, but ign...
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Chapter and Conference Paper
Tensor-Based Multi-index Representation Learning for Major Depression Disorder Detection with Resting-State fMRI
Major depressive disorder (MDD) is a common and costly mental illness whose pathophysiology is difficult to clarify. Resting-state functional MRI (rs-fMRI) provides a non-invasive solution for the study of fun...
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Chapter and Conference Paper
Gaussian Vector: An Efficient Solution for Facial Landmark Detection
Significant progress has been made in facial landmark detection with the development of Convolutional Neural Networks. The widely-used algorithms can be classified into coordinate regression methods and heatma...
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Article
Detecting outliers in industrial systems using a hybrid ensemble scheme
The massive growth of process data in industrial systems has promoted the development of data-driven techniques, while the presence of outliers in process data always deteriorates the effectiveness. This paper...
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Chapter and Conference Paper
Experimental Study on Improvement of Sign Language Motion Classification Performance Using Pre-trained Network Models
Sign language is a major means of communication for people with hearing disabilities. However, there are very few hearing people who have learned sign language, and this is a great barrier to communication bet...
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Chapter and Conference Paper
SeqXY2SeqZ: Structure Learning for 3D Shapes by Sequentially Predicting 1D Occupancy Segments from 2D Coordinates
Structure learning for 3D shapes is vital for 3D computer vision. State-of-the-art methods show promising results by representing shapes using implicit functions in 3D that are learned using discriminative neu...
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Article
Stylistic scene enhancement GAN: mixed stylistic enhancement generation for 3D indoor scenes
In this paper, we present stylistic scene enhancement GAN, SSE-GAN, a conditional Wasserstein GAN-based approach to automatic generation of mixed stylistic enhancements for 3D indoor scenes. An enhancement ind...
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Article
Complete 3D Scene Parsing from an RGBD Image
One major goal of vision is to infer physical models of objects, surfaces, and their layout from sensors. In this paper, we aim to interpret indoor scenes from one RGBD image. Our representation encodes the la...
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Chapter and Conference Paper
A Visual-Inertial Information Fusion Method for SLAM Front-End Odometry
In a pure visual odometry, a pose transformation matrix between adjacent two frames is estimated by an algorithm based on pixel variation between images. However, pure monocular visual odometers cannot obtain ...
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Chapter
Deep Learning for 3D Data Processing
Extracting local features from raw 3D data is a nontrivial and challenging task that requires carefully designed 3D shape descriptors. In conventional methods, these descriptors are handcrafted and require int...
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Article
Semantic 3D indoor scene enhancement using guide words
We propose a novel framework for semantically enhancing a 3D indoor scene in agreement with a user-provided guide word. To do so, we make changes to furniture colors and place small objects in the scene. The r...
