Search Results for author:
Found 25 papers, 12 papers with code
Sentinel-Guided Zero-Shot Learning: A Collaborative Paradigm without Real Data Exposure
With increasing concerns over data privacy and model copyrights, especially in the context of collaborations between AI service providers and data owners, an innovative SG-ZSL paradigm is proposed in this work.
ConRF: Zero-shot Stylization of 3D Scenes with Conditioned Radiation Fields
Most of the existing works on arbitrary 3D NeRF style transfer required retraining on each single style condition.
CTNeRF: Cross-Time Transformer for Dynamic Neural Radiance Field from Monocular Video
The goal of our work is to generate high-quality novel views from monocular videos of complex and dynamic scenes.
Dual Feature Augmentation Network for Generalized Zero-shot Learning
To address these issues, we propose a novel Dual Feature Augmentation Network (DFAN), which comprises two feature augmentation modules, one for visual features and the other for semantic features.
DS-Depth: Dynamic and Static Depth Estimation via a Fusion Cost Volume
Nevertheless, the dynamic cost volume inevitably generates extra occlusions and noise, thus we alleviate this by designing a fusion module that makes static and dynamic cost volumes compensate for each other.
Graph-based Representation for Image based on Granular-ball
Current image processing methods usually operate on the finest-granularity unit; that is, the pixel, which leads to challenges in terms of efficiency, robustness, and understandability in deep learning models.
On Isotropy, Contextualization and Learning Dynamics of Contrastive-based Sentence Representation Learning
In this paper, we aim to help guide future designs of sentence representation learning methods by taking a closer look at contrastive SRL through the lens of isotropy, contextualization and learning dynamics.
Evolutionary Generalized Zero-Shot Learning
In this work, we propose a novel Evolutionary Generalized Zero-Shot Learning setting, which (i) avoids the domain shift problem in inductive GZSL, and (ii) is more in line with the needs of real-world deployments than transductive GZSL.
Knowing the Past to Predict the Future: Reinforcement Virtual Learning
Reinforcement Learning (RL)-based control system has received considerable attention in recent decades.
Action Quality Assessment with Temporal Parsing Transformer
Action Quality Assessment(AQA) is important for action understanding and resolving the task poses unique challenges due to subtle visual differences.
Absolute Zero-Shot Learning
Considering the increasing concerns about data copyright and privacy issues, we present a novel Absolute Zero-Shot Learning (AZSL) paradigm, i. e., training a classifier with zero real data.
Aerial Scene Parsing: From Tile-level Scene Classification to Pixel-wise Semantic Labeling
Finally, we perform ASP by unifying the tile-level scene classification and object-based image analysis to achieve pixel-wise semantic labeling.
Semi-Supervised Crowd Counting from Unlabeled Data
Our method achieved competitive performance in semi-supervised learning approaches on these crowd counting datasets.
EfficientTDNN: Efficient Architecture Search for Speaker Recognition
Compared with hand-designed approaches, neural architecture search (NAS) appears as a practical technique in automating the manual architecture design process and has attracted increasing interest in spoken language processing tasks such as speaker recognition.
Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models
Deep generative models are a class of techniques that train deep neural networks to model the distribution of training samples.
Query Twice: Dual Mixture Attention Meta Learning for Video Summarization
Video summarization aims to select representative frames to retain high-level information, which is usually solved by predicting the segment-wise importance score via a softmax function.
On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances and Million-AID
After reviewing existing benchmark datasets in the research community of RS image interpretation, this article discusses the problem of how to efficiently prepare a suitable benchmark dataset for RS image interpretation.
Order Matters: Shuffling Sequence Generation for Video Prediction
Predicting future frames in natural video sequences is a new challenge that is receiving increasing attention in the computer vision community.
Towards Reliable, Automated General Movement Assessment for Perinatal Stroke Screening in Infants Using Wearable Accelerometers
We demonstrate the effectiveness of our approach in a study with 34 newborns (21 typically developing infants and 13 PS infants with abnormal movements).
Learning RoI Transformer for Detecting Oriented Objects in Aerial Images
Especially when detecting densely packed objects in aerial images, methods relying on horizontal proposals for common object detection often introduce mismatches between the Region of Interests (RoIs) and objects.
Ranked #48 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
Robust Cross-View Gait Recognition with Evidence: A Discriminant Gait GAN (DiGGAN) Approach
Gait as a biometric trait has attracted much attention in many security and privacy applications such as identity recognition and authentication, during the last few decades.
ActionXPose: A Novel 2D Multi-view Pose-based Algorithm for Real-time Human Action Recognition
We present ActionXPose, a novel 2D pose-based algorithm for posture-level Human Action Recognition (HAR).
Towards Universal Representation for Unseen Action Recognition
Unseen Action Recognition (UAR) aims to recognise novel action categories without training examples.
Ranked #14 on
Action Recognition
on ActivityNet
From Zero-shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis
Using the proposed Unseen Visual Data Synthesis (UVDS) algorithm, semantic attributes are effectively utilised as an intermediate clue to synthesise unseen visual features at the training stage.
