1,114 Result(s)
within
Chapter
Conference Paper
**li Zhang
Computer Graphics
Image Processing and Computer Vision
Pattern Recognition
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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
ROSA-Net: Rotation-Robust Structure-Aware Network for Fine-Grained 3D Shape Retrieval
Fine-grained 3D shape retrieval aims to retrieve 3D shapes similar to a query shape in a repository with models belonging to the same class, which requires shape descriptors to represent detailed geometric inf...
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Chapter and Conference Paper
Denoised Dual-Level Contrastive Network for Weakly-Supervised Temporal Sentence Grounding
The task of temporal sentence grounding aims to localize the target moment corresponding to a given natural language query. Due to the large burden of labeling the temporal boundaries, weakly-supervised method...
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Chapter and Conference Paper
Silhouette-Based 6D Object Pose Estimation
For a long time, deep learning-based 6D object pose estimation networks have lacked the ability to address the problem of pose estimation of the unknown objects beyond the training datasets, due to the closed-...
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Chapter and Conference Paper
A U-Shaped Spatio-Temporal Transformer as Solver for Motion Capture
Motion capture (MoCap) suffers from inevitable noises. The raw markers can be mislabeled, occluded, or contain positional noise, which must be refined before being used for production. However, the clean-up of...
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Chapter and Conference Paper
Zero-Shot Real Facial Attribute Separation and Transfer at Novel Views
Real-time and zero-shot attribute separation of a given real-face image, allowing attribute transfer and rendering at novel views without the aid of multi-view information, has been demonstrated to be benefici...
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Chapter and Conference Paper
FASSET: Frame Supersampling and Extrapolation Using Implicit Neural Representations of Rendering Contents
Despite recent advances in ray tracing hardwares, ray budgets are still limited for many rendering applications, especially when global illumination is enabled. This typically results in undersampling, which m...
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Chapter and Conference Paper
Walking Telescope: Exploring the Zooming Effect in Expanding Detection Threshold Range for Translation Gain
Redirected Walking (RDW) is a locomotion technique utilized in virtual reality. It involves manipulating the displayed scene to redirect the user without their awareness, causing them to adjust their position ...
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Chapter and Conference Paper
Point Cloud Segmentation with Guided Sampling and Continuous Interpolation
Sampling and interpolation are pivotal in the design of 3D neural networks. Presently, farthest point sampling and \(k\) ...
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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
Face Expression Recognition via Product-Cross Dual Attention and Neutral-Aware Anchor Loss
Face expression recognition is an important task whose aim is to classify a face image to a kind of expression such as happy, sad, or surprise, etc. This task is challenging due to the ambiguities in expressio...
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Chapter and Conference Paper
Single-Video Temporal Consistency Enhancement with Rolling Guidance
Image/video synthesis has been extensively studied in academics, and computer-generated videos are becoming increasingly popular among the general public. However, ensuring the temporal consistency of generate...
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Chapter and Conference Paper
Neural Radiance Fields for Dynamic View Synthesis Using Local Temporal Priors
Neural Radiance Fields (NeRF) have demonstrated promising results in synthesizing novel view images from a set of unconstrained captured scenes. One important extension of NeRF is using it on non-rigid reconst...
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Chapter and Conference Paper
SARNet: Semantic Augmented Registration of Large-Scale Urban Point Clouds
Registering urban point clouds is a pretty challenging task due to the large-scale, noise and data incompleteness of LiDAR scanning data. In this paper, we propose SARNet, a novel semantic augmented registration ...
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Chapter and Conference Paper
Deep Tiny Network for Recognition-Oriented Face Image Quality Assessment
Face recognition has made significant progress in recent years due to deep convolutional neural networks (CNN). In many face recognition (FR) scenarios, face images are acquired from a sequence with huge intr...
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Chapter and Conference Paper
The Sixth Visual Object Tracking VOT2018 Challenge Results
The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers...
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Chapter and Conference Paper
Group LSTM: Group Trajectory Prediction in Crowded Scenarios
The analysis of crowded scenes is one of the most challenging scenarios in visual surveillance, and a variety of factors need to be taken into account, such as the structure of the environments, and the presen...
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Chapter and Conference Paper
Occlusion Resistant Object Rotation Regression from Point Cloud Segments
Rotation estimation of known rigid objects is important for robotic applications such as dexterous manipulation. Most existing methods for rotation estimation use intermediate representations such as templates...
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Chapter and Conference Paper
Variational Wasserstein Clustering
We propose a new clustering method based on optimal transportation. We discuss the connection between optimal transportation and k-means clustering, solve optimal transportation with the variational principle,...
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Chapter and Conference Paper
Deep Bilinear Learning for RGB-D Action Recognition
In this paper, we focus on exploring modality-temporal mutual information for RGB-D action recognition. In order to learn time-varying information and multi-modal features jointly, we propose a novel deep bili...
