Search Results for author:
Found 14 papers, 11 papers with code
3DTopia: Large Text-to-3D Generation Model with Hybrid Diffusion Priors
Specifically, it is powered by a text-conditioned tri-plane latent diffusion model, which quickly generates coarse 3D samples for fast prototyping.
Large-Vocabulary 3D Diffusion Model with Transformer
To this end, we propose a novel triplane-based 3D-aware Diffusion model with TransFormer, DiffTF, for handling challenges via three aspects.
Towards Real-World Visual Tracking with Temporal Contexts
To handle those problems, we propose a two-level framework (TCTrack) that can exploit temporal contexts efficiently.
Tracker Meets Night: A Transformer Enhancer for UAV Tracking
To realize reliable UAV tracking at night, a spatial-channel Transformer-based low-light enhancer (namely SCT), which is trained in a novel task-inspired manner, is proposed and plugged prior to tracking approaches.
Local Perception-Aware Transformer for Aerial Tracking
Specifically, with local-modeling to global-search mechanism, the proposed tracker replaces the global encoder by a novel local-recognition encoder.
Siamese Object Tracking for Unmanned Aerial Vehicle: A Review and Comprehensive Analysis
Unmanned aerial vehicle (UAV)-based visual object tracking has enabled a wide range of applications and attracted increasing attention in the field of intelligent transportation systems because of its versatility and effectiveness.
Egocentric Prediction of Action Target in 3D
We are interested in anticipating as early as possible the target location of a person's object manipulation action in a 3D workspace from egocentric vision.
Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV Tracking
Therefore, to help increase awareness of the potential risk and the robustness of UAV tracking, this work proposes a novel adaptive adversarial attack approach, i. e., Ad$^2$Attack, against UAV object tracking.
TCTrack: Temporal Contexts for Aerial Tracking
Temporal contexts among consecutive frames are far from being fully utilized in existing visual trackers.
NoisyMix: Boosting Model Robustness to Common Corruptions
For many real-world applications, obtaining stable and robust statistical performance is more important than simply achieving state-of-the-art predictive test accuracy, and thus robustness of neural networks is an increasingly important topic.
HiFT: Hierarchical Feature Transformer for Aerial Tracking
Most existing Siamese-based tracking methods execute the classification and regression of the target object based on the similarity maps.
DarkLighter: Light Up the Darkness for UAV Tracking
Recent years have witnessed the fast evolution and promising performance of the convolutional neural network (CNN)-based trackers, which aim at imitating biological visual systems.
SiamAPN++: Siamese Attentional Aggregation Network for Real-Time UAV Tracking
By virtue of the attention mechanism, we conduct a special attentional aggregation network (AAN) consisting of self-AAN and cross-AAN for raising the representation ability of features eventually.
Siamese Anchor Proposal Network for High-Speed Aerial Tracking
In the domain of visual tracking, most deep learning-based trackers highlight the accuracy but casting aside efficiency.
