Search Results for author:
Found 28 papers, 18 papers with code
Test-Time Degradation Adaption for Open-Set Image Restoration
In contrast to close-set scenarios that restore images from a predefined set of degradations, open-set image restoration aims to handle the unknown degradations that were unforeseen during the pretraining phase, which is less-touched as far as we know.
Variational Relational Point Completion Network for Robust 3D Classification
Existing point cloud completion methods tend to generate global shape skeletons and hence lack fine local details.
ABLE-NeRF: Attention-Based Rendering with Learnable Embeddings for Neural Radiance Field
Our method, which we call ABLE-NeRF, significantly reduces `blurry' glossy surfaces in rendering and produces realistic translucent surfaces which lack in prior art.
Comprehensive and Delicate: An Efficient Transformer for Image Restoration
Vision Transformers have shown promising performance in image restoration, which usually conduct window- or channel-based attention to avoid intensive computations.
IntegratedPIFu: Integrated Pixel Aligned Implicit Function for Single-view Human Reconstruction
We propose IntegratedPIFu, a new pixel aligned implicit model that builds on the foundation set by PIFuHD.
Exploring Point-BEV Fusion for 3D Point Cloud Object Tracking with Transformer
In a point cloud sequence, 3D object tracking aims to predict the location and orientation of an object in consecutive frames given an object template.
PTTR: Relational 3D Point Cloud Object Tracking with Transformer
In a point cloud sequence, 3D object tracking aims to predict the location and orientation of an object in the current search point cloud given a template point cloud.
Playing for 3D Human Recovery
Specifically, we contribute GTA-Human, a large-scale 3D human dataset generated with the GTA-V game engine, featuring a highly diverse set of subjects, actions, and scenarios.
CSG-Stump: A Learning Friendly CSG-Like Representation for Interpretable Shape Parsing
Generating an interpretable and compact representation of 3D shapes from point clouds is an important and challenging problem.
Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency
In addition, existing 3D domain adaptive detection methods often assume prior access to the target domain annotations, which is rarely feasible in the real world.
Delving Deep into the Generalization of Vision Transformers under Distribution Shifts
By comprehensively investigating these GE-ViTs and comparing with their corresponding CNN models, we observe: 1) For the enhanced model, larger ViTs still benefit more for the OOD generalization.
Unsupervised 3D Shape Completion through GAN Inversion
In contrast to previous fully supervised approaches, in this paper we present ShapeInversion, which introduces Generative Adversarial Network (GAN) inversion to shape completion for the first time.
Variational Relational Point Completion Network
In particular, we propose a dual-path architecture to enable principled probabilistic modeling across partial and complete clouds.
Ranked #2 on
Point Cloud Completion
on Completion3D
HAVANA: Hierarchical and Variation-Normalized Autoencoder for Person Re-identification
Person Re-Identification (Re-ID) is of great importance to the many video surveillance systems.
Towards Overcoming False Positives in Visual Relationship Detection
We observe that during training, the relationship proposal distribution is highly imbalanced: most of the negative relationship proposals are easy to identify, e. g., the inaccurate object detection, which leads to the under-fitting of low-frequency difficult proposals.
REFINE: Prediction Fusion Network for Panoptic Segmentation
Panoptic segmentation aims at generating pixel-wise class and instance predictions for each pixel in the input image, which is a challenging task and far more complicated than naively fusing the semantic and instance segmentation results.
Ranked #11 on
Panoptic Segmentation
on COCO test-dev
BiPointNet: Binary Neural Network for Point Clouds
To alleviate the resource constraint for real-time point cloud applications that run on edge devices, in this paper we present BiPointNet, the first model binarization approach for efficient deep learning on point clouds.
Balanced Activation for Long-tailed Visual Recognition
Deep classifiers have achieved great success in visual recognition.
Leveraging Localization for Multi-camera Association
We present McAssoc, a deep learning approach to the as-sociation of detection bounding boxes in different views ofa multi-camera system.
MessyTable: Instance Association in Multiple Camera Views
We present an interesting and challenging dataset that features a large number of scenes with messy tables captured from multiple camera views.
Balanced Meta-Softmax for Long-Tailed Visual Recognition
In our experiments, we demonstrate that Balanced Meta-Softmax outperforms state-of-the-art long-tailed classification solutions on both visual recognition and instance segmentation tasks.
Ranked #7 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
Leveraging Temporal Information for 3D Detection and Domain Adaptation
Ever since the prevalent use of the LiDARs in autonomous driving, tremendous improvements have been made to the learning on the point clouds.
Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction
In this paper, we present STAR, a Spatio-Temporal grAph tRansformer framework, which tackles trajectory prediction by only attention mechanisms.
Efficient Attention: Attention with Linear Complexities
Dot-product attention has wide applications in computer vision and natural language processing.
Ranked #2 on
Extractive Text Summarization
on GovReport
FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification
Our proposed FD-GAN achieves state-of-the-art performance on three person reID datasets, which demonstrates that the effectiveness and robust feature distilling capability of the proposed FD-GAN.
Ranked #3 on
Person Re-Identification
on CUHK03
HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis
Pedestrian analysis plays a vital role in intelligent video surveillance and is a key component for security-centric computer vision systems.
Ranked #2 on
Pedestrian Attribute Recognition
on RAP
Hierarchical Deep Recurrent Architecture for Video Understanding
The proposed framework contains hierarchical deep architecture, including the frame-level sequence modeling part and the video-level classification part.
Spindle Net: Person Re-Identification With Human Body Region Guided Feature Decomposition and Fusion
Person re-identification (ReID) is an important task in video surveillance and has various applications.
