Search Results for author:
Found 29 papers, 24 papers with code
Structural Deep Metric Learning for Room Layout Estimation
We employ a metric model and a layout encoder to map the RGB images and the ground-truth layouts to the embedding space, respectively, and a layout decoder to map the embeddings to the corresponding layouts, where the whole framework is trained in an end-to-end manner.
GenAD: Generative End-to-End Autonomous Driving
We then employ a variational autoencoder to learn the future trajectory distribution in a structural latent space for trajectory prior modeling.
Path Choice Matters for Clear Attribution in Path Methods
Rigorousness and clarity are both essential for interpretations of DNNs to engender human trust.
OccWorld: Learning a 3D Occupancy World Model for Autonomous Driving
In this paper, we explore a new framework of learning a world model, OccWorld, in the 3D Occupancy space to simultaneously predict the movement of the ego car and the evolution of the surrounding scenes.
SelfOcc: Self-Supervised Vision-Based 3D Occupancy Prediction
Our SelfOcc outperforms the previous best method SceneRF by 58. 7% using a single frame as input on SemanticKITTI and is the first self-supervised work that produces reasonable 3D occupancy for surround cameras on nuScenes.
LiDAR-HMR: 3D Human Mesh Recovery from LiDAR
In recent years, point cloud perception tasks have been garnering increasing attention.
Ranked #1 on
3D Human Pose Estimation
on SLOPER4D
Exploring Unified Perspective For Fast Shapley Value Estimation
Shapley values have emerged as a widely accepted and trustworthy tool, grounded in theoretical axioms, for addressing challenges posed by black-box models like deep neural networks.
Introspective Deep Metric Learning
This paper proposes an introspective deep metric learning (IDML) framework for uncertainty-aware comparisons of images.
PointOcc: Cylindrical Tri-Perspective View for Point-based 3D Semantic Occupancy Prediction
To address this, we propose a cylindrical tri-perspective view to represent point clouds effectively and comprehensively and a PointOcc model to process them efficiently.
Human-M3: A Multi-view Multi-modal Dataset for 3D Human Pose Estimation in Outdoor Scenes
In this article, we propose Human-M3, an outdoor multi-modal multi-view multi-person human pose database which includes not only multi-view RGB videos of outdoor scenes but also corresponding pointclouds.
SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving
Towards a more comprehensive perception of a 3D scene, in this paper, we propose a SurroundOcc method to predict the 3D occupancy with multi-camera images.
Tri-Perspective View for Vision-Based 3D Semantic Occupancy Prediction
To lift image features to the 3D TPV space, we further propose a transformer-based TPV encoder (TPVFormer) to obtain the TPV features effectively.
Ranked #1 on
Prediction Of Occupancy Grid Maps
on nuScenes
Deep Factorized Metric Learning
Learning a generalizable and comprehensive similarity metric to depict the semantic discrepancies between images is the foundation of many computer vision tasks.
Bort: Towards Explainable Neural Networks with Bounded Orthogonal Constraint
Deep learning has revolutionized human society, yet the black-box nature of deep neural networks hinders further application to reliability-demanded industries.
Probabilistic Deep Metric Learning for Hyperspectral Image Classification
We propose to learn a global probabilistic distribution for each pixel in the patch and a probabilistic metric to model the distance between distributions.
Token-Label Alignment for Vision Transformers
Data mixing strategies (e. g., CutMix) have shown the ability to greatly improve the performance of convolutional neural networks (CNNs).
OPERA: Omni-Supervised Representation Learning with Hierarchical Supervisions
The pretrain-finetune paradigm in modern computer vision facilitates the success of self-supervised learning, which tends to achieve better transferability than supervised learning.
A Simple Baseline for Multi-Camera 3D Object Detection
First, we extract multi-scale features and generate the perspective object proposals on each monocular image.
BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Driving
Specifically, BEVerse first performs shared feature extraction and lifting to generate 4D BEV representations from multi-timestamp and multi-view images.
Ranked #15 on
Robust Camera Only 3D Object Detection
on nuScenes-C
Introspective Deep Metric Learning for Image Retrieval
This paper proposes an introspective deep metric learning (IDML) framework for uncertainty-aware comparisons of images.
SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimation
In this paper, we propose a SurroundDepth method to incorporate the information from multiple surrounding views to predict depth maps across cameras.
Attributable Visual Similarity Learning
This paper proposes an attributable visual similarity learning (AVSL) framework for a more accurate and explainable similarity measure between images.
Ranked #3 on
Metric Learning
on CARS196
(using extra training data)
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
Dimension Embeddings for Monocular 3D Object Detection
In this paper, we propose a general method to learn appropriate embeddings for dimension estimation in monocular 3D object detection.
Deep Relational Metric Learning
This paper presents a deep relational metric learning (DRML) framework for image clustering and retrieval.
Deep Compositional Metric Learning
In this paper, we propose a deep compositional metric learning (DCML) framework for effective and generalizable similarity measurement between images.
Deep Metric Learning via Adaptive Learnable Assessment
In this paper, we propose a deep metric learning via adaptive learnable assessment (DML-ALA) method for image retrieval and clustering, which aims to learn a sample assessment strategy to maximize the generalization of the trained metric.
Hardness-Aware Deep Metric Learning
This paper presents a hardness-aware deep metric learning (HDML) framework.
Ranked #30 on
Metric Learning
on CUB-200-2011
(using extra training data)
Deep Adversarial Metric Learning
Learning an effective distance metric between image pairs plays an important role in visual analysis, where the training procedure largely relies on hard negative samples.
