Search Results for author:
Found 79 papers, 35 papers with code
MTKD: Multi-Teacher Knowledge Distillation for Image Super-Resolution
Knowledge distillation (KD) has emerged as a promising technique in deep learning, typically employed to enhance a compact student network through learning from their high-performance but more complex teacher variant.
NYC-Indoor-VPR: A Long-Term Indoor Visual Place Recognition Dataset with Semi-Automatic Annotation
Visual Place Recognition (VPR) in indoor environments is beneficial to humans and robots for better localization and navigation.
LUWA Dataset: Learning Lithic Use-Wear Analysis on Microscopic Images
Most importantly, the LUWA dataset provides an underexplored opportunity for vision and learning communities and complements existing image classification problems on common objects.
MASSTAR: A Multi-Modal and Large-Scale Scene Dataset with a Versatile Toolchain for Surface Prediction and Completion
To bridge this research gap, we propose MASSTAR: a Multi-modal lArge-scale Scene dataset with a verSatile Toolchain for surfAce pRediction and completion.
LAFS: Landmark-based Facial Self-supervised Learning for Face Recognition
This enables our method - namely LAndmark-based Facial Self-supervised learning LAFS), to learn key representation that is more critical for face recognition.
ActFormer: Scalable Collaborative Perception via Active Queries
This proactive understanding of the visual features' relevance does not require the transmission of the features themselves, enhancing both communication and computation efficiency.
Learning When to See for Long-term Traffic Data Collection on Power-constrained Devices
We introduce a novel learning-based framework that strategically decides observation timings for battery-powered devices and reconstructs the full data stream from sparsely sampled observations, resulting in minimal performance loss and a significantly prolonged system lifetime.
Networked Multiagent Reinforcement Learning for Peer-to-Peer Energy Trading
Utilizing distributed renewable and energy storage resources in local distribution networks via peer-to-peer (P2P) energy trading has long been touted as a solution to improve energy systems' resilience and sustainability.
Full-reference Video Quality Assessment for User Generated Content Transcoding
In this work, we observe that existing full-/no-reference quality metrics fail to accurately predict the perceptual quality difference between transcoded UGC content and the corresponding unpristine references.
BVI-Artefact: An Artefact Detection Benchmark Dataset for Streamed Videos
Professionally generated content (PGC) streamed online can contain visual artefacts that degrade the quality of user experience.
RankDVQA-mini: Knowledge Distillation-Driven Deep Video Quality Assessment
Deep learning-based video quality assessment (deep VQA) has demonstrated significant potential in surpassing conventional metrics, with promising improvements in terms of correlation with human perception.
SO-NeRF: Active View Planning for NeRF using Surrogate Objectives
Despite the great success of Neural Radiance Fields (NeRF), its data-gathering process remains vague with only a general rule of thumb of sampling as densely as possible.
Scene Summarization: Clustering Scene Videos into Spatially Diverse Frames
It aims to summarize a long video walkthrough of a scene into a small set of frames that are spatially diverse in the scene, which has many impotant applications, such as in surveillance, real estate, and robotics.
LiDAR-based 4D Occupancy Completion and Forecasting
In this paper, we introduce a novel LiDAR perception task of Occupancy Completion and Forecasting (OCF) in the context of autonomous driving to unify these aspects into a cohesive framework.
Distillation Improves Visual Place Recognition for Low-Quality Queries
The shift to online computing for real-time visual localization often requires streaming query images/videos to a server for visual place recognition (VPR), where fast video transmission may result in reduced resolution or increased quantization.
AutoEncoding Tree for City Generation and Applications
Based on this dataset, we propose AETree, a tree-structured auto-encoder neural network, for city generation.
Self-Supervised Representation Learning with Cross-Context Learning between Global and Hypercolumn Features
Inspired by cross-modality learning, we extend this existing framework that only learns from global features by encouraging the global features and intermediate layer features to learn from each other.
MacFormer: Map-Agent Coupled Transformer for Real-time and Robust Trajectory Prediction
Predicting the future behavior of agents is a fundamental task in autonomous vehicle domains.
Concavity-Induced Distance for Unoriented Point Cloud Decomposition
We propose Concavity-induced Distance (CID) as a novel way to measure the dissimilarity between a pair of points in an unoriented point cloud.
SSCBench: Monocular 3D Semantic Scene Completion Benchmark in Street Views
Monocular scene understanding is a foundational component of autonomous systems.
3D Semantic Scene Completion
3D Semantic Scene Completion from a single 2D image
Collaborative Multi-Object Tracking with Conformal Uncertainty Propagation
MOT-CUP demonstrates the importance of uncertainty quantification in both COD and MOT, and provides the first attempt to improve the accuracy and reduce the uncertainty in MOT based on COD through uncertainty propagation.
MaskCon: Masked Contrastive Learning for Coarse-Labelled Dataset
More specifically, within the contrastive learning framework, for each sample our method generates soft-labels with the aid of coarse labels against other samples and another augmented view of the sample in question.
Ranked #1 on
Learning with coarse labels
on cifar100
Among Us: Adversarially Robust Collaborative Perception by Consensus
This leads to our hypothesize-and-verify framework: perception results with and without collaboration from a random subset of teammates are compared until reaching a consensus.
VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completion
To enable such capability in AI systems, we propose VoxFormer, a Transformer-based semantic scene completion framework that can output complete 3D volumetric semantics from only 2D images.
3D Semantic Scene Completion from a single RGB image
Depth Estimation
Committed Private Information Retrieval
A PIR scheme is $v$-verifiable if the client can verify the correctness of the retrieved $x_i$ even when $v \leq k$ servers collude and try to fool the client by sending manipulated data.
Decentralized Voltage Control with Peer-to-peer Energy Trading in a Distribution Network
Utilizing distributed renewable and energy storage resources via peer-to-peer (P2P) energy trading has long been touted as a solution to improve energy system's resilience and sustainability.
DeepMapping2: Self-Supervised Large-Scale LiDAR Map Optimization
LiDAR mapping is important yet challenging in self-driving and mobile robotics.
Robust Collaborative 3D Object Detection in Presence of Pose Errors
Collaborative 3D object detection exploits information exchange among multiple agents to enhance accuracy of object detection in presence of sensor impairments such as occlusion.
UNav: An Infrastructure-Independent Vision-Based Navigation System for People with Blindness and Low vision
Given a query image taken by an end-user on a mobile application, the pipeline leverages a visual place recognition (VPR) algorithm to find similar images in a reference image database of the target space.
Uncertainty Quantification of Collaborative Detection for Self-Driving
Our work is the first to estimate the uncertainty of collaborative object detection.
Self-Supervised Visual Place Recognition by Mining Temporal and Feature Neighborhoods
Inspired by noisy label learning, we propose a novel self-supervised framework named \textit{TF-VPR} that uses temporal neighborhoods and learnable feature neighborhoods to discover unknown spatial neighborhoods.
Adaptive Soft Contrastive Learning
Self-supervised learning has recently achieved great success in representation learning without human annotations.
Enhancing HDR Video Compression through CNN-based Effective Bit Depth Adaptation
In this work, we modify the MFRNet network architecture to enable multiple frame processing, and the new network, multi-frame MFRNet, has been integrated into the EBDA framework using two Versatile Video Coding (VVC) host codecs: VTM 16. 2 and the Fraunhofer Versatile Video Encoder (VVenC 1. 4. 0).
TENET: Transformer Encoding Network for Effective Temporal Flow on Motion Prediction
This technical report presents an effective method for motion prediction in autonomous driving.
Ranked #12 on
Motion Forecasting
on Argoverse CVPR 2020
Enhancing VVC with Deep Learning based Multi-Frame Post-Processing
This paper describes a CNN-based multi-frame post-processing approach based on a perceptually-inspired Generative Adversarial Network architecture, CVEGAN.
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.
Represent, Compare, and Learn: A Similarity-Aware Framework for Class-Agnostic Counting
In this work, we propose a similarity-aware CAC framework that jointly learns representation and similarity metric.
Ranked #4 on
Object Counting
on CARPK
An Empirical Study of Low Precision Quantization for TinyML
In this study, we focus on post-training quantization (PTQ) algorithms that quantize a model to low-bit (less than 8-bit) precision with only a small set of calibration data and benchmark them on different tinyML use cases.
V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Driving
Vehicle-to-everything (V2X) communication techniques enable the collaboration between vehicles and many other entities in the neighboring environment, which could fundamentally improve the perception system for autonomous driving.
RankDVQA: Deep VQA based on Ranking-inspired Hybrid Training
In recent years, deep learning techniques have shown significant potential for improving video quality assessment (VQA), achieving higher correlation with subjective opinions compared to conventional approaches.
ViSTRA3: Video Coding with Deep Parameter Adaptation and Post Processing
This paper presents a deep learning-based video compression framework (ViSTRA3).
A Dataset-Dispersion Perspective on Reconstruction Versus Recognition in Single-View 3D Reconstruction Networks
Our finding shows that a leading factor in determining recognition versus reconstruction is how dispersed the training data is.
SSR: An Efficient and Robust Framework for Learning with Unknown Label Noise
Under this setting, unlike previous methods that often introduce multiple assumptions and lead to complex solutions, we propose a simple, efficient and robust framework named Sample Selection and Relabelling(SSR), that with a minimal number of hyperparameters achieves SOTA results in various conditions.
Ranked #1 on
Image Classification
on CIFAR-10 (with noisy labels)
Learning Distilled Collaboration Graph for Multi-Agent Perception
Our approach is validated on V2X-Sim 1. 0, a large-scale multi-agent perception dataset that we synthesized using CARLA and SUMO co-simulation.
Ranked #4 on
3D Object Detection
on V2XSet
NYU-VPR: Long-Term Visual Place Recognition Benchmark with View Direction and Data Anonymization Influences
Visual place recognition (VPR) is critical in not only localization and mapping for autonomous driving vehicles, but also in assistive navigation for the visually impaired population.
Self-supervised Spatial Reasoning on Multi-View Line Drawings
Based on the fact that self-supervised learning is helpful when a large number of data are available, we propose two self-supervised learning approaches to improve the baseline performance for view consistency reasoning and camera pose reasoning tasks on the SPARE3D dataset.
Deep Weakly Supervised Positioning
PoseNet can map a photo to the position where it is taken, which is appealing in robotics.
Simultaneous Navigation and Construction Benchmarking Environments
We need intelligent robots for mobile construction, the process of navigating in an environment and modifying its structure according to a geometric design.
Fooling LiDAR Perception via Adversarial Trajectory Perturbation
LiDAR point clouds collected from a moving vehicle are functions of its trajectories, because the sensor motion needs to be compensated to avoid distortions.
Mobile Construction Benchmark
We need intelligent robots to perform mobile construction, the process of moving in an environment and modifying its geometry according to a design plan.
AETree: Areal Spatial Data Generation
Areal spatial data represent not only geographical locations but also sizes and shapes of physical objects such as buildings in a city.
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.
Video Compression with CNN-based Post Processing
In recent years, video compression techniques have been significantly challenged by the rapidly increased demands associated with high quality and immersive video content.
DR^2Track: Towards Real-Time Visual Tracking for UAV via Distractor Repressed Dynamic Regression
By repressing the response of distractors in the regressor learning, we can dynamically and adaptively alter our regression target to leverage the tracking robustness as well as adaptivity.
Automatic Failure Recovery and Re-Initialization for Online UAV Tracking with Joint Scale and Aspect Ratio Optimization
Current unmanned aerial vehicle (UAV) visual tracking algorithms are primarily limited with respect to: (i) the kind of size variation they can deal with, (ii) the implementation speed which hardly meets the real-time requirement.
P2B: Point-to-Box Network for 3D Object Tracking in Point Clouds
Specifically, we first sample seeds from the point clouds in template and search area respectively.
LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihood
In this paper, we present a novel framework for jointly predicting landmark locations, associated uncertainties of these predicted locations, and landmark visibilities.
Ranked #1 on
Face Alignment
on Menpo
SPARE3D: A Dataset for SPAtial REasoning on Three-View Line Drawings
Our experiments show that although convolutional networks have achieved superhuman performance in many visual learning tasks, their spatial reasoning performance on SPARE3D tasks is either lower than average human performance or even close to random guesses.
A Robust Speaker Clustering Method Based on Discrete Tied Variational Autoencoder
Recently, the speaker clustering model based on aggregation hierarchy cluster (AHC) is a common method to solve two main problems: no preset category number clustering and fix category number clustering.
3D Point Cloud Processing and Learning for Autonomous Driving
We present a review of 3D point cloud processing and learning for autonomous driving.
Incentive Analysis of Bitcoin-NG, Revisited
First, we propose a new incentive analysis that takes the network capacity into account, showing that Bitcoin-NG can still maintain incentive compatibility against the microblock mining attack even under limited network capacity.
Cryptography and Security Distributed, Parallel, and Cluster Computing
Regularizing Neural Networks via Minimizing Hyperspherical Energy
Inspired by the Thomson problem in physics where the distribution of multiple propelling electrons on a unit sphere can be modeled via minimizing some potential energy, hyperspherical energy minimization has demonstrated its potential in regularizing neural networks and improving their generalization power.
Solving Large-Scale 0-1 Knapsack Problems and its Application to Point Cloud Resampling
0-1 knapsack is of fundamental importance in computer science, business, operations research, etc.
Deep Unsupervised Learning of 3D Point Clouds via Graph Topology Inference and Filtering
The experimental results show that (1) the proposed networks outperform the state-of-the-art methods in various tasks; (2) a graph topology can be inferred as auxiliary information without specific supervision on graph topology inference; and (3) graph filtering refines the reconstruction, leading to better performances.
Dynamic Origin-Destination Matrix Prediction with Line Graph Neural Networks and Kalman Filter
In this paper we propose a novel O-D prediction framework combining heterogeneous prediction in graph neural networks and Kalman filter to recognize spatial and temporal patterns simultaneously.
Low-Power Computer Vision: Status, Challenges, Opportunities
In addition to mobile phones, many autonomous systems rely on visual data for making decisions and some of these systems have limited energy (such as unmanned aerial vehicles also called drones and mobile robots).
Real-time Soft Body 3D Proprioception via Deep Vision-based Sensing
Soft bodies made from flexible and deformable materials are popular in many robotics applications, but their proprioceptive sensing has been a long-standing challenge.
Robotics
Low Power Inference for On-Device Visual Recognition with a Quantization-Friendly Solution
The IEEE Low-Power Image Recognition Challenge (LPIRC) is an annual competition started in 2015 that encourages joint hardware and software solutions for computer vision systems with low latency and power.
DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds
We propose DeepMapping, a novel registration framework using deep neural networks (DNNs) as auxiliary functions to align multiple point clouds from scratch to a globally consistent frame.
Primitive Fitting Using Deep Boundary Aware Geometric Segmentation
To identify and fit geometric primitives (e. g., planes, spheres, cylinders, cones) in a noisy point cloud is a challenging yet beneficial task for fields such as robotics and reverse engineering.
2018 Low-Power Image Recognition Challenge
The Low-Power Image Recognition Challenge (LPIRC, https://rebootingcomputing. ieee. org/lpirc) is an annual competition started in 2015.
Simultaneous Edge Alignment and Learning
Edge detection is among the most fundamental vision problems for its role in perceptual grouping and its wide applications.
VLASE: Vehicle Localization by Aggregating Semantic Edges
In this paper, we propose VLASE, a framework to use semantic edge features from images to achieve on-road localization.
A Quantization-Friendly Separable Convolution for MobileNets
As deep learning (DL) is being rapidly pushed to edge computing, researchers invented various ways to make inference computation more efficient on mobile/IoT devices, such as network pruning, parameter compression, and etc.
FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation
Recent deep networks that directly handle points in a point set, e. g., PointNet, have been state-of-the-art for supervised learning tasks on point clouds such as classification and segmentation.
Ranked #15 on
3D Point Cloud Linear Classification
on ModelNet40
3D Point Cloud Linear Classification
General Classification
+1
Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling
Unlike on images, semantic learning on 3D point clouds using a deep network is challenging due to the naturally unordered data structure.
Detecting and Grouping Identical Objects for Region Proposal and Classification
Often multiple instances of an object occur in the same scene, for example in a warehouse.
CASENet: Deep Category-Aware Semantic Edge Detection
To this end, we propose a novel end-to-end deep semantic edge learning architecture based on ResNet and a new skip-layer architecture where category-wise edge activations at the top convolution layer share and are fused with the same set of bottom layer features.
Ranked #1 on
Edge Detection
on Cityscapes test
Fast Resampling of 3D Point Clouds via Graphs
We use a general feature-extraction operator to represent application-dependent features and propose a general reconstruction error to evaluate the quality of resampling.
