Search Results for author:
Found 51 papers, 22 papers with code
Distributed Swarm Learning for Edge Internet of Things
The rapid growth of Internet of Things (IoT) has led to the widespread deployment of smart IoT devices at wireless edge for collaborative machine learning tasks, ushering in a new era of edge learning.
Unraveling the Mystery of Scaling Laws: Part I
However, the original scaling law paper by OpenAI did not disclose the complete details necessary to derive the precise scaling law formulas, and their conclusions are only based on models containing up to 1. 5 billion parameters.
RobustCalib: Robust Lidar-Camera Extrinsic Calibration with Consistency Learning
Current traditional methods for LiDAR-camera extrinsics estimation depend on offline targets and human efforts, while learning-based approaches resort to iterative refinement for calibration results, posing constraints on their generalization and application in on-board systems.
Byzantine-Robust Distributed Online Learning: Taming Adversarial Participants in An Adversarial Environment
But we prove that, even with a class of state-of-the-art robust aggregation rules, in an adversarial environment and in the presence of Byzantine participants, distributed online gradient descent can only achieve a linear adversarial regret bound, which is tight.
SegViTv2: Exploring Efficient and Continual Semantic Segmentation with Plain Vision Transformers
This paper investigates the capability of plain Vision Transformers (ViTs) for semantic segmentation using the encoder-decoder framework and introduces \textbf{SegViTv2}.
Ranked #16 on
Semantic Segmentation
on ADE20K
FastPillars: A Deployment-friendly Pillar-based 3D Detector
The deployment of 3D detectors strikes one of the major challenges in real-world self-driving scenarios.
Super-Resolution Harmonic Retrieval of Non-Circular Signals
This paper proposes a super-resolution harmonic retrieval method for uncorrelated strictly non-circular signals, whose covariance and pseudo-covariance present Toeplitz and Hankel structures, respectively.
Compressive Spectrum Sensing Using Sampling-Controlled Block Orthogonal Matching Pursuit
To this end, the minimum number of required measurements for successful recovery is first derived in terms of its probabilistic lower bound.
Compressive Spectrum Sensing Using Blind-Block Orthogonal Least Squares
In this paper, we propose a blind-block orthogonal least squares-based compressive spectrum sensing (B-BOLS-CSS) algorithm, which utilizes a novel blind stopping rule to cut the cords to these prior information.
Robust Distributed Learning Against Both Distributional Shifts and Byzantine Attacks
In this paper, we propose a new algorithm that equips distributed learning with robustness measures against both distributional shifts and byzantine attacks.
Distributed Swarm Learning for Internet of Things at the Edge: Where Artificial Intelligence Meets Biological Intelligence
With the proliferation of versatile Internet of Things (IoT) services, smart IoT devices are increasingly deployed at the edge of wireless networks to perform collaborative machine learning tasks using locally collected data, giving rise to the edge learning paradigm.
SegViT: Semantic Segmentation with Plain Vision Transformers
We explore the capability of plain Vision Transformers (ViTs) for semantic segmentation and propose the SegVit.
Ranked #4 on
Semantic Segmentation
on COCO-Stuff test
CB-DSL: Communication-efficient and Byzantine-robust Distributed Swarm Learning on Non-i.i.d. Data
data issues and Byzantine attacks, global data samples are introduced in CB-DSL and shared among IoT workers, which not only alleviates the local data heterogeneity effectively but also enables to fully utilize the exploration-exploitation mechanism of swarm intelligence.
QC-ODKLA: Quantized and Communication-Censored Online Decentralized Kernel Learning via Linearized ADMM
We then propose a novel learning framework named Online Decentralized Kernel learning via Linearized ADMM (ODKLA) to efficiently solve the online decentralized kernel learning problem.
Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images
In this work, we tackle this challenging issue with a novel range view projection mechanism, and for the first time demonstrate the benefits of fusing multi-frame point clouds for a range-view based detector.
Blind Orthogonal Least Squares based Compressive Spectrum Sensing
As an enabling technique of cognitive radio (CR), compressive spectrum sensing (CSS) based on compressive sensing (CS) can detect the spectrum opportunities from wide frequency bands efficiently and accurately by using sub-Nyquist sampling rate.
Adaptive-Gravity: A Defense Against Adversarial Samples
We conceptualize the model parameters/features associated with each class as a mass characterized by its centroid location and the spread (standard deviation of the distance) of features around the centroid.
Poseur: Direct Human Pose Regression with Transformers
We propose a direct, regression-based approach to 2D human pose estimation from single images.
Ranked #2 on
Keypoint Detection
on MS COCO
Fed2: Feature-Aligned Federated Learning
Federated learning learns from scattered data by fusing collaborative models from local nodes.
NAS-FCOS: Efficient Search for Object Detection Architectures
Neural Architecture Search (NAS) has shown great potential in effectively reducing manual effort in network design by automatically discovering optimal architectures.
BEV-SGD: Best Effort Voting SGD for Analog Aggregation Based Federated Learning against Byzantine Attackers
As a promising distributed learning technology, analog aggregation based federated learning over the air (FLOA) provides high communication efficiency and privacy provisioning under the edge computing paradigm.
Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation
This neural representation enables our decoder to leverage the smoothness prior in the semantic label space, and thus makes our decoder more efficient.
FCPose: Fully Convolutional Multi-Person Pose Estimation with Dynamic Instance-Aware Convolutions
We propose a fully convolutional multi-person pose estimation framework using dynamic instance-aware convolutions, termed FCPose.
Twins: Revisiting the Design of Spatial Attention in Vision Transformers
Very recently, a variety of vision transformer architectures for dense prediction tasks have been proposed and they show that the design of spatial attention is critical to their success in these tasks.
Ranked #48 on
Semantic Segmentation
on ADE20K val
Joint Optimization of Communications and Federated Learning Over the Air
Federated learning (FL) is an attractive paradigm for making use of rich distributed data while protecting data privacy.
1-Bit Compressive Sensing for Efficient Federated Learning Over the Air
For distributed learning among collaborative users, this paper develops and analyzes a communication-efficient scheme for federated learning (FL) over the air, which incorporates 1-bit compressive sensing (CS) into analog aggregation transmissions.
TFPose: Direct Human Pose Estimation with Transformers
We propose a human pose estimation framework that solves the task in the regression-based fashion.
Ranked #26 on
Pose Estimation
on MPII Human Pose
(using extra training data)
Conditional Positional Encodings for Vision Transformers
Built on PEG, we present Conditional Position encoding Vision Transformer (CPVT).
Instance and Panoptic Segmentation Using Conditional Convolutions
In the literature, top-performing instance segmentation methods typically follow the paradigm of Mask R-CNN and rely on ROI operations (typically ROIAlign) to attend to each instance.
BoxInst: High-Performance Instance Segmentation with Box Annotations
We present a high-performance method that can achieve mask-level instance segmentation with only bounding-box annotations for training.
Unifying Instance and Panoptic Segmentation with Dynamic Rank-1 Convolutions
To our knowledge, DR1Mask is the first panoptic segmentation framework that exploits a shared feature map for both instance and semantic segmentation by considering both efficacy and efficiency.
The Capacity of Multi-user Private Information Retrieval for Computationally Limited Databases
This scheme is of particular significance when there is only one accessible database -- a special case that turns out to be more challenging for PIR in the multi-database case.
Heterogeneous Federated Learning
Specifically, we design a feature-oriented regulation method ({$\Psi$-Net}) to ensure explicit feature information allocation in different neural network structures.
Third-Order Statistics Reconstruction from Compressive Measurements
Estimation of third-order statistics relies on the availability of a huge amount of data records, which can pose severe challenges on the data collecting hardware in terms of considerable storage costs, overwhelming energy consumption, and unaffordably high sampling rate especially when dealing with high-dimensional data such as wideband signals.
FCOS: A simple and strong anchor-free object detector
In computer vision, object detection is one of most important tasks, which underpins a few instance-level recognition tasks and many downstream applications.
Mask Encoding for Single Shot Instance Segmentation
To date, instance segmentation is dominated by twostage methods, as pioneered by Mask R-CNN.
Conditional Convolutions for Instance Segmentation
We propose a simple yet effective instance segmentation framework, termed CondInst (conditional convolutions for instance segmentation).
DiverseDepth: Affine-invariant Depth Prediction Using Diverse Data
Compared with previous learning objectives, i. e., learning metric depth or relative depth, we propose to learn the affine-invariant depth using our diverse dataset to ensure both generalization and high-quality geometric shapes of scenes.
COKE: Communication-Censored Decentralized Kernel Learning
This paper studies the decentralized optimization and learning problem where multiple interconnected agents aim to learn an optimal decision function defined over a reproducing kernel Hilbert space by jointly minimizing a global objective function, with access to their own locally observed dataset.
Learning and Memorizing Representative Prototypes for 3D Point Cloud Semantic and Instance Segmentation
3D point cloud semantic and instance segmentation is crucial and fundamental for 3D scene understanding.
Ranked #27 on
3D Instance Segmentation
on ScanNet(v2)
BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation
The proposed BlendMask can effectively predict dense per-pixel position-sensitive instance features with very few channels, and learn attention maps for each instance with merely one convolution layer, thus being fast in inference.
Ranked #13 on
Real-time Instance Segmentation
on MSCOCO
DirectPose: Direct End-to-End Multi-Person Pose Estimation
We propose the first direct end-to-end multi-person pose estimation framework, termed DirectPose.
Ranked #13 on
Keypoint Detection
on COCO test-dev
Convolutional Character Networks
We evaluate CharNet on three standard benchmarks, where it consistently outperforms the state-of-the-art approaches [25, 24] by a large margin, e. g., with improvements of 65. 33%->71. 08% (with generic lexicon) on ICDAR 2015, and 54. 0%->69. 23% on Total-Text, on end-to-end text recognition.
Ranked #2 on
Scene Text Detection
on ICDAR 2015
Communication-Censored Linearized ADMM for Decentralized Consensus Optimization
COLA is proven to be convergent when the local cost functions have Lipschitz continuous gradients and the censoring threshold is summable.
NAS-FCOS: Fast Neural Architecture Search for Object Detection
The success of deep neural networks relies on significant architecture engineering.
Ranked #113 on
Object Detection
on COCO test-dev
FCOS: Fully Convolutional One-Stage Object Detection
By eliminating the predefined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating overlapping during training.
Ranked #4 on
Pedestrian Detection
on TJU-Ped-campus
Knowledge Adaptation for Efficient Semantic Segmentation
To tackle this dilemma, we propose a knowledge distillation method tailored for semantic segmentation to improve the performance of the compact FCNs with large overall stride.
Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation
In this work, we propose a data-dependent upsampling (DUpsampling) to replace bilinear, which takes advantages of the redundancy in the label space of semantic segmentation and is able to recover the pixel-wise prediction from low-resolution outputs of CNNs.
Ranked #46 on
Semantic Segmentation
on PASCAL Context
An end-to-end TextSpotter with Explicit Alignment and Attention
This allows the two tasks to work collaboratively by shar- ing convolutional features, which is critical to identify challenging text instances.
Detecting Text in Natural Image with Connectionist Text Proposal Network
We propose a novel Connectionist Text Proposal Network (CTPN) that accurately localizes text lines in natural image.
