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Found 30 papers, 6 papers with code
TAVP: Task-Adaptive Visual Prompt for Cross-domain Few-shot Segmentation
While ensuring that the prior knowledge of SAM is not discarded, the new branch disentangles category and domain information through prototypes, guiding it in adapting the CD-FSS.
NuSegDG: Integration of Heterogeneous Space and Gaussian Kernel for Domain-Generalized Nuclei Segmentation
To alleviate the labor-intensive requirement of manual prompts, we introduce a Gaussian-Kernel Prompt Encoder (GKP-Encoder) to generate density maps driven by a single point, which guides segmentation predictions by mixing position prompts and semantic prompts.
ESP-MedSAM: Efficient Self-Prompting SAM for Universal Domain-Generalized Medical Image Segmentation
Finally, we design the Query-Decoupled Modality Decoder (QDMD) that leverages a one-to-one strategy to provide an independent decoding channel for every modality.
Millimeter Wave Radar-based Human Activity Recognition for Healthcare Monitoring Robot
Finally, we deploy our system on a movable robot-mounted edge computing platform, achieving flexible healthcare monitoring in real-world scenarios.
ESP-Zero: Unsupervised enhancement of zero-shot classification for Extremely Sparse Point cloud
To the point cloud encoders to fit the extremely sparse point clouds without re-running the pre-training procedure which could be time-consuming and expensive, in this work, we propose an unsupervised model adaptation approach to enhance the point cloud encoder for the extremely sparse point clouds.
MorphText: Deep Morphology Regularized Arbitrary-shape Scene Text Detection
Bottom-up text detection methods play an important role in arbitrary-shape scene text detection but there are two restrictions preventing them from achieving their great potential, i. e., 1) the accumulation of false text segment detections, which affects subsequent processing, and 2) the difficulty of building reliable connections between text segments.
Scale Optimization Using Evolutionary Reinforcement Learning for Object Detection on Drone Imagery
A set of rewards measuring the localization accuracy, the accuracy of predicted labels, and the scale consistency among nearby patches are designed in the agent to guide the scale optimization.
Point Clouds Are Specialized Images: A Knowledge Transfer Approach for 3D Understanding
Self-supervised representation learning (SSRL) has gained increasing attention in point cloud understanding, in addressing the challenges posed by 3D data scarcity and high annotation costs.
CARD: Semantic Segmentation with Efficient Class-Aware Regularized Decoder
Extensive experiments and ablation studies conducted on multiple benchmark datasets demonstrate that the proposed CAR can boost the accuracy of all baseline models by up to 2. 23% mIOU with superior generalization ability.
Pixels, Regions, and Objects: Multiple Enhancement for Salient Object Detection
A multi-level hybrid loss is firstly designed to guide the network to learn pixel-level, region-level, and object-level features.
Leveraging Systematic Knowledge of 2D Transformations
The existing deep learning models suffer from out-of-distribution (o. o. d.)
CAR: Class-aware Regularizations for Semantic Segmentation
Recent segmentation methods, such as OCR and CPNet, utilizing "class level" information in addition to pixel features, have achieved notable success for boosting the accuracy of existing network modules.
Ranked #8 on
Semantic Segmentation
on PASCAL Context
Channelized Axial Attention for Semantic Segmentation -- Considering Channel Relation within Spatial Attention for Semantic Segmentation
Spatial and channel attentions, modelling the semantic interdependencies in spatial and channel dimensions respectively, have recently been widely used for semantic segmentation.
Ranked #6 on
Semantic Segmentation
on COCO-Stuff test
EdgeLoc: An Edge-IoT Framework for Robust Indoor Localization Using Capsule Networks
First, the localization accuracy can be degraded by the random signal fluctuations, which would influence conventional localization algorithms that simply learn handcrafted features from raw fingerprint data.
Binarized Graph Neural Network
Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches to binarize the model parameters and learn the compact embedding.
Security and Privacy in IoT Using Machine Learning and Blockchain: Threats & Countermeasures
In this paper, we provide a summary of research efforts made in the past few years, starting from 2008 to 2019, addressing security and privacy issues using ML algorithms and BCtechniques in the IoT domain.
PDANet: Pyramid Density-aware Attention Net for Accurate Crowd Counting
For this purpose, a classifier evaluates the density level of the input features and then passes them to the corresponding high and low crowded DAD modules.
See More Than Once -- Kernel-Sharing Atrous Convolution for Semantic Segmentation
Experiments conducted on the benchmark PASCAL VOC 2012 dataset show that the proposed sharing strategy can not only boost a network s generalization and representation abilities but also reduce the model complexity significantly.
FACLSTM: ConvLSTM with Focused Attention for Scene Text Recognition
Scene text recognition has recently been widely treated as a sequence-to-sequence prediction problem, where traditional fully-connected-LSTM (FC-LSTM) has played a critical role.
DENet: A Universal Network for Counting Crowd with Varying Densities and Scales
Counting people or objects with significantly varying scales and densities has attracted much interest from the research community and yet it remains an open problem.
Voiceprint recognition of Parkinson patients based on deep learning
In this paper, a method based on Deep Neural Network (DNN) recognition and classification combined with Mini-Batch Gradient Descent (MBGD) is proposed to distinguish PD patients from healthy people using voiceprint features.
A-CCNN: adaptive ccnn for density estimation and crowd counting
Crowd counting, for estimating the number of people in a crowd using vision-based computer techniques, has attracted much interest in the research community.
Beyond Context: Exploring Semantic Similarity for Tiny Face Detection
Tiny face detection aims to find faces with high degrees of variability in scale, resolution and occlusion in cluttered scenes.
A Structural Correlation Filter Combined with A Multi-task Gaussian Particle Filter for Visual Tracking
First, it can detect the tracked target in a large-scale search scope via weak KCF trackers and evaluate the reliability of weak trackers\rq decisions for a Gaussian particle filter to make a strong decision, and hence it can tackle fast motions, appearance variations, occlusions and re-detections.
Structured Inhomogeneous Density Map Learning for Crowd Counting
In this paper, we aim at tackling the problem of crowd counting in extremely high-density scenes, which contain hundreds, or even thousands of people.
Soft Locality Preserving Map (SLPM) for Facial Expression Recognition
Furthermore, the proposed feature-generation method can improve the generalizability of the underlying manifolds for facial expression recognition.
Face Parsing via a Fully-Convolutional Continuous CRF Neural Network
In this work, we address the face parsing task with a Fully-Convolutional continuous CRF Neural Network (FC-CNN) architecture.
Diversity-Enhanced Condensation Algorithm and Its Application for Robust and Accurate Endoscope Three-Dimensional Motion Tracking
The paper proposes a diversity-enhanced condensation algorithm to address the particle impoverishment problem which stochastic filtering usually suffers from.
