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Found 26 papers, 12 papers with code
Polynomial Regression Network for Variable-Number Lane Detection
Most of previous methods utilize semantic segmentation to identify the regions of traffic lanes in an image, and then adopt some curve-fitting method to reconstruct the lanes.
Multipath Component-Aided Signal Processing for Integrated Sensing and Communication Systems
Specifically, we propose a symbol-level fusion for MPC-aided localization (SFMC) scheme to achieve robust and high-accuracy localization, and apply a Khatri-Rao space-time (KRST) code to improve the communication and sensing performance in rich multipath environment.
How to select slices for annotation to train best-performing deep learning segmentation models for cross-sectional medical images?
Automated segmentation of medical images highly depends on the availability of accurate manual image annotations.
Quantifying the Limits of Segment Anything Model: Analyzing Challenges in Segmenting Tree-Like and Low-Contrast Structures
Segment Anything Model (SAM) has shown impressive performance in interactive and zero-shot segmentation across diverse domains, suggesting that they have learned a general concept of "objects" from their large-scale training.
Advancing Sustainability via Recommender Systems: A Survey
Human behavioral patterns and consumption paradigms have emerged as pivotal determinants in environmental degradation and climate change, with quotidian decisions pertaining to transportation, energy utilization, and resource consumption collectively precipitating substantial ecological impacts.
OpenSatMap: A Fine-grained High-resolution Satellite Dataset for Large-scale Map Construction
By publishing and maintaining the dataset, we provide a high-quality benchmark for satellite-based map construction and downstream tasks like autonomous driving.
Multi-modal Food Recommendation using Clustering and Self-supervised Learning
Our preliminary investigation of two datasets indicates that pre-trained multi-modal dense representations might precipitate a deterioration in performance compared to ID features when encapsulating interactive relationships.
ClinicalLab: Aligning Agents for Multi-Departmental Clinical Diagnostics in the Real World
Existing clinical diagnostic evaluation benchmarks for evaluating medical agents powered by LLMs have severe limitations.
Infer from What You Have Seen Before: Temporally-dependent Classifier for Semi-supervised Video Segmentation
Motivated by this observation we propose a novel temporally-dependent classifier (TDC) to mimic the human-like recognition procedure.
Revisiting Foreground and Background Separation in Weakly-supervised Temporal Action Localization: A Clustering-based Approach
It comprises two core components: a snippet clustering component that groups the snippets into multiple latent clusters and a cluster classification component that further classifies the cluster as foreground or background.
How to Efficiently Annotate Images for Best-Performing Deep Learning Based Segmentation Models: An Empirical Study with Weak and Noisy Annotations and Segment Anything Model
In situations where unlimited annotation time was available, precise annotations still lead to the highest segmentation model performance.
Smart Agent-Based Modeling: On the Use of Large Language Models in Computer Simulations
ABM's strength lies in its bottom-up methodology, illuminating emergent phenomena by modeling the behaviors of individual components of a system.
Exploiting Low-confidence Pseudo-labels for Source-free Object Detection
The LPU module consists of Proposal Soft Training (PST) and Local Spatial Contrastive Learning (LSCL).
Convolutional Neural Networks Rarely Learn Shape for Semantic Segmentation
Shape learning, or the ability to leverage shape information, could be a desirable property of convolutional neural networks (CNNs) when target objects have specific shapes.
Segment Anything Model for Medical Image Analysis: an Experimental Study
We conclude that SAM shows impressive zero-shot segmentation performance for certain medical imaging datasets, but moderate to poor performance for others.
Mutilmodal Feature Extraction and Attention-based Fusion for Emotion Estimation in Videos
The continuous improvement of human-computer interaction technology makes it possible to compute emotions.
Towards Effective Instance Discrimination Contrastive Loss for Unsupervised Domain Adaptation
We further propose a target-dominated cross-domain mixup that can incorporate accurate semantic information from the source domain.
Preparing the Future for Continual Semantic Segmentation
In this study, we focus on Continual Semantic Segmentation (CSS) and present a novel approach to tackle the issue of existing methods struggling to learn new classes.
Class Relationship Embedded Learning for Source-Free Unsupervised Domain Adaptation
To this end, we first regard the classifier weights of the source model as class prototypes to compute class relationship, and then propose a novel probability-based similarity between target-domain samples by embedding the source-domain class relationship, resulting in Class Relationship embedded Similarity (CRS).
Enhancing Sequential Recommendation with Graph Contrastive Learning
Specifically, GCL4SR employs a Weighted Item Transition Graph (WITG), built based on interaction sequences of all users, to provide global context information for each interaction and weaken the noise information in the sequence data.
Who will stay? Using Deep Learning to predict engagement of citizen scientists
Using data from the annotation activity of citizen scientists in a Swedish marine project, we constructed Deep Neural Network models to predict forthcoming engagement.
Low-confidence Samples Matter for Domain Adaptation
Representing the target data structure in such a way would overlook the huge low-confidence samples, resulting in sub-optimal transferability that is biased towards the samples similar to the source domain.
5th Place Solution for VSPW 2021 Challenge
In this article, we introduce the solution we used in the VSPW 2021 Challenge.
Probabilistic Contrastive Learning for Domain Adaptation
However, it is undesirably observed that the standard contrastive paradigm (features+$\ell_{2}$ normalization) only brings little help for domain adaptation.
RPN Prototype Alignment for Domain Adaptive Object Detector
It essentially cooperates the learning of RPN prototypes and features to align the source and target RPN features.
Deep Reinforcement Learning for Adaptive Exploration of Unknown Environments
Often, these missions start with building a map for the environment via pure exploration and subsequently using (i. e. exploiting) the generated map for downstream navigation tasks.
