CDDN tackles visual relationship detection with context-dependent diffusion networks.
problem Combustion of combinatorial explosion in relation triplets detection.
method CDDN framework using semantic and visual scene graphs for adaptive information aggregation.
result CDDN achieves state-of-the-art performance on visual relationship detection datasets.
SAFE detects fake news by analyzing text and image similarities.
problem Detecting fake news with less focus on text-image similarity.
method SAFE uses neural networks to extract text and visual features, then learns their relationship to predict fake news.
result SAFE effectively recognizes fake news based on text, images, or mismatches.
Method detects and visualizes changes in financial markets' asset relationships.
problem Detecting and explaining changes in financial markets' asset relationships.
method Construct co-occurrence networks, calculate Graph-Based Entropy, apply Differential Network.
result Visualization of changes in financial markets with high interpretability.
Paper tackles zero-shot learning for semantic image interpretation.
problem Extracting structured semantic descriptions from images requires complete training sets, which are often unavailable.
method Uses Logic Tensor Networks to leverage logical constraints and similarities among relationships in the training set.
result Background knowledge can alleviate the incompleteness of training sets, improving zero-shot learning performance.
UN-AVOIDS visualizes and detects anomalies without needing labeled data.
problem The need for a unified framework to visualize and detect anomalies.
method UN-AVOIDS is an unsupervised, nonparametric approach that transforms data into a new space (NCDF) for both visualization and detection.
result UN-AVOIDS assigns invariant anomalous scores and achieves high AUC in detecting anomalies.
Study improves recognition of long-tail visual relationships.
problem Improving recognition of structured visual relationships from long-tail classes.
method Developed two benchmarks, introduced VilHub loss, and applied RelMix augmentation.
result Simple techniques significantly improved performance on tail classes.
Constellation learns group-level visual relationships for abstract reasoning.
problem Learning configurational properties of entire groups of objects.
method Introduces Constellation, a network that learns relational abstractions over static visual scenes.
result Offers a basis for abstract relational reasoning and sensory imagination.
Visual spoofing bypasses spam filters and plagiarism detection.
problem Vulnerability in spam filters that can be exploited by visually similar but differently encoded characters.
method Replaces characters with visually similar but differently encoded characters from a different alphabet.
result Spammers can create messages that bypass existing spam filters.
A new method detects interactions in machine learning models.
problem Interpreting non-linear and interaction effects in machine learning models.
method Regional effect plots with implicit interaction detection.
result The method quantifies and interprets feature effects reliably, less confounded by interactions.
CMC method detects causal relationships in time series data.
problem Understanding causal relationships in nonlinear systems.
method Cross-Mapping Coherence method, based on nonlinear state-space reconstruction and coherence metrics.
result CMC accurately identifies causal connections in various systems.
Paper develops brain-inspired unsupervised learning for scalable object detection.
problem Machine vision systems struggle with object discovery and detection, especially under nonideal conditions.
method Leverages large-scale perceptual data to develop brain-inspired flexible, scale, and shift invariant object prototypes.
result Efficient unsupervised learning framework constructs accurate part-aware object models and robust detection algorithms.
This paper finds a linear relationship between t-SNE perplexity and data set size.
problem Choosing the right perplexity for t-SNE embeddings.
method Analyzed the relationship between perplexity and data set size.
result Embeddings remain structurally consistent when perplexity is adjusted accordingly.
It is well known that quasi-isometric embeddings of Gromov hyperbolic spaces induce topological embeddings of their Gromov boundaries. A more general question is to detect classes of functions between Gromov hyperbolic spaces that induce continuous maps between their Gromov boundaries. In this paper we introduce the cl…
The paper evaluates saliency methods for image predictions, finding some inadequate.
problem The reliability of saliency methods in explaining model predictions.
method Proposed a methodology to evaluate saliency methods based on their independence from model and data.
result Some saliency methods are independent of both model and data, making them inadequate for certain tasks.
Detects anomalies in autonomous mobile robots using vision.
problem Anomaly detection for autonomous mobile robots.
method Unsupervised deep learning methods and a novel dataset.
result State-of-the-art approach tested on a new dataset.
Proposes a fair deep anomaly detection method.
problem Ensuring fairness in deep anomaly detection.
method Adversarial network to de-correlate sensitive attributes and learned representations.
result Our approach largely removes unfairness with minimal anomaly detection performance loss.
Associating image regions with text queries has been recently explored as a new way to bridge visual and linguistic representations. A few pioneering approaches have been proposed based on recurrent neural language models trained generatively (e.g., generating captions), but achieving somewhat limited localization accu…
Visual analytics tool detects and corrects concept drift in data streams.
problem Concept drift causes inaccurate predictions in evolving data.
method DriftVis combines drift detection and visualization.
result Visual analytics supports detection, examination, and correction of concept drift.
Few-shot visual reasoning model learns analogical relationships from small data.
problem Training deep models on few samples for visual reasoning tasks.
method Meta-analogical contrastive learning to enforce structural similarity between training and test samples.
result Method outperforms state-of-the-art on RAVEN dataset with scarce training data.
Evolutionary methods improve understanding of LLMs and their relationships.
problem Improving understanding of LLMs and their relationships.
method Relating weights to genotypes and output text to phenotypes using evolutionary methods.
result Estimated evolutionary trees reliably recover the topology of the ground-truth training tree.
Improved student engagement detection using contextual and visual data.
problem Detecting students' behavioral engagement in real-world settings.
method Two-phase approach: contextual logs for active use, appearance information for engagement inference.
result Improved F1-scores from 0.77 to 0.82 with contextual information.
A new system detects and classifies defects in semiconductor manufacturing.
problem Detecting and classifying novel defect patterns in high-resolution imagery.
method Stacked hybrid convolutional neural networks (SH-CNN) with visual attention.
result SH-CNN outperforms current approaches in automated visual inspection.
Survey visual analytics methods for detecting anomalous user behaviors.
problem Understanding and detecting anomalous user behaviors in various domains.
method Survey and classification of visual analytics methods in four categories.
result Discussion of findings and potential research directions.
Proposes manifold-based unsupervised anomaly detection for visual data.
problem Rare anomalies in unlabeled data.
method Constant curvature manifolds, hyperspherical Variational Auto-Encoders (VAE) with gyroplane layer.
result State-of-the-art results on visual anomaly benchmarks and histopathology.
DEMUD-VIS detects novel image content and explains it visually.
problem Detecting and explaining novel image content in large datasets.
method Uses CNN for feature extraction, reconstruction error for novelty detection, and up-convolutional networks for image reconstruction.
result Demonstrates visual explanations of novel image content on diverse datasets.
Paper uses SSD to detect miners' activities in a mining environment.
problem Tracking miners' activities in a mining environment with little obstruction.
method Used SSD trained on COCO dataset to detect miners' activities. Implemented machine learning algorithms using Tensorflow and C++.
result Improved accuracy of detecting miners' activities through data fusion.
Capsule networks detect and diagnose adversarial images better than CNNs.
problem Detecting and diagnosing adversarial images in neural networks.
method Class-conditional capsule reconstruction and reconstructive attack.
result Capsule networks outperform CNNs in detecting and diagnosing adversarial images.
Automated graphics testing detects novel corruptions without manual labeling.
problem Detecting novel visual corruptions in graphics unit testing without manual labeling.
method Reproduces driver bugs to generate corruptions and uses Multiple Instance Learning (MIL) methods.
result Significantly outperforms unsupervised methods and discovers novel corruptions.
This paper presents a self-supervised method for visual detection of the active speaker in a multi-person spoken interaction scenario. Active speaker detection is a fundamental prerequisite for any artificial cognitive system attempting to acquire language in social settings. The proposed method is intended to compleme…
Two approaches for understanding and visualizing 3D surgery on manifolds.
problem Understanding and visualizing the effects of 3D surgery on manifolds.
method Two approaches: fundamental group alterations and visualizations of rotations.
result New visualizations and methods for understanding 3D surgery.
Enhances group convolutional networks with attention to learn meaningful relationships.
problem Lack of explicit means to learn meaningful relationships among symmetry patterns.
method Introduces attentive group equivariant convolutions, applying attention during convolution.
result Consistently outperforms conventional group convolutional networks on benchmark datasets.
PySS3 simplifies access to SS3's text classification and visualization.
problem Limited availability of an open-source SS3 implementation.
method Developed PySS3, an open-source Python package implementing SS3.
result PySS3 enables robust, explainable, and trusty text classification.
PyODDS automates outlier detection for new data sources.
problem Manual outlier detection is inefficient and domain-specific.
method Automated end-to-end system with Apache Spark and database support.
result PyODDS optimizes outlier detection pipelines automatically.
Study detects anomalies in robot vision data to predict hazards.
problem Detecting unexpected hazards in robot exploration data.
method Anomaly detection using autoencoders at different scales.
result Autoencoders improve anomaly detection performance on diverse robot scenarios.
SVMs improve forest fire detection accuracy on challenging datasets.
problem Rapid and accurate detection of forest fires.
method Training SVMs on labeled fire image datasets, focusing on data preprocessing, feature extraction, and model training.
result SVMs enhance detection accuracy on complex datasets, revealing key parameters affecting performance.
Reassesses calibration metrics in machine learning models.
problem Inconsistent reporting of calibration metrics in recent literature.
method Calibration-based decomposition of Bregman divergences, visualization of calibration and generalization error.
result New visualization technique for detecting trade-offs between calibration and generalization.
We propose a method to visualize class similarity in large-scale classifiers.
problem Analyzing hierarchical structures and relationships in large-scale classification.
method Compute class similarity based on prediction scores and visualize the class similarity matrix.
result Visualizing class similarity matrices reveals hierarchical structures and relationships.
sPortfolio visualizes stock portfolios and factor data for better investment analysis.
problem Insufficient intuitive visual analytics for multi-factor stock portfolios.
method Develops a holistic visualization system for risk-factor, multiple-portfolios, and single-portfolios.
result Facilitates actionable insights and market trend understanding through intuitive visual analytics.
A novel method for visual question answering using scene graphs and reinforcement learning.
problem Answering free-form questions about images with deep linguistic and visual understanding.
method Context-driven, sequential reasoning based on scene graphs and reinforcement learning.
result Our method almost reaches human performance on the GQA dataset.
We consider the task of semantic robotic grasping, in which a robot picks up an object of a user-specified class using only monocular images. Inspired by the two-stream hypothesis of visual reasoning, we present a semantic grasping framework that learns object detection, classification, and grasp planning in an end-to-…
We employ the Bayesian framework to define a cointegration measure aimed to represent long term relationships between time series. For visualization of these relationships we introduce a dissimilarity matrix and a map based on the Sorting Points Into Neighborhoods (SPIN) technique, which has been previously used to ana…
Improved detection of burnt areas in satellite images using evolved hyper-features.
problem Radiometric variations across satellite images and different datasets.
method Understanding feature spaces, training on multi-image datasets, evolving hyper-features, and optimizing for different classifiers.
result Training on multi-image datasets improves model generalization, and evolved hyper-features enhance classifier performance.
Enhances binscatter method for better visualization and econometrics.
problem Incorrect conclusions from covariate adjustment in binscatter.
method Formalizes binscatter properties, introduces new tools for estimation and uncertainty quantification.
result Substantially different results from prior methods in applications.
Algorithm detects lead-lag relationships in multivariate time series.
problem Understanding temporal dependencies between time series.
method Cluster-driven methodology based on dynamic time warping.
result Robust detection of lead-lag relationships in lagged multi-factor models.
Tool visualizes Transformer model attention for better understanding.
problem Understanding complex attention mechanisms in deep learning models.
method Developed an open-source tool to visualize attention at three levels.
result Visualization helps interpret and analyze Transformer models.
Kernelized Taylor diagram visualizes data populations with fewer assumptions.
problem Limitations of Taylor diagram in capturing non-linear relationships and sensitivity to outliers.
method Proposes a kernelized version of the Taylor diagram that uses maximum mean discrepancy and kernel mean embedding.
result Kernelized Taylor diagram visualizes data populations with minimal assumptions of data distributions.
HMN detects fake faces using memory networks and visual cues.
problem Detecting fake faces from unseen manipulation types.
method Hierarchical Memory Network (HMN) architecture.
result Superior performance in fake and fraudulent face detection.
Multivariate Pattern (MVP) classification holds enormous potential for decoding visual stimuli in the human brain by employing task-based fMRI data sets. There is a wide range of challenges in the MVP techniques, i.e. decreasing noise and sparsity, defining effective regions of interest (ROIs), visualizing results, and…