Deep learning improves microscale object segmentation.
problem Accurate estimation of crowded microscale objects.
method Deep learning model for semantic segmentation.
result High mean Intersection Over Union score of 0.91.
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48 results for “Microscale objects”
We present a generic framework for spatio-temporal (ST) data modeling, analysis, and forecasting, with a special focus on data that is sparse in both space and time. Our multi-scaled framework is a seamless coupling of two major components: a self-exciting point process that models the macroscale statistical behaviors …
The paper models cryptocurrency market bubbles using agent-based models.
problem Understanding speculative bubbles in cryptocurrency markets.
method Agent-based models and ODE models to estimate return rates and market values.
result Formulated formula for total system risk.
We show that some pieces of cylinders bounded by two parallel straight-lines bifurcate in a family of periodic non-rotational surfaces with constant mean curvature and with the same boundary conditions. These cylinders are initial interfaces in a problem of microscale range modeling the morphologies that adopt a liquid…
Solvents can induce helical knots in simulated biopolymer tubes.
problem Understanding how solvents influence the folding of biopolymers.
method Computer simulations using morphometric solvation techniques.
result Solvents can induce complex helical structures, including knots.
Neural network approach simplifies multiscale problem homogenization.
problem Homogenizing multiscale problems with varying microscale structures.
method Derivative-free neural network with Brownian walkers.
result Neural network method is computationally efficient and robust.
New method learns kernels in nonlocal operators robustly.
problem Learning kernels in nonlocal operators is ill-posed.
method Nonparametric regression with Tikhonov regularization.
result Robust estimator of kernel yields homogenized model.
Differentiable simulations control molecular Hamiltonians for desired outcomes.
problem Control and learning of molecular Hamiltonians for desired outcomes.
method Differentiable simulations to differentiate Hamiltonians with respect to target observables.
result Control and learning of molecular Hamiltonians for desired outcomes.
Model-data-driven constitutive responses: application to a multiscale computational frameworkstat.ML
A hybrid method combines model-based and data-driven approaches for multiscale constitutive responses.
problem High computational costs and inaccuracies in nonlinear multiscale methods.
method Hybrid methodology combining model-based constitutive laws, data-driven corrections, and computational multiscale approaches.
result Model-data-driven approach improves macroscale simulations with similar accuracy and computational cost.
Deep learning improves sleep apnea diagnosis accuracy.
problem Manual sleep expert scoring is tedious, time-consuming, and variable.
method Adapted deep learning method DOSED for automatic sleep event detection in PSG.
result Automatic approach achieved 81% accuracy for sleep apnea severity diagnosis.
Novel approach constructs differential causal networks from EEG data.
problem Difficulty in modeling interactions of thousands of neurons in group comparisons.
method Hierarchical differential dynamic causal nets based on Chen-Fliess expansions.
result Evidence of network functional disruptions in epileptic brains.
Method estimates posterior model for boundary value problems with uncertain constraints.
problem Estimating posterior probability model for stochastic boundary value problems with uncertain constraints.
method Probabilistic learning inference using Kullback-Leibler divergence and MCMC.
result Method successfully estimates posterior probability measure with constraints.
Flocking refers to collective behavior of a large number of interacting entities, where the interactions between discrete individuals produce collective motion on the large scale. We employ an agent-based model to describe the microscopic dynamics of each individual in a flock, and use a fractional PDE to model the evo…
Detect objects from motion without annotations.
problem Weakly supervised object detection.
method Train model on videos of moving objects and negative scenes.
result Detects objects in single images without annotations.
A new method for optimizing hierarchical multi-objective problems.
problem Symmetry and neglect of objective hierarchy in existing multi-objective methods.
method Priority-Constrained Descent (PCD) framework exploiting hierarchical objective structures.
result Pareto dominance and better per-objective performance with secondary progress guarantees.
LORL learns object-centric representations from vision and language.
problem Learning disentangled, object-centric scene representations from vision and language.
method LORL integrates unsupervised object discovery and segmentation with language input to learn object-centric concepts.
result LORL improves unsupervised object discovery methods and aids downstream tasks.
The paper introduces a new metric to quantify uncertainty's impact on multiple objectives.
problem Quantifying the impact of uncertainty on multiple objectives in complex systems.
method Proposes the mean multi-objective cost of uncertainty (multi-objective MOCU) to quantify uncertainty.
result Demonstrates the effectiveness of the multi-objective MOCU in real-world applications.
A new Adamize method improves multi-objective recommender systems.
problem Improving recommendation systems with multiple conflicting objectives.
method Developed a multi-objective model-agnostic Adamize method that corrects and stabilizes gradients.
result Significant improvements in recommendation systems, measured by hypervolume, coverage, and spacing.
The paper proposes a framework to reason about object dynamics for faster reinforcement learning.
problem Current reinforcement learning approaches lack prior knowledge about the environment, limiting efficiency.
method Integrates object dynamics and behavior into reinforcement learning to improve efficiency.
result Demonstrates the need for reasoning about object behavior and dynamics, leading to faster learning.
ROOTS learns to represent and render 3D scenes with object-centric models.
problem Learning to represent and render 3D scenes with object-centric compositionality.
method Probabilistic generative model for learning object representations and scene rendering from partial observations.
result The model can infer 3D object representations and render scenes from arbitrary viewpoints.
Object-centric learning improves generalization and robustness in multi-object scenes.
problem Improving generalization and robustness in neural networks for scenes with multiple objects.
method Training state-of-the-art unsupervised models on multi-object datasets and evaluating segmentation metrics and downstream tasks.
result Object-centric representations are useful for downstream tasks and generally robust to most distribution shifts affecting objects, but less so for less structured shifts.
ADS explains object differences by quantifying and removing underlying properties.
problem Explaining differences between two object images.
method Align-Deform-Subtract (ADS) framework that uses semantic alignments and iterative quantification/removal of differences.
result ADS provides disentangled error measures explaining object differences in terms of underlying properties.
Proposes class-agnostic object detection to handle all objects without class labels.
problem Difficulty and cost in creating annotated datasets limit conventional object detection models to specific object types.
method Proposes class-agnostic object detection as a new problem and proposes training and evaluation protocols. Uses adversarial learning to exclude class-specific information.
result Adversarial learning improves class-agnostic detection efficacy.
Many objective Bayesian optimization tackles redundant objectives in expensive black-box functions.
problem Efficiently optimizing multiple expensive and noisy black-box functions with redundant objectives.
method Proposes a metric to identify redundant objectives and a Bayesian optimization algorithm to stop evaluating them.
result Reduces computational cost by stopping evaluation of redundant objectives, improving efficiency.
The paper introduces a new method to measure the shape relations between biological objects using r-parallel sets.
problem The influence of neighboring objects on the shape and function of biological objects.
method The authors develop a theory based on spatial point processes to measure the geometrical interaction between objects.
result The proposed measures provide detailed information about the shape of individual objects and their interactions.
SCALOR learns scalable object representations for crowded scenes.
problem Scalability in scenes with many objects.
method Spatially-parallel attention and proposal-rejection mechanisms.
result SCALOR can handle up to a hundred objects in crowded scenes.
New architecture tracks objects in cluttered scenes without supervision.
problem Learning to track objects in large scenes with many objects.
method Spatially invariant computations and representations.
result Outperforms competing methods in tracking objects in cluttered scenes.
We introduce a rich model for multi-objective clustering with lexicographic ordering over objectives and a slack. The slack denotes the allowed multiplicative deviation from the optimal objective value of the higher priority objective to facilitate improvement in lower-priority objectives. We then propose an algorithm …
Paper presents attacks on real-time object detection systems.
problem Adversarial attacks on real-time object detection systems.
method Three targeted adversarial Objectness Gradient attacks (TOG).
result Adversarial attacks can cause object-vanishing, object-fabrication, and object-mislabeling.
A new method for faster multi-objective optimization by evaluating objectives separately.
problem Finding the Pareto front of trade-offs between multiple objectives efficiently.
method Knowledge Gradient with decoupled evaluations, accounting for different costs.
result The method significantly outperforms existing approaches in terms of evaluation cost.
Top 8 robotic vision systems tackled lifelong object recognition challenges.
problem Lifelong learning in robotic vision for varied, dynamic environments.
method Design of a dataset with diverse conditions and rules for evaluation.
result Robotic vision systems improved over time with dynamic object appearances.
Generative models create indistinguishable adversarial objects for object detection.
problem Creating unrestricted adversarial examples for object detection.
method Search over latent space of GAN for adversarial objects.
result Generated adversarial objects are indistinguishable from non-adversarial objects and transferable.
Physical patches can fool object detectors anywhere in an image.
problem Physical adversarial attacks on object detection systems.
method Demonstrated a patch that suppresses all detected objects regardless of its position.
result A patch can be placed anywhere in an image to suppress all detected objects, including those far away.
Scalable approach for object pose estimation across domains.
problem Object pose estimation across different datasets and models.
method Multi-path learning: shared encoder, object-specific decoders.
result Generalizes well from synthetic to real data and across various instances.
Bayesian nonparametric modeling for predicting dynamic dependencies in multiple object trackingcs.LG
Bayesian methods improve tracking multiple objects through dynamic dependencies.
problem Tracking multiple objects with time-varying cardinality and unordered measurements.
method Employing Bayesian nonparametric models, specifically dependent Dirichlet and Pitman-Yor processes, for state estimation and Monte Carlo sampling for trajectory learning.
result The proposed methods outperform existing algorithms in estimating the time-varying number of objects and identifying object associations.
Unified information-theoretic objectives for training deep neural networks.
problem Difficulty in computing information-theoretic quantities for large deep neural networks.
method Review and unify competing objectives, develop surrogate objectives.
result Surrogate objectives allow applying information bottleneck to modern neural network architectures.
BFPM improves machine learning accuracy by considering object types and memberships flexibly.
problem Inaccurate learning due to missing important parameters.
method BFPM relaxes fuzzy and possibilistic constraints, allowing arbitrary object memberships and movement analysis.
result BFPM enhances object movement analysis and provides flexible search space.
New framework converts multi-objective to single-objective optimisation.
problem Solving multi-objective optimisation problems.
method Formalises scalarisation into mathematical framework, uses R2 utilities.
result R2 utilities are monotone and submodular, optimised by greedy algorithms.
Generative model separates objects, positions, and appearances in images.
problem Generating complete images with accurate object positions and appearances.
method Structured latent representation that separates objects, positions, and appearances.
result Generative model can infer object decompositions and accurate depth orderings.
Paper introduces an efficient comparison operator for robust multi-objective optimization with uncertain objectives.
problem Optimizing with uncertain objectives in multi-objective problems.
method Empirical approach to compare solutions with arbitrary distributions of uncertain objectives.
result Higher optimization quality achieved at lower overheads compared to existing techniques.
New method improves domain generalization by matching object representations.
problem Existing domain generalization methods fail to generalize to unseen domains.
method Proposes matching-based algorithms to match object representations across domains.
result MatchDG algorithm matches ground-truth object representations and improves out-of-domain accuracy.
Proposes a novel algorithm for multi-objective reinforcement learning.
problem Challenges in setting numerical preferences for objectives in different units and scales.
method Learn action distributions for each objective and use supervised learning to fit a parametric policy.
result Demonstrates effectiveness on robotics tasks, allowing tracing out the space of nondominated solutions.
We consider the problem of learning object arrangements in a 3D scene. The key idea here is to learn how objects relate to human poses based on their affordances, ease of use and reachability. In contrast to modeling object-object relationships, modeling human-object relationships scales linearly in the number of objec…
Bayesian nonparametric models improve multi-object tracking robustness and flexibility.
problem Challenges in multi-object tracking, including time-dependent cardinality, unordered measurements, and object labeling.
method Robust nonparametric methods for state prior modeling, incorporating dynamic object birth/death and survival.
result Proposed models estimate time-dependent object cardinality, provide object labeling, and identify associated measurements.
This paper tackles multilingual speech processing by optimizing conflicting objectives hierarchically.
problem Training models for multilingual, multi-task speech processing is hampered by conflicting objectives.
method Investigates three multi-objective MSP formulations and introduces a lightweight layer-selection mechanism.
result A bi-level recipe outperforms standard flat optimization in state-of-the-art MSP models.
Proposes using logical specifications for multi-objective reinforcement learning to improve agent behavior.
problem Difficulties in controlling reinforcement learning agents and the need for better generalization.
method Uses propositional logic to specify the importance of multiple objectives, encoding these specifications using a recurrent neural network.
result MORL agents parameterized by logical specifications can generalize to novel combinations of objectives and achieve comparable performance.
Simple greedy algorithms can excel in multi-objective bandits with multiple good arms.
problem Optimizing multiple objectives in bandits is traditionally harder.
method Introduced greedy algorithms that exploit multiple good arms for multiple objectives.
result Simple greedy algorithms achieve strong performance in multi-objective bandits.
GENESIS-V2 infers unordered object representations without iterative refinement.
problem Unsupervised learning of unordered object representations for complex images.
method Stochastic stick-breaking process for clustering pixel embeddings.
result GENESIS-V2 outperforms recent baselines in unsupervised image segmentation and scene generation.