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Found 45 papers, 19 papers with code
From Time-inconsistency to Time-consistency for Optimal Stopping Problems
In particular, when in a repeated experiment the naive agent can observe her actual sequence of actions which are inconsistent with what she has planned at the initial time, she then chooses her immediate action based on the observations on her later actual behavior.
FlexiDreamer: Single Image-to-3D Generation with FlexiCubes
However, due to the challenge of directly deforming the mesh representation to approach the target topology, most methodologies learn an implicit representation (such as NeRF) during the sparse-view reconstruction and acquire the target mesh by a post-processing extraction.
On Diffusion Process in SE(3)-invariant Space
Sampling viable 3D structures (e. g., molecules and point clouds) with SE(3)-invariance using diffusion-based models proved promising in a variety of real-world applications, wherein SE(3)-invariant properties can be naturally characterized by the inter-point distance manifold.
Multi-Constraint Safe RL with Objective Suppression for Safety-Critical Applications
Safe reinforcement learning tasks with multiple constraints are a challenging domain despite being very common in the real world.
MatPlotAgent: Method and Evaluation for LLM-Based Agentic Scientific Data Visualization
Scientific data visualization plays a crucial role in research by enabling the direct display of complex information and assisting researchers in identifying implicit patterns.
Sequence adaptive field-imperfection estimation (SAFE): retrospective estimation and correction of $B_1^+$ and $B_0$ inhomogeneities for enhanced MRF quantification
$B_1^+$ and $B_0$ field-inhomogeneities can significantly reduce accuracy and robustness of MRF's quantitative parameter estimates.
On Accelerating Diffusion-based Molecular Conformation Generation in SE(3)-invariant Space
Diffusion-based generative models in SE(3)-invariant space have demonstrated promising performance in molecular conformation generation, but typically require solving stochastic differential equations (SDEs) with thousands of update steps.
Model-Free, Regret-Optimal Best Policy Identification in Online CMDPs
PRI achieves trio objectives: (i) PRI is a model-free algorithm; and (ii) it outputs an approximately optimal policy with a high probability at the end of learning; and (iii) PRI guarantees $\tilde{\mathcal{O}}(H\sqrt{K})$ regret and constraint violation, which significantly improves the best existing regret bound $\tilde{\mathcal{O}}(H^4 \sqrt{SA}K^{\frac{4}{5}})$ under a model-free algorithm, where $H$ is the length of each episode, $S$ is the number of states, $A$ is the number of actions, and the total number of episodes during learning is $2K+\tilde{\cal O}(K^{0. 25}).$ We further present a matching lower via an example that shows under any online learning algorithm, there exists a well-separated CMDP instance such that either the regret or violation has to be $\Omega(H\sqrt{K}),$ which matches the upper bound by a polylogarithmic factor.
NeRF-Enhanced Outpainting for Faithful Field-of-View Extrapolation
In various applications, such as robotic navigation and remote visual assistance, expanding the field of view (FOV) of the camera proves beneficial for enhancing environmental perception.
Molecular Conformation Generation via Shifting Scores
Molecular conformation generation, a critical aspect of computational chemistry, involves producing the three-dimensional conformer geometry for a given molecule.
PlankAssembly: Robust 3D Reconstruction from Three Orthographic Views with Learnt Shape Programs
In this paper, we develop a new method to automatically convert 2D line drawings from three orthographic views into 3D CAD models.
Scene Separation & Data Selection: Temporal Segmentation Algorithm for Real-Time Video Stream Analysis
We present 2SDS (Scene Separation and Data Selection algorithm), a temporal segmentation algorithm used in real-time video stream interpretation.
Learning Achievement Structure for Structured Exploration in Domains with Sparse Reward
We propose Structured Exploration with Achievements (SEA), a multi-stage reinforcement learning algorithm designed for achievement-based environments, a particular type of environment with an internal achievement set.
Learning to Decouple Complex Systems
In this paper, we propose a sequential learning approach under this setting by decoupling a complex system for handling irregularly sampled and cluttered sequential observations.
Deep Learning Assisted Optimization for 3D Reconstruction from Single 2D Line Drawings
In this paper, we revisit the long-standing problem of automatic reconstruction of 3D objects from single line drawings.
End-to-end Graph-constrained Vectorized Floorplan Generation with Panoptic Refinement
In the first stage, we encode the room connectivity graph input by users with a graph convolutional network (GCN), then apply an autoregressive transformer network to generate an initial floorplan sequence.
Continuously Discovering Novel Strategies via Reward-Switching Policy Optimization
We present Reward-Switching Policy Optimization (RSPO), a paradigm to discover diverse strategies in complex RL environments by iteratively finding novel policies that are both locally optimal and sufficiently different from existing ones.
Neural Face Identification in a 2D Wireframe Projection of a Manifold Object
In computer-aided design (CAD) systems, 2D line drawings are commonly used to illustrate 3D object designs.
Lerna: Transformer Architectures for Configuring Error Correction Tools for Short- and Long-Read Genome Sequencing
We also show that the alignment rate and assembly quality computed for the corrected reads are strongly negatively correlated with the perplexity, enabling the automated selection of k-mer values for better error correction, and hence, improved assembly quality.
Deep Depth from Focus with Differential Focus Volume
Depth-from-focus (DFF) is a technique that infers depth using the focus change of a camera.
Temporal Induced Self-Play for Stochastic Bayesian Games
One practical requirement in solving dynamic games is to ensure that the players play well from any decision point onward.
Towards Robust Human Trajectory Prediction in Raw Videos
Human trajectory prediction has received increased attention lately due to its importance in applications such as autonomous vehicles and indoor robots.
Model-based Synthetic Data-driven Learning (MOST-DL): Application in Single-shot T2 Mapping with Severe Head Motion Using Overlapping-echo Acquisition
Use of synthetic data has provided a potential solution for addressing unavailable or insufficient training samples in deep learning-based magnetic resonance imaging (MRI).
Data-Driven Distributed State Estimation and Behavior Modeling in Sensor Networks
Nowadays, the prevalence of sensor networks has enabled tracking of the states of dynamic objects for a wide spectrum of applications from autonomous driving to environmental monitoring and urban planning.
Learning to Parse Wireframes in Images of Man-Made Environments
To this end, we have built a very large new dataset of over 5, 000 images with wireframes thoroughly labelled by humans.
Probing new physics with multi-vacua quantum tunnelings beyond standard model through gravitational waves
We report on a novel phenomenon of particle cosmology, which features specific cosmological phase transitions via quantum tunnelings through multiple vacua.
Cosmology and Nongalactic Astrophysics General Relativity and Quantum Cosmology High Energy Physics - Phenomenology High Energy Physics - Theory
Superpixel Segmentation with Fully Convolutional Networks
In computer vision, superpixels have been widely used as an effective way to reduce the number of image primitives for subsequent processing.
Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning
In multi-agent games, the complexity of the environment can grow exponentially as the number of agents increases, so it is particularly challenging to learn good policies when the agent population is large.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Neural Wireframe Renderer: Learning Wireframe to Image Translations
To this end, we propose a novel model based on a structure-appearance joint representation learned from both images and wireframes.
Structured3D: A Large Photo-realistic Dataset for Structured 3D Modeling
Recently, there has been growing interest in developing learning-based methods to detect and utilize salient semi-global or global structures, such as junctions, lines, planes, cuboids, smooth surfaces, and all types of symmetries, for 3D scene modeling and understanding.
CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario
The most commonly used open-source traffic simulator SUMO is, however, not scalable to large road network and large traffic flow, which hinders the study of reinforcement learning on traffic scenarios.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Single-Image Piece-wise Planar 3D Reconstruction via Associative Embedding
In the first stage, we train a CNN to map each pixel to an embedding space where pixels from the same plane instance have similar embeddings.
Ranked #1 on
Plane Instance Segmentation
on NYU Depth v2
Image Based Review Text Generation with Emotional Guidance
Therefore, we aimed at a new field concerning generating review text from customers based on images together with the ratings of online shopping products, which appear as non-image attributes.
Recovering 3D Planes from a Single Image via Convolutional Neural Networks
In this paper, we study the problem of recovering 3D planar surfaces from a single image of man-made environment.
Multi-Scale FCN With Cascaded Instance Aware Segmentation for Arbitrary Oriented Word Spotting in the Wild
Scene text detection has attracted great attention these years.
Smart Library: Identifying Books in a Library using Richly Supervised Deep Scene Text Reading
Physical library collections are valuable and long standing resources for knowledge and learning.
Detecting Dominant Vanishing Points in Natural Scenes with Application to Composition-Sensitive Image Retrieval
To overcome this difficulty, we propose a novel vanishing point detection method that exploits global structures in the scene via contour detection.
Graph Construction with Label Information for Semi-Supervised Learning
In the literature, most existing graph-based semi-supervised learning (SSL) methods only use the label information of observed samples in the label propagation stage, while ignoring such valuable information when learning the graph.
Modeling Photographic Composition via Triangles
The capacity of automatically modeling photographic composition is valuable for many real-world machine vision applications such as digital photography, image retrieval, image understanding, and image aesthetics assessment.
Single-Sample Face Recognition with Image Corruption and Misalignment via Sparse Illumination Transfer
To compensate the missing illumination information typically provided by multiple training images, a sparse illumination transfer (SIT) technique is introduced.
Plane-Based Content Preserving Warps for Video Stabilization
Recently, a new image deformation technique called content-preserving warping (CPW) has been successfully employed to produce the state-of-the-art video stabilization results in many challenging cases.
Sparsity and Robustness in Face Recognition
This report concerns the use of techniques for sparse signal representation and sparse error correction for automatic face recognition.
Fast L1-Minimization Algorithms For Robust Face Recognition
L1-minimization refers to finding the minimum L1-norm solution to an underdetermined linear system b=Ax.
Fast L1-Minimization Algorithms For Robust Face Recognition
L1-minimization refers to finding the minimum L1-norm solution to an underdetermined linear system b=Ax.
Stable Principal Component Pursuit
We further prove that the solution to a related convex program (a relaxed PCP) gives an estimate of the low-rank matrix that is simultaneously stable to small entrywise noise and robust to gross sparse errors.
Information Theory Information Theory
