Search Results for author:
Found 29 papers, 11 papers with code
PlugNet: Degradation Aware Scene Text Recognition Supervised by a Pluggable Super-Resolution Unit
In this paper, we address the problem of recognizing degradation images that are suffering from high blur or low-resolution.
MonoDiff9D: Monocular Category-Level 9D Object Pose Estimation via Diffusion Model
Our motivation is to leverage the probabilistic nature of diffusion models to alleviate the need for shape priors, CAD models, or depth sensors for intra-class unknown object pose estimation.
Novel Object 6D Pose Estimation with a Single Reference View
Using only a single reference view is more scalable, but challenging due to large pose discrepancies and limited geometric and spatial information.
Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation
Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation.
RegD: Hierarchical Embeddings via Distances over Geometric Regions
Hierarchical data are common in many domains like life sciences and e-commerce, and their embeddings often play a critical role.
An Efficient Diffusion-based Non-Autoregressive Solver for Traveling Salesman Problem
To enhance the solution quality while maintaining fast inference, we propose DEITSP, a diffusion model with efficient iterations tailored for TSP that operates in a NAR manner.
OOD-HOI: Text-Driven 3D Whole-Body Human-Object Interactions Generation Beyond Training Domains
Generating realistic 3D human-object interactions (HOIs) from text descriptions is a active research topic with potential applications in virtual and augmented reality, robotics, and animation.
TransBox: EL++-closed Ontology Embedding
OWL (Web Ontology Language) ontologies, which are able to represent both relational and type facts as standard knowledge graphs and complex domain knowledge in Description Logic (DL) axioms, are widely adopted in domains such as healthcare and bioinformatics.
Alleviating Over-Smoothing via Aggregation over Compact Manifolds
To this end, we propose the aggregation over compacted manifolds method (ACM) that replaces the existing information aggregation with aggregation over compact manifolds, a special type of manifold, which avoids contracted aggregations.
Deep Learning-Based Object Pose Estimation: A Comprehensive Survey
Object pose estimation is a fundamental computer vision problem with broad applications in augmented reality and robotics.
StableMoFusion: Towards Robust and Efficient Diffusion-based Motion Generation Framework
The effect of the design of each component is still unclear.
MetaF2N: Blind Image Super-Resolution by Learning Efficient Model Adaptation from Faces
To evaluate our proposed MetaF2N, we have collected a real-world low-quality dataset with one or multiple faces in each image, and our MetaF2N achieves superior performance on both synthetic and real-world datasets.
Auto-GPT for Online Decision Making: Benchmarks and Additional Opinions
In this paper, we present a comprehensive benchmark study of Auto-GPT styled agents in decision-making tasks that simulate real-world scenarios.
Efficient Computation of General Modules for ALC Ontologies (Extended Version)
Our evaluation indicates that our general modules are often smaller than classical modules and uniform interpolants computed by the state-of-the-art, and compared with uniform interpolants, can be computed in a significantly shorter time.
Automatic Detection of Industry Sectors in Legal Articles Using Machine Learning Approaches
The experimental results show that the suggested automated industry analysis which employs ML techniques allows the processing of large collections of text data in an easy, efficient, and scalable way.
Deep Learning Neural Networks for Emotion Classification from Text: Enhanced Leaky Rectified Linear Unit Activation and Weighted Loss
The proposed solution improves accuracy and reduces the processing time of text emotion classification.
Lessons from the AdKDD'21 Privacy-Preserving ML Challenge
Designing data sharing mechanisms providing performance and strong privacy guarantees is a hot topic for the Online Advertising industry.
Uncertainty Detection and Reduction in Neural Decoding of EEG Signals
In this work, we proposed an uncertainty estimation and reduction model (UNCER) to quantify and mitigate the uncertainty during the EEG decoding process.
Union and Intersection of all Justifications
We present new algorithm for computing the union and intersection of all justifications for a given ontological consequence without first computing the set of all justifications.
Inflection Point: A New Perspective on Photonic Nanojets
When light propagates through the edge or middle part of microparticle's incoming interface, there is a basic rule that light converges and diverges rapidly or slowly at the output port.
Optics
Robust Protection of III-V Nanowires in Water Splitting by a Thin Compact TiO$_2$ Layer
Working as a photocathode for water splitting, they exhibited a 45% larger photocurrent density compared with un-protected counterparts and a high Faraday efficiency of 91%, and can also maintain a record-long highly-stable performance among narrow-bandgap III-V NW photoelectrodes; after 67 hours photoelectrochemical stability test reaction in strong acid electrolyte solution (pH = 1), they show no apparent indication of corrosion, which is in stark contrast to the un-protected NWs that are fully failed after 35-hours.
Mesoscale and Nanoscale Physics Chemical Physics
Deep Retrieval: Learning A Retrievable Structure for Large-Scale Recommendations
With the model learnt, a beam search over the structure is performed to retrieve the top candidates for reranking.
BLCS: Brain-Like based Distributed Control Security in Cyber Physical Systems
To deal with this issue, we propose a brain-like based distributed control security (BLCS) architecture for F-RON in CPS, by introducing a brain-like security (BLS) scheme.
Image Dehazing using Bilinear Composition Loss Function
In this paper, we introduce a bilinear composition loss function to address the problem of image dehazing.
Analytic Feature Selection for Support Vector Machines
Our algorithm is based on logistic and linear regression models using these six geometric properties as predictor variables.
