Search Results for author:
Found 35 papers, 12 papers with code
Learning Pairwise Inter-Plane Relations for Piecewise Planar Reconstruction
This paper proposes a novel single-image piecewise planar reconstruction technique that infers and enforces inter-plane relationships.
DPA-Net: Structured 3D Abstraction from Sparse Views via Differentiable Primitive Assembly
We present a differentiable rendering framework to learn structured 3D abstractions in the form of primitive assemblies from sparse RGB images capturing a 3D object.
Harnessing the Power of Large Language Model for Uncertainty Aware Graph Processing
To equip the graph processing with both high accuracy and explainability, we introduce a novel approach that harnesses the power of a large language model (LLM), enhanced by an uncertainty-aware module to provide a confidence score on the generated answer.
SEER: Facilitating Structured Reasoning and Explanation via Reinforcement Learning
Moreover, existing reinforcement learning (RL) based methods overlook the structured relationships, underutilizing the potential of RL in structured reasoning.
MPrompt: Exploring Multi-level Prompt Tuning for Machine Reading Comprehension
The large language models have achieved superior performance on various natural language tasks.
TCRA-LLM: Token Compression Retrieval Augmented Large Language Model for Inference Cost Reduction
Our summarization compression can reduce 65% of the retrieval token size with further 0. 3% improvement on the accuracy; semantic compression provides a more flexible way to trade-off the token size with performance, for which we can reduce the token size by 20% with only 1. 6% of accuracy drop.
Unsupervised Deep Cross-Language Entity Alignment
We outperformed the state-of-the-art method in unsupervised and semi-supervised categories.
Point-TTA: Test-Time Adaptation for Point Cloud Registration Using Multitask Meta-Auxiliary Learning
This could be sub-optimal since it is difficult for the same model to handle all the variations during testing.
Test-Time Adaptation for Point Cloud Upsampling Using Meta-Learning
During meta-testing, the trained model is fine-tuned with a few gradient updates to produce a unique set of network parameters for each test instance.
IncreLoRA: Incremental Parameter Allocation Method for Parameter-Efficient Fine-tuning
This approach is different from the pruning method as it is not limited by the initial number of training parameters, and each parameter matrix has a higher rank upper bound for the same training overhead.
RoSI: Recovering 3D Shape Interiors from Few Articulation Images
The dominant majority of 3D models that appear in gaming, VR/AR, and those we use to train geometric deep learning algorithms are incomplete, since they are modeled as surface meshes and missing their interior structures.
Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification
Failure to recognize samples from the classes unseen during training is a major limitation of artificial intelligence in the real-world implementation for recognition and classification of retinal anomalies.
Learning to Recover Spectral Reflectance from RGB Images
Instead of relying on naive end-to-end training, we also propose a novel architecture that integrates the physical relationship between the spectral reflectance and the corresponding RGB images into the network based on our mathematical analysis.
Federated Uncertainty-Aware Aggregation for Fundus Diabetic Retinopathy Staging
Our TWEU employs an evidential deep layer to produce the uncertainty score with the DR staging results for client reliability evaluation.
Inference Time Evidences of Adversarial Attacks for Forensic on Transformers
Vision Transformers (ViTs) are becoming a very popular paradigm for vision tasks as they achieve state-of-the-art performance on image classification.
Reliable Federated Disentangling Network for Non-IID Domain Feature
To the best of our knowledge, our proposed RFedDis is the first work to develop an FL approach based on evidential uncertainty combined with feature disentangling, which enhances the performance and reliability of FL in non-IID domain features.
HAL3D: Hierarchical Active Learning for Fine-Grained 3D Part Labeling
We present the first active learning tool for fine-grained 3D part labeling, a problem which challenges even the most advanced deep learning (DL) methods due to the significant structural variations among the small and intricate parts.
A Reliable Online Method for Joint Estimation of Focal Length and Camera Rotation
Linear perspectivecues deriving from regularities of the built environment can be used to recalibrate both intrinsic and extrinsic camera parameters online, but these estimates can be unreliable due to irregularities in the scene, uncertainties in line segment estimation and background clutter.
Structured Light with Redundancy Codes
We propose three applications using our redundancy codes: (1) Self error-correction for SL imaging under strong ambient light, (2) Error detection for adaptive reconstruction under global illumination, and (3) Interference filtering with device-specific projection sequence encoding, especially for event camera-based SL and light curtain devices.
Glass Segmentation with RGB-Thermal Image Pairs
Due to the large difference between the transmission property of visible light and that of the thermal energy through the glass where most glass is transparent to the visible light but opaque to thermal energy, glass regions of a scene are made more distinguishable with a pair of RGB and thermal images than solely with an RGB image.
HIPA: Hierarchical Patch Transformer for Single Image Super Resolution
Transformer-based architectures start to emerge in single image super resolution (SISR) and have achieved promising performance.
HEAT: Holistic Edge Attention Transformer for Structured Reconstruction
This paper presents a novel attention-based neural network for structured reconstruction, which takes a 2D raster image as an input and reconstructs a planar graph depicting an underlying geometric structure.
Edge Classification
Extracting Buildings In Remote Sensing Images
+1
Built Year Prediction from Buddha Face with Heterogeneous Labels
Buddha statues are a part of human culture, especially of the Asia area, and they have been alongside human civilisation for more than 2, 000 years.
Human Pose and Shape Estimation from Single Polarization Images
This paper focuses on a new problem of estimating human pose and shape from single polarization images.
Fusion-DHL: WiFi, IMU, and Floorplan Fusion for Dense History of Locations in Indoor Environments
The paper proposes a multi-modal sensor fusion algorithm that fuses WiFi, IMU, and floorplan information to infer an accurate and dense location history in indoor environments.
Roof-GAN: Learning to Generate Roof Geometry and Relations for Residential Houses
This paper presents Roof-GAN, a novel generative adversarial network that generates structured geometry of residential roof structures as a set of roof primitives and their relationships.
3D Human Shape Reconstruction from a Polarization Image
Inspired by the recent advances in human shape estimation from single color images, in this paper, we attempt at estimating human body shapes by leveraging the geometric cues from single polarization images.
Polarization Human Shape and Pose Dataset
Polarization images are known to be able to capture polarized reflected lights that preserve rich geometric cues of an object, which has motivated its recent applications in reconstructing detailed surface normal of the objects of interest.
MCMLSD: A Probabilistic Algorithm and Evaluation Framework for Line Segment Detection
Traditional approaches to line segment detection typically involve perceptual grouping in the image domain and/or global accumulation in the Hough domain.
Simultaneous 3D Reconstruction for Water Surface and Underwater Scene
This paper presents the first approach for simultaneously recovering the 3D shape of both the wavy water surface and the moving underwater scene.
Full 3D Reconstruction of Transparent Objects
Numerous techniques have been proposed for reconstructing 3D models for opaque objects in past decades.
Stereo-Based 3D Reconstruction of Dynamic Fluid Surfaces by Global Optimization
3D reconstruction of dynamic fluid surfaces is an open and challenging problem in computer vision.
MCMLSD: A Dynamic Programming Approach to Line Segment Detection
Prior approaches to line segment detection typically involve perceptual grouping in the image domain or global accumulation in the Hough domain.
Ranked #8 on
Line Segment Detection
on York Urban Dataset
3D Reconstruction of Transparent Objects With Position-Normal Consistency
Estimating the shape of transparent and refractive objects is one of the few open problems in 3D reconstruction.
Frequency-Based Environment Matting by Compressive Sensing
Extracting environment mattes using existing approaches often requires either thousands of captured images or a long processing time, or both.
