Search Results for author:
Found 32 papers, 23 papers with code
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Adversarial Cross-Modal Retrieval via Learning and Transferring Single-Modal Similarities
Cross-modal retrieval aims to retrieve relevant data across different modalities (e. g., texts vs. images).
L2G Auto-encoder: Understanding Point Clouds by Local-to-Global Reconstruction with Hierarchical Self-Attention
Specifically, L2G-AE employs an encoder to encode the geometry information of multiple scales in a local region at the same time.
Point2SpatialCapsule: Aggregating Features and Spatial Relationships of Local Regions on Point Clouds using Spatial-aware Capsules
Compared to the previous capsule network based methods, the feature routing on the spatial-aware capsules can learn more discriminative spatial relationships among local regions for point clouds, which establishes a direct mapping between log priors and the spatial locations through feature clusters.
Point Cloud Completion by Skip-attention Network with Hierarchical Folding
Point cloud completion aims to infer the complete geometries for missing regions of 3D objects from incomplete ones.
Distilling Visual Priors from Self-Supervised Learning
Convolutional Neural Networks (CNNs) are prone to overfit small training datasets.
Temporal Context Aggregation for Video Retrieval with Contrastive Learning
The current research focus on Content-Based Video Retrieval requires higher-level video representation describing the long-range semantic dependencies of relevant incidents, events, etc.
Ranked #6 on
Video Retrieval
on FIVR-200K
PMP-Net: Point Cloud Completion by Learning Multi-step Point Moving Paths
As a result, the network learns a strict and unique correspondence on point-level, which can capture the detailed topology and structure relationships between the incomplete shape and the complete target, and thus improves the quality of the predicted complete shape.
Cycle4Completion: Unpaired Point Cloud Completion using Cycle Transformation with Missing Region Coding
We provide a comprehensive evaluation in experiments, which shows that our model with the learned bidirectional geometry correspondence outperforms state-of-the-art unpaired completion methods.
SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer
However, previous methods usually suffered from discrete nature of point cloud and unstructured prediction of points in local regions, which makes it hard to reveal fine local geometric details on the complete shape.
KTNet: Knowledge Transfer for Unpaired 3D Shape Completion
The student network takes the incomplete one as input and restores the corresponding complete shape.
Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and Registration: Methods and Results
Based on the MVP dataset, this paper reports methods and results in the Multi-View Partial Point Cloud Challenge 2021 on Completion and Registration.
Snowflake Point Deconvolution for Point Cloud Completion and Generation with Skip-Transformer
Our insight into the detailed geometry is to introduce a skip-transformer in the SPD to learn the point splitting patterns that can best fit the local regions.
Ranked #5 on
Point Cloud Completion
on ShapeNet
PMP-Net++: Point Cloud Completion by Transformer-Enhanced Multi-step Point Moving Paths
It moves each point of incomplete input to obtain a complete point cloud, where total distance of point moving paths (PMPs) should be the shortest.
Ranked #1 on
Point Cloud Completion
on Completion3D
3D-OAE: Occlusion Auto-Encoders for Self-Supervised Learning on Point Clouds
To address this problem, we present a novel and efficient self-supervised point cloud representation learning framework, named 3D Occlusion Auto-Encoder (3D-OAE), to facilitate the detailed supervision inherited in local regions and global shapes.
Learning Deep Implicit Functions for 3D Shapes with Dynamic Code Clouds
However, the local codes are constrained at discrete and regular positions like grid points, which makes the code positions difficult to be optimized and limits their representation ability.
3D Shape Reconstruction from 2D Images with Disentangled Attribute Flow
Reconstructing 3D shape from a single 2D image is a challenging task, which needs to estimate the detailed 3D structures based on the semantic attributes from 2D image.
Self-Supervised Visual Representation Learning with Semantic Grouping
The semantic grouping is performed by assigning pixels to a set of learnable prototypes, which can adapt to each sample by attentive pooling over the feature and form new slots.
Ranked #15 on
Unsupervised Semantic Segmentation
on COCO-Stuff-27
(Accuracy metric)
Propagating Variational Model Uncertainty for Bioacoustic Call Label Smoothing
We focus on using the predictive uncertainty signal calculated by Bayesian neural networks to guide learning in the self-same task the model is being trained on.
PAI3D: Painting Adaptive Instance-Prior for 3D Object Detection
3D object detection is a critical task in autonomous driving.
Parametric Classification for Generalized Category Discovery: A Baseline Study
Generalized Category Discovery (GCD) aims to discover novel categories in unlabelled datasets using knowledge learned from labelled samples.
Ranked #1 on
Open-World Semi-Supervised Learning
on ImageNet-100
Masked Scene Contrast: A Scalable Framework for Unsupervised 3D Representation Learning
As a pioneering work, PointContrast conducts unsupervised 3D representation learning via leveraging contrastive learning over raw RGB-D frames and proves its effectiveness on various downstream tasks.
Ranked #9 on
Semantic Segmentation
on ScanNet
(val mIoU metric, using extra
training data)
Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery
In this paper, we address the problem of generalized category discovery (GCD), \ie, given a set of images where part of them are labelled and the rest are not, the task is to automatically cluster the images in the unlabelled data, leveraging the information from the labelled data, while the unlabelled data contain images from the labelled classes and also new ones.
Retro-FPN: Retrospective Feature Pyramid Network for Point Cloud Semantic Segmentation
In this way, the categorization of each point is conditioned on its local semantic pattern.
Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training
In contrast, such privilege has not yet fully benefited 3D deep learning, mainly due to the limited availability of large-scale 3D datasets.
Ranked #3 on
3D Semantic Segmentation
on ScanNet200
(using extra training data)
What If the TV Was Off? Examining Counterfactual Reasoning Abilities of Multi-modal Language Models
In light of the advancements in current multi-modal large language models, we explore their effectiveness in counterfactual reasoning.
CoDet: Co-Occurrence Guided Region-Word Alignment for Open-Vocabulary Object Detection
CoDet then leverages visual similarities to discover the co-occurring objects and align them with the shared concept.
Ranked #2 on
Open Vocabulary Object Detection
on LVIS v1.0
(using extra training data)
An Improved Neural Network Model Based On CNN Using For Fruit Sugar Degree Detection
In this paper, we proposed a new evaluation standard derived from dataset standard deviation(STD) for evaluating detection performance, validating the viability of using an artificial neural network model to do fruit sugar degree nondestructive detection.
Online Tensor Inference
Recent technological advances have led to contemporary applications that demand real-time processing and analysis of sequentially arriving tensor data.
Classes Are Not Equal: An Empirical Study on Image Recognition Fairness
Second, with the proposed concept of Model Prediction Bias, we investigate the origins of problematic representation during optimization.
Adaptive Variance Based Label Distribution Learning For Facial Age Estimation
Some existing methods adopt distribution learning to tackle this issue by exploiting the semantic correlation between age labels.
Ranked #6 on
Age Estimation
on MORPH album2 (Caucasian)
俄语网络仇恨言论语料库研究与构建(An Russian Internet Corpus for Hate Speech Detection)
“近年来, 网络科技的飞速发展在为整个社会带来极大便利的同时, 也加剧了仇恨言论的传播。仇恨言论可能会构成网络暴力, 诱发仇恨性的犯罪行为, 对社会公共文明和网络空间秩序造成极大的威胁。因此, 对网络仇恨言论进行主动的监管和制约具有重大意义。而当前学术界针对俄语的网络仇恨言论研究不足, 尤其缺乏俄语网络仇恨言论语料库, 这极大地限制了相关技术和应用的发展。2022年俄乌冲突爆发以后, 对于俄语网络仇恨言论语料库的研究与构建显得更加迫切。在本文中, 作者提出了一种细粒度的俄语网络仇恨言论语料库构建及标注方案, 并基于该方案首次创建了包含20476条文本数据, 具有针对性、话题统一的俄语仇恨性言论语料库。”
