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Found 8 papers, 0 papers with code
3DViewGraph: Learning Global Features for 3D Shapes from A Graph of Unordered Views with Attention
Then, the content and spatial information of each pair of view nodes are encoded by a novel spatial pattern correlation, where the correlation is computed among latent semantic patterns.
Efficient Outdoor 3D Point Cloud Semantic Segmentation for Critical Road Objects and Distributed Contexts
Large-scale point cloud semantic understanding is an important problem in self-driving cars and autonomous robotics navigation.
Improving Conversational Recommendation System by Pretraining on Billions Scale of Knowledge Graph
We first construct a billion-scale conversation knowledge graph (CKG) from information about users, items and conversations, and then pretrain CKG by introducing knowledge graph embedding method and graph convolution network to encode semantic and structural information respectively. To make the CTR prediction model sensible of current state of users and the relationship between dialogues and items, we introduce user-state and dialogue-interaction representations based on pre-trained CKG and propose K-DCN. In K-DCN, we fuse the user-state representation, dialogue-interaction representation and other normal feature representations via deep cross network, which will give the rank of candidate items to be recommended. We experimentally prove that our proposal significantly outperforms baselines and show it's real application in Alime.
Complexity-Optimized Sparse Bayesian Learning for Scalable Classification Tasks
Sparse Bayesian Learning (SBL) constructs an extremely sparse probabilistic model with very competitive generalization.
Persistent Homology Based Graph Convolution Network for Fine-Grained 3D Shape Segmentation
Current deep learning and graph machine learning methods fail to tackle such challenges and thus provide inferior performance in fine-grained 3D analysis.
Single-Stage Broad Multi-Instance Multi-Label Learning (BMIML) with Diverse Inter-Correlations and its application to medical image classification
Existing MIML methods are useful in many applications but most of which suffer from relatively low accuracy and training efficiency due to several issues: i) the inter-label correlations(i. e., the probabilistic correlations between the multiple labels corresponding to an object) are neglected; ii) the inter-instance correlations (i. e., the probabilistic correlations of different instances in predicting the object label) cannot be learned directly (or jointly) with other types of correlations due to the missing instance labels; iii) diverse inter-correlations (e. g., inter-label correlations, inter-instance correlations) can only be learned in multiple stages.
Graph based Label Enhancement for Multi-instance Multi-label learning
Multi-instance multi-label (MIML) learning is widely applicated in numerous domains, such as the image classification where one image contains multiple instances correlated with multiple logic labels simultaneously.
Boosting Few-Shot Semantic Segmentation Via Segment Anything Model
To avoid predicting wrong masks with SAM, we propose a prediction result selection (PRS) algorithm.
