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Found 13 papers, 9 papers with code
Anomaly Detection by Adapting a pre-trained Vision Language Model
Recently, large vision and language models have shown their success when adapting them to many downstream tasks.
A Discrepancy Aware Framework for Robust Anomaly Detection
To alleviate this issue, we present a Discrepancy Aware Framework (DAF), which demonstrates robust performance consistently with simple and cheap strategies across different anomaly detection benchmarks.
Multi-Modal 3D Object Detection by Box Matching
Extensive experiments on the nuScenes dataset demonstrate that our method is much more stable in dealing with challenging cases such as asynchronous sensors, misaligned sensor placement, and degenerated camera images than existing fusion methods.
Ranked #47 on
3D Object Detection
on nuScenes
PRA-Net: Point Relation-Aware Network for 3D Point Cloud Analysis
Learning intra-region contexts and inter-region relations are two effective strategies to strengthen feature representations for point cloud analysis.
Ranked #40 on
3D Point Cloud Classification
on ModelNet40
LATFormer: Locality-Aware Point-View Fusion Transformer for 3D Shape Recognition
To investigate this, we propose a novel Locality-Aware Point-View Fusion Transformer (LATFormer) for 3D shape retrieval and classification.
LeViT-UNet: Make Faster Encoders with Transformer for Medical Image Segmentation
Medical image segmentation plays an essential role in developing computer-assisted diagnosis and therapy systems, yet still faces many challenges.
Discovering Hypernymy in Text-Rich Heterogeneous Information Network by Exploiting Context Granularity
Extensive experiments on two large real-world datasets demonstrate the effectiveness of HyperMine and the utility of modeling context granularity.
View N-gram Network for 3D Object Retrieval
By doing so, spatial information across multiple views is captured, which helps to learn a discriminative global embedding for each 3D object.
Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series
Gaussian processes (GP) for machine learning have been studied systematically over the past two decades and they are by now widely used in a number of diverse applications.
User-Guided Clustering in Heterogeneous Information Networks via Motif-Based Comprehensive Transcription
We therefore approach the problem of user-guided clustering in HINs with network motifs.
Entity Set Search of Scientific Literature: An Unsupervised Ranking Approach
Different from Web or general domain search, a large portion of queries in scientific literature search are entity-set queries, that is, multiple entities of possibly different types.
Triplet-Center Loss for Multi-View 3D Object Retrieval
Most existing 3D object recognition algorithms focus on leveraging the strong discriminative power of deep learning models with softmax loss for the classification of 3D data, while learning discriminative features with deep metric learning for 3D object retrieval is more or less neglected.
mvn2vec: Preservation and Collaboration in Multi-View Network Embedding
With experiments on a series of synthetic datasets, a large-scale internal Snapchat dataset, and two public datasets, we confirm the validity and importance of preservation and collaboration as two objectives for multi-view network embedding.
