Search Results for author:
Found 15 papers, 6 papers with code
Meta-RangeSeg: LiDAR Sequence Semantic Segmentation Using Multiple Feature Aggregation
Furthermore, Feature Aggregation Module (FAM) aggregates the meta features and multi-scale features, which tends to strengthen the role of range channel.
Ranked #7 on
3D Semantic Segmentation
on SemanticKITTI
Learning Representation from Neural Fisher Kernel with Low-rank Approximation
We show that the low-rank approximation of NFKs derived from unsupervised generative models and supervised learning models gives rise to high-quality compact representations of data, achieving competitive results on a variety of machine learning tasks.
A Dot Product Attention Free Transformer
We introduce Dot Product Attention Free Transformer (DAFT), an efficient variant of Transformers \citep{transformer} that eliminates the query-key dot product in self attention.
An Attention Free Transformer
We introduce Attention Free Transformer (AFT), an efficient variant of Transformers that eliminates the need for dot product self attention.
RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation
The voxel-based view is regular, but sparse, and computation grows cubically when voxel resolution increases.
A nonabelian Brunn-Minkowski inequality
Henstock and Macbeath asked in 1953 whether the Brunn-Minkowski inequality can be generalized to nonabelian locally compact groups; questions in the same line were also asked by Hrushovski, McCrudden, and Tao.
Group Theory Classical Analysis and ODEs Combinatorics Functional Analysis Metric Geometry 22D05, 43A05, 49Q20, 60B15, 05D99
Tiny Object Detection in Aerial Images
To build a benchmark for tiny object detection in aerial images, we evaluate the state-of-the-art object detectors on our AI-TOD dataset.
Ranked #3 on
Object Detection
on AI-TOD
Improving unsupervised anomaly localization by applying multi-scale memories to autoencoders
Autoencoder and its variants have been widely applicated in anomaly detection. The previous work memory-augmented deep autoencoder proposed memorizing normality to detect anomaly, however it neglects the feature discrepancy between different resolution scales, therefore we introduce multi-scale memories to record scale-specific features and multi-scale attention fuser between the encoding and decoding module of the autoencoder for anomaly detection, namely MMAE. MMAE updates slots at corresponding resolution scale as prototype features during unsupervised learning.
Learning Structured Latent Factors from Dependent Data:A Generative Model Framework from Information-Theoretic Perspective
Learning controllable and generalizable representation of multivariate data with desired structural properties remains a fundamental problem in machine learning.
Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling
To make that practical, we show that sampling from this modified density can be achieved by sampling in latent space according to an energy-based model induced by the sum of the latent prior log-density and the discriminator output score.
Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models
We test the verifier network on out-of-distribution detection and adversarial example detection problems, as well as anomaly detection problems in structured prediction tasks such as image caption generation.
Perceptual Generative Autoencoders
We therefore propose to map both the generated and target distributions to a latent space using the encoder of a standard autoencoder, and train the generator (or decoder) to match the target distribution in the latent space.
MetaGAN: An Adversarial Approach to Few-Shot Learning
In this paper, we propose a conceptually simple and general framework called MetaGAN for few-shot learning problems.
Understanding Hidden Memories of Recurrent Neural Networks
We propose a technique to explain the function of individual hidden state units based on their expected response to input texts.
Maximum-Likelihood Augmented Discrete Generative Adversarial Networks
Despite the successes in capturing continuous distributions, the application of generative adversarial networks (GANs) to discrete settings, like natural language tasks, is rather restricted.
