Search Results for author:
Found 25 papers, 11 papers with code
TypeScore: A Text Fidelity Metric for Text-to-Image Generative Models
In this work, we focus on the text rendering aspect of these models, which provides a lens for evaluating a generative model's fine-grained instruction-following capabilities.
Enhancing Fine-grained Object Detection in Aerial Images via Orthogonal Mapping
This letter introduces Orthogonal Mapping (OM), a simple yet effective method aimed at addressing the challenge of semantic confusion inherent in FGOD.
dMel: Speech Tokenization made Simple
Large language models have revolutionized natural language processing by leveraging self-supervised pretraining on vast textual data.
Improving GFlowNets for Text-to-Image Diffusion Alignment
Diffusion models have become the de-facto approach for generating visual data, which are trained to match the distribution of the training dataset.
How Far Are We from Intelligent Visual Deductive Reasoning?
Vision-Language Models (VLMs) have recently demonstrated incredible strides on diverse vision language tasks.
Rethinking Scale Imbalance in Semi-supervised Object Detection for Aerial Images
This paper focuses on the scale imbalance problem of semi-supervised object detection(SSOD) in aerial images.
HAVANA: Hard negAtiVe sAmples aware self-supervised coNtrastive leArning for Airborne laser scanning point clouds semantic segmentation
Hence, this work proposes a hard-negative sample aware self-supervised contrastive learning method to pre-train the model for semantic segmentation.
Robust and Controllable Object-Centric Learning through Energy-based Models
Humans are remarkably good at understanding and reasoning about complex visual scenes.
Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning
The main idea behind our approach is to first represent the discrete data as binary bits, and then train a continuous diffusion model to model these bits as real numbers which we call analog bits.
Ranked #7 on
Image Captioning
on MS COCO
Meta-RangeSeg: LiDAR Sequence Semantic Segmentation Using Multiple Feature Aggregation
LiDAR sensor is essential to the perception system in autonomous vehicles and intelligent robots.
Ranked #18 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.
Ranked #672 on
Image Classification
on ImageNet
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.
Ranked #15 on
Robust 3D Semantic Segmentation
on SemanticKITTI-C
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 along 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
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 #5 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.
