Search Results for author:
Found 18 papers, 9 papers with code
MosaicFusion: Diffusion Models as Data Augmenters for Large Vocabulary Instance Segmentation
We present MosaicFusion, a simple yet effective diffusion-based data augmentation approach for large vocabulary instance segmentation.
Towards Optimal Randomized Strategies in Adversarial Example Game
The vulnerability of deep neural network models to adversarial example attacks is a practical challenge in many artificial intelligence applications.
An Asynchronous Decentralized Algorithm for Wasserstein Barycenter Problem
Wasserstein Barycenter Problem (WBP) has recently received much attention in the field of artificial intelligence.
A Random-patch based Defense Strategy Against Physical Attacks for Face Recognition Systems
In this paper, we propose a random-patch based defense strategy to robustly detect physical attacks for Face Recognition System (FRS).
CrowdCLIP: Unsupervised Crowd Counting via Vision-Language Model
To the best of our knowledge, CrowdCLIP is the first to investigate the vision language knowledge to solve the counting problem.
Ranked #1 on
Cross-Part Crowd Counting
on ShanghaiTech B
Correlational Image Modeling for Self-Supervised Visual Pre-Training
We introduce Correlational Image Modeling (CIM), a novel and surprisingly effective approach to self-supervised visual pre-training.
Super-Resolution Information Enhancement For Crowd Counting
As the proposed method requires SR labels, we further propose a Super-Resolution Crowd Counting dataset (SR-Crowd).
Controllable Image Captioning via Prompting
Despite the remarkable progress of image captioning, existing captioners typically lack the controllable capability to generate desired image captions, e. g., describing the image in a rough or detailed manner, in a factual or emotional view, etc.
Masked Frequency Modeling for Self-Supervised Visual Pre-Training
We present Masked Frequency Modeling (MFM), a unified frequency-domain-based approach for self-supervised pre-training of visual models.
UniVIP: A Unified Framework for Self-Supervised Visual Pre-training
Furthermore, our method can also exploit single-centric-object dataset such as ImageNet and outperforms BYOL by 2. 5% with the same pre-training epochs in linear probing, and surpass current self-supervised object detection methods on COCO dataset, demonstrating its universality and potential.
Unsupervised Object-Level Representation Learning from Scene Images
Extensive experiments on COCO show that ORL significantly improves the performance of self-supervised learning on scene images, even surpassing supervised ImageNet pre-training on several downstream tasks.
CDMA: A Practical Cross-Device Federated Learning Algorithm for General Minimax Problems
We establish theoretical guarantees of CDMA under different choices of hyperparameters and conduct experiments on AUC maximization, robust adversarial network training, and GAN training tasks.
L-SNet: from Region Localization to Scale Invariant Medical Image Segmentation
Coarse-to-fine models and cascade segmentation architectures are widely adopted to solve the problem of large scale variations in medical image segmentation.
Partial Gromov-Wasserstein Learning for Partial Graph Matching
Graph matching finds the correspondence of nodes across two graphs and is a basic task in graph-based machine learning.
Delving into Inter-Image Invariance for Unsupervised Visual Representations
In this work, we present a comprehensive empirical study to better understand the role of inter-image invariance learning from three main constituting components: pseudo-label maintenance, sampling strategy, and decision boundary design.
Online Deep Clustering for Unsupervised Representation Learning
In this way, labels and the network evolve shoulder-to-shoulder rather than alternatingly.
Efficient Projection-Free Online Methods with Stochastic Recursive Gradient
This paper focuses on projection-free methods for solving smooth Online Convex Optimization (OCO) problems.
Aggregated Gradient Langevin Dynamics
In this paper, we explore a general Aggregated Gradient Langevin Dynamics framework (AGLD) for the Markov Chain Monte Carlo (MCMC) sampling.
