74 citations · 448 across the 40 of their papers we have counts for
23 papers · 1 filter
WorldCast: Exploring Intelligent Visual Generation in World State Prediction
Wenhao Yan, Haodong Li, Xiaotong Li +9
Humans interpret multimodal observations of the world and forecast how it will evolve. Recent multimodal generative systems have demonstrated similar capabilities: they can integra…
Image Tokens Matter: Mitigating Hallucination in Discrete Tokenizer-based Large Vision-Language Models via Latent Editing
Weixing Wang, Zifeng Ding, Jindong Gu +4
Large Vision-Language Models (LVLMs) with discrete image tokenizers unify multimodal representations by encoding visual inputs into a finite set of tokens. Despite their effectiven…
Data Pruning Can Do More: A Comprehensive Data Pruning Approach for Object Re-identification
Zi Yang, Haojin Yang, Soumajit Majumder +2
Previous studies have demonstrated that not each sample in a dataset is of equal importance during training. Data pruning aims to remove less important or informative samples while…
Generalized Categories Discovery for Long-tailed Recognition
Ziyun Li, Christoph Meinel, Haojin Yang
Generalized Class Discovery (GCD) plays a pivotal role in discerning both known and unknown categories from unlabeled datasets by harnessing the insights derived from a labeled set…
ImbaGCD: Imbalanced Generalized Category Discovery
Ziyun Li, Ben Dai, Furkan Simsek +2
Generalized class discovery (GCD) aims to infer known and unknown categories in an unlabeled dataset leveraging prior knowledge of a labeled set comprising known classes. Existing…
DocLangID: Improving Few-Shot Training to Identify the Language of Historical Documents
Furkan Simsek, Brian Pfitzmann, Hendrik Raetz +3
Language identification describes the task of recognizing the language of written text in documents. This information is crucial because it can be used to support the analysis of a…
Join the High Accuracy Club on ImageNet with A Binary Neural Network Ticket
Nianhui Guo, Joseph Bethge, Christoph Meinel +1
Binary neural networks are the extreme case of network quantization, which has long been thought of as a potential edge machine learning solution. However, the significant accuracy…
A Closer Look at Novel Class Discovery from the Labeled Set
Ziyun Li, Jona Otholt, Ben Dai +3
Novel class discovery (NCD) aims to infer novel categories in an unlabeled dataset leveraging prior knowledge of a labeled set comprising disjoint but related classes. Existing res…
Synthesis in Style: Semantic Segmentation of Historical Documents using Synthetic Data
Christian Bartz, Hendrik Raetz, Jona Otholt +2
One of the most pressing problems in the automated analysis of historical documents is the availability of annotated training data. The problem is that labeling samples is a time-c…
AsymmNet: Towards ultralight convolution neural networks using asymmetrical bottlenecks
Haojin Yang, Zhen Shen, Yucheng Zhao
Deep convolutional neural networks (CNN) have achieved astonishing results in a large variety of applications. However, using these models on mobile or embedded devices is difficul…
One Model to Reconstruct Them All: A Novel Way to Use the Stochastic Noise in StyleGAN
Christian Bartz, Joseph Bethge, Haojin Yang +1
Generative Adversarial Networks (GANs) have achieved state-of-the-art performance for several image generation and manipulation tasks. Different works have improved the limited und…
KISS: Keeping It Simple for Scene Text Recognition
Christian Bartz, Joseph Bethge, Haojin Yang +1
Over the past few years, several new methods for scene text recognition have been proposed. Most of these methods propose novel building blocks for neural networks. These novel bui…
Multi-Task Generative Adversarial Network for Handling Imbalanced Clinical Data
Mina Rezaei, Haojin Yang, Christoph Meinel
We propose a new generative adversarial architecture to mitigate imbalance data problem for the task of medical image semantic segmentation where the majority of pixels belong to a…
LoANs: Weakly Supervised Object Detection with Localizer Assessor Networks
Christian Bartz, Haojin Yang, Joseph Bethge +1
Recently, deep neural networks have achieved remarkable performance on the task of object detection and recognition. The reason for this success is mainly grounded in the availabil…
Conditional Generative Refinement Adversarial Networks for Unbalanced Medical Image Semantic Segmentation
Mina Rezaei, Haojin Yang, Christoph Meinel
We propose a new generative adversarial architecture to mitigate imbalance data problem in medical image semantic segmentation where the majority of pixels belongs to a healthy reg…
SEE: Towards Semi-Supervised End-to-End Scene Text Recognition
Christian Bartz, Haojin Yang, Christoph Meinel
Detecting and recognizing text in natural scene images is a challenging, yet not completely solved task. In recent years several new systems that try to solve at least one of the t…
Deep Learning for Medical Image Analysis
Mina Rezaei, Haojin Yang, Christoph Meinel
This report describes my research activities in the Hasso Plattner Institute and summarizes my Ph.D. plan and several novels, end-to-end trainable approaches for analyzing medical…
Conditional Adversarial Network for Semantic Segmentation of Brain Tumor
Mina Rezaei, Konstantin Harmuth, Willi Gierke +4
Automated medical image analysis has a significant value in diagnosis and treatment of lesions. Brain tumors segmentation has a special importance and difficulty due to the differe…
Deep Neural Network with l2-norm Unit for Brain Lesions Detection
Mina Rezaei, Haojin Yang, Christoph Meinel
Automated brain lesions detection is an important and very challenging clinical diagnostic task because the lesions have different sizes, shapes, contrasts, and locations. Deep Lea…
Brain Abnormality Detection by Deep Convolutional Neural Network
Mina Rezaei, Haojin Yang, Christoph Meinel
In this paper, we describe our method for classification of brain magnetic resonance (MR) images into different abnormalities and healthy classes based on the deep neural network.…
Language Identification Using Deep Convolutional Recurrent Neural Networks
Christian Bartz, Tom Herold, Haojin Yang +1
Language Identification (LID) systems are used to classify the spoken language from a given audio sample and are typically the first step for many spoken language processing tasks,…
STN-OCR: A single Neural Network for Text Detection and Text Recognition
Christian Bartz, Haojin Yang, Christoph Meinel
Detecting and recognizing text in natural scene images is a challenging, yet not completely solved task. In re- cent years several new systems that try to solve at least one of the…
Image Captioning with Deep Bidirectional LSTMs
Cheng Wang, Haojin Yang, Christian Bartz +1
This work presents an end-to-end trainable deep bidirectional LSTM (Long-Short Term Memory) model for image captioning. Our model builds on a deep convolutional neural network (CNN…