Search Results for author: Ying Cao

Found 17 papers, 4 papers with code

A Robust Panel Extraction Method for Manga

no code implementations Video, Image, and Sound Analysis Lab (VISAL) at the City University of Hong Kong! 2014 Xufang Pang, Ying Cao, Rynson W. H. Lau, and Antoni B. Chan

Automatically extracting frames/panels from digital comic pages is crucial for techniques that facilitate comic reading on mobile devices with limited display areas.

Layout Design

Deep Recurrent Models with Fast-Forward Connections for Neural Machine Translation

1 code implementation TACL 2016 Jie Zhou, Ying Cao, Xuguang Wang, Peng Li, Wei Xu

On the WMT'14 English-to-French task, we achieve BLEU=37. 7 with a single attention model, which outperforms the corresponding single shallow model by 6. 2 BLEU points.

Machine Translation NMT +1

Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering

3 code implementations21 Jul 2016 Peng Li, Wei Li, Zhengyan He, Xuguang Wang, Ying Cao, Jie zhou, Wei Xu

While question answering (QA) with neural network, i. e. neural QA, has achieved promising results in recent years, lacking of large scale real-word QA dataset is still a challenge for developing and evaluating neural QA system.

Answer Generation Question Answering

Deep Speaker: an End-to-End Neural Speaker Embedding System

15 code implementations5 May 2017 Chao Li, Xiaokong Ma, Bing Jiang, Xiangang Li, Xuewei Zhang, Xiao Liu, Ying Cao, Ajay Kannan, Zhenyao Zhu

We present Deep Speaker, a neural speaker embedding system that maps utterances to a hypersphere where speaker similarity is measured by cosine similarity.

Clustering Speaker Identification +1

Task-driven Webpage Saliency

no code implementations ECCV 2018 Quanlong Zheng, Jianbo Jiao, Ying Cao, Rynson W. H. Lau

Inspired by the observation that given a specific task, human attention is strongly correlated with certain semantic components on a webpage (e. g., images, buttons and input boxes), our network explicitly disentangles saliency prediction into two independent sub-tasks: task-specific attention shift prediction and task-free saliency prediction.

Saliency Prediction

Night-time Scene Parsing with a Large Real Dataset

no code implementations15 Mar 2020 Xin Tan, Ke Xu, Ying Cao, Yiheng Zhang, Lizhuang Ma, Rynson W. H. Lau

Although huge progress has been made on scene analysis in recent years, most existing works assume the input images to be in day-time with good lighting conditions.

Scene Parsing Semantic Segmentation

Online Network Utility Maximization: Algorithm, Competitive Analysis, and Applications

no code implementations26 Jan 2021 Ying Cao, Bo Sun, Danny H. K. Tsang

In addition, since worst-case scenarios rarely occur in practice, we devise an adaptive implementation of our algorithm to improve its average-case performance and validate its effectiveness via simulations.

Data Structures and Algorithms

Automatic Comic Generation with Stylistic Multi-page Layouts and Emotion-driven Text Balloon Generation

no code implementations26 Jan 2021 Xin Yang, Zongliang Ma, Letian Yu, Ying Cao, BaoCai Yin, Xiaopeng Wei, Qiang Zhang, Rynson W. H. Lau

Finally, as opposed to using the same type of balloon as in previous works, we propose an emotion-aware balloon generation method to create different types of word balloons by analyzing the emotion of subtitles and audios.

Optimal Regularized Online Allocation by Adaptive Re-Solving

no code implementations1 Sep 2022 Wanteng Ma, Ying Cao, Danny H. K. Tsang, Dong Xia

This paper introduces a dual-based algorithm framework for solving the regularized online resource allocation problems, which have potentially non-concave cumulative rewards, hard resource constraints, and a non-separable regularizer.

Nesting Forward Automatic Differentiation for Memory-Efficient Deep Neural Network Training

no code implementations22 Sep 2022 Cong Guo, Yuxian Qiu, Jingwen Leng, Chen Zhang, Ying Cao, Quanlu Zhang, Yunxin Liu, Fan Yang, Minyi Guo

An activation function is an element-wise mathematical function and plays a crucial role in deep neural networks (DNN).

Boosting Neural Networks to Decompile Optimized Binaries

no code implementations3 Jan 2023 Ying Cao, Ruigang Liang, Kai Chen, Peiwei Hu

They formulate the decompilation process as a translation problem between LPL and HPL, aiming to reduce the human cost required to develop decompilation tools and improve their generalizability.

Machine Translation Malware Analysis +2

Decentralized Adversarial Training over Graphs

no code implementations23 Mar 2023 Ying Cao, Elsa Rizk, Stefan Vlaski, Ali H. Sayed

The vulnerability of machine learning models to adversarial attacks has been attracting considerable attention in recent years.

AdaFuse: Adaptive Medical Image Fusion Based on Spatial-Frequential Cross Attention

1 code implementation9 Oct 2023 Xianming Gu, Lihui Wang, Zeyu Deng, Ying Cao, Xingyu Huang, Yue-Min Zhu

Specifically, we propose the cross-attention fusion (CAF) block, which adaptively fuses features of two modalities in the spatial and frequency domains by exchanging key and query values, and then calculates the cross-attention scores between the spatial and frequency features to further guide the spatial-frequential information fusion.

Hierarchical Fashion Design with Multi-stage Diffusion Models

no code implementations15 Jan 2024 Zhifeng Xie, Hao Li, Huiming Ding, Mengtian Li, Ying Cao

Cross-modal fashion synthesis and editing offer intelligent support to fashion designers by enabling the automatic generation and local modification of design drafts. While current diffusion models demonstrate commendable stability and controllability in image synthesis, they still face significant challenges in generating fashion design from abstract design elements and fine-grained editing. Abstract sensory expressions, \eg office, business, and party, form the high-level design concepts, while measurable aspects like sleeve length, collar type, and pant length are considered the low-level attributes of clothing. Controlling and editing fashion images using lengthy text descriptions poses a difficulty. In this paper, we propose HieraFashDiff, a novel fashion design method using the shared multi-stage diffusion model encompassing high-level design concepts and low-level clothing attributes in a hierarchical structure. Specifically, we categorized the input text into different levels and fed them in different time step to the diffusion model according to the criteria of professional clothing designers. HieraFashDiff allows designers to add low-level attributes after high-level prompts for interactive editing incrementally. In addition, we design a differentiable loss function in the sampling process with a mask to keep non-edit areas. Comprehensive experiments performed on our newly conducted Hierarchical fashion dataset, demonstrate that our proposed method outperforms other state-of-the-art competitors.

Fashion Synthesis Image Generation

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