Search Results for author:
Found 61 papers, 22 papers with code
Phase Error Sensitivity to Injection Signals in Multi-Phase Injection-Locked Ring Oscillators
Multi-phase injection-locked ring oscillators (MP-ILROs) are widely used for multi-phase clock generation, with their phase accuracy primarily determined by the inherent accuracy of the oscillator itself, due to the suppression of input signal errors.
Amplitude-to-Phase Conversion in Injection-Locked CMOS Ring Oscillators
Injection-locked ring oscillators (ILROs) are extensively employed for multi-phase clock generation in wireline and optical links.
Scaling Up Video Summarization Pretraining with Large Language Models
Long-form video content constitutes a significant portion of internet traffic, making automated video summarization an essential research problem.
Visual Layout Composer: Image-Vector Dual Diffusion Model for Design Layout Generation
This paper proposes an image-vector dual diffusion model for generative layout design.
Multi-Modal Video Topic Segmentation with Dual-Contrastive Domain Adaptation
Video topic segmentation unveils the coarse-grained semantic structure underlying videos and is essential for other video understanding tasks.
DualVector: Unsupervised Vector Font Synthesis with Dual-Part Representation
Automatic generation of fonts can be an important aid to typeface design.
Improving Diffusion Models for Scene Text Editing with Dual Encoders
Scene text editing is a challenging task that involves modifying or inserting specified texts in an image while maintaining its natural and realistic appearance.
Align and Attend: Multimodal Summarization with Dual Contrastive Losses
The goal of multimodal summarization is to extract the most important information from different modalities to form output summaries.
Ranked #3 on
Supervised Video Summarization
on SumMe
Extractive Text Summarization
Supervised Video Summarization
SVGformer: Representation Learning for Continuous Vector Graphics Using Transformers
Advances in representation learning have led to great success in understanding and generating data in various domains.
Moment Detection in Long Tutorial Videos
To study this problem, we propose the first dataset of untrimmed, long-form tutorial videos for the task of Moment Detection called the Behance Moment Detection (BMD) dataset.
LiveSeg: Unsupervised Multimodal Temporal Segmentation of Long Livestream Videos
Livestream videos have become a significant part of online learning, where design, digital marketing, creative painting, and other skills are taught by experienced experts in the sessions, making them valuable materials.
Semantics-Consistent Cross-domain Summarization via Optimal Transport Alignment
Multimedia summarization with multimodal output (MSMO) is a recently explored application in language grounding.
Toward Understanding WordArt: Corner-Guided Transformer for Scene Text Recognition
Thirdly, we utilize Transformer to learn the global feature on image-level and model the global relationship of the corner points, with the assistance of a corner-query cross-attention mechanism.
MHMS: Multimodal Hierarchical Multimedia Summarization
Multimedia summarization with multimodal output can play an essential role in real-world applications, i. e., automatically generating cover images and titles for news articles or providing introductions to online videos.
STALP: Style Transfer with Auxiliary Limited Pairing
We present an approach to example-based stylization of images that uses a single pair of a source image and its stylized counterpart.
Font Completion and Manipulation by Cycling Between Multi-Modality Representations
The novel graph constructor maps a glyph's latent code to its graph representation that matches expert knowledge, which is trained to help the translation task.
Black-Box Diagnosis and Calibration on GAN Intra-Mode Collapse: A Pilot Study
Existing diversity tests of samples from GANs are usually conducted qualitatively on a small scale, and/or depends on the access to original training data as well as the trained model parameters.
A Multi-Implicit Neural Representation for Fonts
Fonts are ubiquitous across documents and come in a variety of styles.
Rethinking Text Segmentation: A Novel Dataset and A Text-Specific Refinement Approach
We also introduce Text Refinement Network (TexRNet), a novel text segmentation approach that adapts to the unique properties of text, e. g. non-convex boundary, diverse texture, etc., which often impose burdens on traditional segmentation models.
G-DARTS-A: Groups of Channel Parallel Sampling with Attention
Differentiable Architecture Search (DARTS) provides a baseline for searching effective network architectures based gradient, but it is accompanied by huge computational overhead in searching and training network architecture.
Texture Hallucination for Large-Factor Painting Super-Resolution
We aim to super-resolve digital paintings, synthesizing realistic details from high-resolution reference painting materials for very large scaling factors (e. g., 8X, 16X).
Is There Mode Collapse? A Case Study on Face Generation and Its Black-box Calibration
Generative adversarial networks (GANs) nowadays are capable of producing im-ages of incredible realism.
An Internal Learning Approach to Video Inpainting
We propose a novel video inpainting algorithm that simultaneously hallucinates missing appearance and motion (optical flow) information, building upon the recent 'Deep Image Prior' (DIP) that exploits convolutional network architectures to enforce plausible texture in static images.
Large-scale Tag-based Font Retrieval with Generative Feature Learning
In this paper, we address the problem of large-scale tag-based font retrieval which aims to bring semantics to the font selection process and enable people without expert knowledge to use fonts effectively.
Privacy-Preserving Deep Action Recognition: An Adversarial Learning Framework and A New Dataset
We first discuss an innovative heuristic of cross-dataset training and evaluation, enabling the use of multiple single-task datasets (one with target task labels and the other with privacy labels) in our problem.
Controllable Artistic Text Style Transfer via Shape-Matching GAN
In this paper, we present the first text style transfer network that allows for real-time control of the crucial stylistic degree of the glyph through an adjustable parameter.
Multimodal Style Transfer via Graph Cuts
An assumption widely used in recent neural style transfer methods is that image styles can be described by global statics of deep features like Gram or covariance matrices.
Dance Dance Generation: Motion Transfer for Internet Videos
This work presents computational methods for transferring body movements from one person to another with videos collected in the wild.
Image Super-Resolution by Neural Texture Transfer
Reference-based super-resolution (RefSR), on the other hand, has proven to be promising in recovering high-resolution (HR) details when a reference (Ref) image with similar content as that of the LR input is given.
Ranked #2 on
Image Super-Resolution
on CUFED5 - 4x upscaling
Visual Font Pairing
Third, font pairing is an asymmetric problem in that the roles played by header and body fonts are not interchangeable.
Learning to Sketch with Deep Q Networks and Demonstrated Strokes
Doodling is a useful and common intelligent skill that people can learn and master.
Synthetically Supervised Feature Learning for Scene Text Recognition
We propose to leverage the parameters that lead to the output images to improve image feature learning.
``Factual'' or ``Emotional'': Stylized Image Captioning with Adaptive Learning and Attention
It uses two groups of matrices to capture the factual and stylized knowledge, respectively, and automatically learns the word-level weights of the two groups based on previous context.
Wide Activation for Efficient and Accurate Image Super-Resolution
Keras-based implementation of WDSR, EDSR and SRGAN for single image super-resolution
Ranked #4 on
Multi-Frame Super-Resolution
on PROBA-V
Flow-Grounded Spatial-Temporal Video Prediction from Still Images
Existing video prediction methods mainly rely on observing multiple historical frames or focus on predicting the next one-frame.
Towards Privacy-Preserving Visual Recognition via Adversarial Training: A Pilot Study
This paper aims to improve privacy-preserving visual recognition, an increasingly demanded feature in smart camera applications, by formulating a unique adversarial training framework.
"Factual" or "Emotional": Stylized Image Captioning with Adaptive Learning and Attention
It uses two groups of matrices to capture the factual and stylized knowledge, respectively, and automatically learns the word-level weights of the two groups based on previous context.
Re-Weighted Adversarial Adaptation Network for Unsupervised Domain Adaptation
In this paper, we propose the Re-weighted Adversarial Adaptation Network (RAAN) to reduce the feature distribution divergence and adapt the classifier when domain discrepancies are disparate.
Open-Ended Question Answering
Unsupervised Domain Adaptation
Multi-Task Adversarial Network for Disentangled Feature Learning
The encoder and the discriminators are trained cooperatively on factors of interest, but in an adversarial way on factors of distraction.
Reference-Conditioned Super-Resolution by Neural Texture Transfer
We focus on transferring the high-resolution texture from reference images to the super-resolution process without the constraint of content similarity between reference and target images, which is a key difference from previous example-based methods.
Exploring Asymmetric Encoder-Decoder Structure for Context-based Sentence Representation Learning
Context information plays an important role in human language understanding, and it is also useful for machines to learn vector representations of language.
Visual to Sound: Generating Natural Sound for Videos in the Wild
As two of the five traditional human senses (sight, hearing, taste, smell, and touch), vision and sound are basic sources through which humans understand the world.
Multi-Content GAN for Few-Shot Font Style Transfer
In this work, we focus on the challenge of taking partial observations of highly-stylized text and generalizing the observations to generate unobserved glyphs in the ornamented typeface.
Visually-Aware Fashion Recommendation and Design with Generative Image Models
Here, we seek to extend this contribution by showing that recommendation performance can be significantly improved by learning `fashion aware' image representations directly, i. e., by training the image representation (from the pixel level) and the recommender system jointly; this contribution is related to recent work using Siamese CNNs, though we are able to show improvements over state-of-the-art recommendation techniques such as BPR and variants that make use of pre-trained visual features.
Speeding up Context-based Sentence Representation Learning with Non-autoregressive Convolutional Decoding
We carefully designed experiments to show that neither an autoregressive decoder nor an RNN decoder is required.
Robust Video Super-Resolution With Learned Temporal Dynamics
Second, we reduce the complexity of motion between neighboring frames using a spatial alignment network that is much more robust and efficient than competing alignment methods and can be jointly trained with the temporal adaptive network in an end-to-end manner.
Robust Lane Tracking with Multi-mode Observation Model and Particle Filtering
Automatic lane tracking involves estimating the underlying signal from a sequence of noisy signal observations.
Rethinking Skip-thought: A Neighborhood based Approach
We train our skip-thought neighbor model on a large corpus with continuous sentences, and then evaluate the trained model on 7 tasks, which include semantic relatedness, paraphrase detection, and classification benchmarks.
Universal Style Transfer via Feature Transforms
The whitening and coloring transforms reflect a direct matching of feature covariance of the content image to a given style image, which shares similar spirits with the optimization of Gram matrix based cost in neural style transfer.
AMC: Attention guided Multi-modal Correlation Learning for Image Search
According to the intent of query, attention mechanism can be introduced to adaptively balance the importance of different modalities.
Diversified Texture Synthesis with Feed-forward Networks
Recent progresses on deep discriminative and generative modeling have shown promising results on texture synthesis.
Learning a Mixture of Deep Networks for Single Image Super-Resolution
This paper proposes the method of learning a mixture of SR inference modules in a unified framework to tackle this problem.
Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation
Understanding users' interactions with highly subjective content---like artistic images---is challenging due to the complex semantics that guide our preferences.
Robust Single Image Super-Resolution via Deep Networks With Sparse Prior
We demonstrate that a sparse coding model particularly designed for SR can be incarnated as a neural network with the merit of end-to-end optimization over training data.
Image Captioning with Semantic Attention
Automatically generating a natural language description of an image has attracted interests recently both because of its importance in practical applications and because it connects two major artificial intelligence fields: computer vision and natural language processing.
Deep Networks for Image Super-Resolution with Sparse Prior
We show that a sparse coding model particularly designed for super-resolution can be incarnated as a neural network, and trained in a cascaded structure from end to end.
Self-Tuned Deep Super Resolution
Deep learning has been successfully applied to image super resolution (SR).
Learning Super-Resolution Jointly from External and Internal Examples
Single image super-resolution (SR) aims to estimate a high-resolution (HR) image from a lowresolution (LR) input.
Scalable Similarity Learning using Large Margin Neighborhood Embedding
In this paper, we present an image similarity learning method that can scale well in both the number of images and the dimensionality of image descriptors.
