Search Results for author:
Found 30 papers, 9 papers with code
A Joint Model for 2D and 3D Pose Estimation from a Single Image
We introduce a novel approach to automatically recover 3D human pose from a single image.
Ranked #25 on
3D Human Pose Estimation
on HumanEva-I
Fracking Deep Convolutional Image Descriptors
In this paper we propose a novel framework for learning local image descriptors in a discriminative manner.
Neuroaesthetics in Fashion: Modeling the Perception of Fashionability
Importantly, our model is able to give rich feedback back to the user, conveying which garments or even scenery she/he should change in order to improve fashionability.
Structured Prediction with Output Embeddings for Semantic Image Annotation
We address the task of annotating images with semantic tuples.
Discriminative Learning of Deep Convolutional Feature Point Descriptors
Deep learning has revolutionalized image-level tasks such as classification, but patch-level tasks, such as correspondence, still rely on hand-crafted features, e. g. SIFT.
Ranked #2 on
Satellite Image Classification
on SAT-4
Understanding Human-Centric Images: From Geometry to Fashion
Understanding humans from photographs has always been a fundamental goal of computer vision.
Fashion Style in 128 Floats: Joint Ranking and Classification Using Weak Data for Feature Extraction
We propose a novel approach for learning features from weakly-supervised data by joint ranking and classification.
Let there be color!: joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification
We present a novel technique to automatically colorize grayscale images that combines both global priors and local image features.
Mastering Sketching: Adversarial Augmentation for Structured Prediction
Our approach augments a simplification network with a discriminator network, training both networks jointly so that the discriminator network discerns whether a line drawing is a real training data or the output of the simplification network, which in turn tries to fool it.
Multi-Modal Fashion Product Retrieval
Finding a product in the fashion world can be a daunting task.
Joint Gap Detection and Inpainting of Line Drawings
We evaluate our method qualitatively on a diverse set of challenging line drawings and also provide quantitative results with a user study, where it significantly outperforms the state of the art.
Virtual Thin Slice: 3D Conditional GAN-based Super-resolution for CT Slice Interval
In this paper, we present a novel architecture based on conditional Generative Adversarial Networks (cGANs) with the goal of generating high resolution images of main body parts including head, chest, abdomen and legs.
Understanding the Effects of Pre-Training for Object Detectors via Eigenspectrum
Furthermore, we propose a method for automatically determining the widths (the numbers of channels) of object detectors based on the eigenspectrum.
Two-stage Discriminative Re-ranking for Large-scale Landmark Retrieval
Due to the variance of the images, which include extreme viewpoint changes such as having to retrieve images of the exterior of a landmark from images of the interior, this is very challenging for approaches based exclusively on visual similarity.
TopNet: Topology Preserving Metric Learning for Vessel Tree Reconstruction and Labelling
To the best of our knowledge, this is the first deep learning based approach which learns multi-label tree structure connectivity from images.
DeepRemaster: Temporal Source-Reference Attention Networks for Comprehensive Video Enhancement
The remastering of vintage film comprises of a diversity of sub-tasks including super-resolution, noise removal, and contrast enhancement which aim to restore the deteriorated film medium to its original state.
Automatic Segmentation, Localization, and Identification of Vertebrae in 3D CT Images Using Cascaded Convolutional Neural Networks
The input to the second networks have an auxiliary channel in addition to the 3D CT images.
User-Guided Line Art Flat Filling With Split Filling Mechanism
We present a deep learning framework for user-guided line art flat filling that can compute the "influence areas" of the user color scribbles, i. e., the areas where the user scribbles should propagate and influence.
General Virtual Sketching Framework for Vector Line Art
Vector line art plays an important role in graphic design, however, it is tedious to manually create.
Constrained Graphic Layout Generation via Latent Optimization
We optimize using the latent space of an off-the-shelf layout generation model, allowing our approach to be complementary to and used with existing layout generation models.
P2Net: A Post-Processing Network for Refining Semantic Segmentation of LiDAR Point Cloud based on Consistency of Consecutive Frames
To remedy this problem, we propose to explicitly train a network to refine these results predicted by an existing segmentation method.
Generative Colorization of Structured Mobile Web Pages
The web page colorization problem is then formalized as a task of estimating plausible color styles for a given web page content with a given hierarchical structure of the elements.
LayoutDM: Discrete Diffusion Model for Controllable Layout Generation
Controllable layout generation aims at synthesizing plausible arrangement of element bounding boxes with optional constraints, such as type or position of a specific element.
Towards Flexible Multi-modal Document Models
Creative workflows for generating graphical documents involve complex inter-related tasks, such as aligning elements, choosing appropriate fonts, or employing aesthetically harmonious colors.
Diffusart: Enhancing Line Art Colorization with Conditional Diffusion Models
Colorization of line art drawings is an important task in illustration and animation workflows.
Controllable Multi-domain Semantic Artwork Synthesis
To address this problem, we propose a dataset, which we call ArtSem, that contains 40, 000 images of artwork from 4 different domains with their corresponding semantic label maps.
Diffusion-based Holistic Texture Rectification and Synthesis
We present a novel framework for rectifying occlusions and distortions in degraded texture samples from natural images.
Visual Grounding of Whole Radiology Reports for 3D CT Images
Our framework combines two components of 1) anatomical segmentation of images, and 2) report structuring.
Image Synthesis-based Late Stage Cancer Augmentation and Semi-Supervised Segmentation for MRI Rectal Cancer Staging
To tackle this, we propose two kinds of approaches of image synthesis-based late stage cancer augmentation and semi-supervised learning which is designed for T-stage prediction.
Return-Aligned Decision Transformer
Traditional approaches in offline reinforcement learning aim to learn the optimal policy that maximizes the cumulative reward, also known as return.
