Search Results for author:
Found 15 papers, 3 papers with code
Disentangled Representation Learning for Controllable Person Image Generation
Unlike the existing works leveraging the semantic masks to obtain the representation of each component, we propose to generate disentangled latent code via a novel attribute encoder with transformers trained in a manner of curriculum learning from a relatively easy step to a gradually hard one.
Feature Representation Learning with Adaptive Displacement Generation and Transformer Fusion for Micro-Expression Recognition
Micro-expressions are spontaneous, rapid and subtle facial movements that can neither be forged nor suppressed.
Micro Expression Recognition
Micro-Expression Recognition
+2
Learning Dynamic Style Kernels for Artistic Style Transfer
Arbitrary style transfer has been demonstrated to be efficient in artistic image generation.
Explicitly Increasing Input Information Density for Vision Transformers on Small Datasets
To boost the performance of vision Transformers on small datasets, this paper proposes to explicitly increase the input information density in the frequency domain.
Dual-Flattening Transformers through Decomposed Row and Column Queries for Semantic Segmentation
We propose a Dual-Flattening Transformer (DFlatFormer) to enable high-resolution output by reducing complexity to $\mathcal{O}(hw(H+W))$ that is multiple orders of magnitude smaller than the naive dense transformer.
A Domain Gap Aware Generative Adversarial Network for Multi-domain Image Translation
Existing approaches rely on a cycle-consistency constraint that supervises the generators to learn an inverse mapping.
Generative Adversarial Network
Image-to-Image Translation
+1
DRB-GAN: A Dynamic ResBlock Generative Adversarial Network for Artistic Style Transfer
The style code is modeled as the shared parameters for Dynamic ResBlocks connecting both the style encoding network and the style transfer network.
Dual Graph Convolutional Networks with Transformer and Curriculum Learning for Image Captioning
With the well-designed Dual-GCN, we can make the linguistic transformer better understand the relationship between different objects in a single image and make full use of similar images as auxiliary information to generate a reasonable caption description for a single image.
Adaptively Denoising Proposal Collection for Weakly Supervised Object Localization
In this paper, we address the problem of weakly supervised object localization (WSL), which trains a detection network on the dataset with only image-level annotations.
Direct Visual-Inertial Odometry with Semi-Dense Mapping
The first one, based on Direct Sparse Odometry (DSO), is to estimate the depths of candidate points for mapping and dense visual tracking.
Adaptively Denoising Proposal Collection forWeakly Supervised Object Localization
In this paper, we address the problem of weakly supervisedobject localization (WSL), which trains a detection network on the datasetwith only image-level annotations.
Stacked Wasserstein Autoencoder
At the first stage, the SWAE flexibly learns a representation distribution, i. e., the encoded prior; and at the second stage, the encoded representation distribution is approximated with a latent variable model under the regularization encouraging the latent distribution to match the explicit prior.
Toward Learning a Unified Many-to-Many Mapping for Diverse Image Translation
Image-to-image translation, which translates input images to a different domain with a learned one-to-one mapping, has achieved impressive success in recent years.
Time-Delay Momentum: A Regularization Perspective on the Convergence and Generalization of Stochastic Momentum for Deep Learning
In this paper we study the problem of convergence and generalization error bound of stochastic momentum for deep learning from the perspective of regularization.
Adversarially Approximated Autoencoder for Image Generation and Manipulation
Regularized autoencoders learn the latent codes, a structure with the regularization under the distribution, which enables them the capability to infer the latent codes given observations and generate new samples given the codes.
