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Found 44 papers, 18 papers with code
S-Prompts Learning with Pre-trained Transformers: An Occam's Razor for Domain Incremental Learning
In this paper, we propose one simple paradigm (named as S-Prompting) and two concrete approaches to highly reduce the forgetting degree in one of the most typical continual learning scenarios, i. e., domain increment learning (DIL).
Improving Video Generation for Multi-functional Applications
In this paper, we aim to improve the state-of-the-art video generative adversarial networks (GANs) with a view towards multi-functional applications.
Freestyle Layout-to-Image Synthesis
In this work, we explore the freestyle capability of the model, i. e., how far can it generate unseen semantics (e. g., classes, attributes, and styles) onto a given layout, and call the task Freestyle LIS (FLIS).
Covariance Pooling For Facial Expression Recognition
In this work, we explore the benefits of using a man- ifold network structure for covariance pooling to improve facial expression recognition.
Facial Expression Recognition
Facial Expression Recognition (FER)
Sliced Wasserstein Generative Models
In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions.
Towards High Resolution Video Generation with Progressive Growing of Sliced Wasserstein GANs
Furthermore, we introduce a sliced version of Wasserstein GAN (SWGAN) loss to improve the distribution learning on the video data of high-dimension and mixed-spatiotemporal distribution.
Sliced Wasserstein Generative Models
In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions.
Ranked #1 on
Video Generation
on TrailerFaces
Efficient Conditional GAN Transfer with Knowledge Propagation across Classes
To address this problem, we introduce a new GAN transfer method to explicitly propagate the knowledge from the old classes to the new classes.
Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search
In this paper, we introduce a new reinforcement learning (RL) based neural architecture search (NAS) methodology for effective and efficient generative adversarial network (GAN) architecture search.
Ranked #13 on
Image Generation
on STL-10
Isolation and Impartial Aggregation: A Paradigm of Incremental Learning without Interference
To avoid obvious stage learning bottlenecks, we propose a brand-new stage-isolation based incremental learning framework, which leverages a series of stage-isolated classifiers to perform the learning task of each stage without the interference of others.
A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials
Within the proposed benchmark, we explore some commonly known essentials of standard continual learning.
Facial Emotion Recognition with Noisy Multi-task Annotations
In our formulation, we exploit a new method to enable the emotion prediction and the joint distribution learning in a unified adversarial learning game.
Wasserstein Divergence for GANs
In many domains of computer vision, generative adversarial networks (GANs) have achieved great success, among which the family of Wasserstein GANs (WGANs) is considered to be state-of-the-art due to the theoretical contributions and competitive qualitative performance.
Direct Differentiable Augmentation Search
Data augmentation has been an indispensable tool to improve the performance of deep neural networks, however the augmentation can hardly transfer among different tasks and datasets.
Spectral Tensor Train Parameterization of Deep Learning Layers
We study low-rank parameterizations of weight matrices with embedded spectral properties in the Deep Learning context.
Neural Architecture Search of SPD Manifold Networks
To address this problem, we first introduce a geometrically rich and diverse SPD neural architecture search space for an efficient SPD cell design.
Linguistic Profiling of Deepfakes: An Open Database for Next-Generation Deepfake Detection
The two distinguished features enable DFLIP-3K to develop a benchmark that promotes progress in linguistic profiling of deepfakes, which includes three sub-tasks namely deepfake detection, model identification, and prompt prediction.
Riemannian Local Mechanism for SPD Neural Networks
The Symmetric Positive Definite (SPD) matrices have received wide attention for data representation in many scientific areas.
Building Deep Networks on Grassmann Manifolds
Learning representations on Grassmann manifolds is popular in quite a few visual recognition tasks.
Manifold-valued Image Generation with Wasserstein Generative Adversarial Nets
In addition, we recommend three benchmark datasets that are CIFAR-10 HSV/CB color images, ImageNet HSV/CB color images, UCL DT image datasets.
Face Translation between Images and Videos using Identity-aware CycleGAN
This paper presents a new problem of unpaired face translation between images and videos, which can be applied to facial video prediction and enhancement.
Deep Learning on Lie Groups for Skeleton-based Action Recognition
In recent years, skeleton-based action recognition has become a popular 3D classification problem.
On the Relation between Color Image Denoising and Classification
Inspired by classification models, we propose a novel deep learning architecture for color (multichannel) image denoising and report on thousands of images from ImageNet dataset as well as commonly used imagery.
Cross Euclidean-to-Riemannian Metric Learning with Application to Face Recognition from Video
With this mapping, the problem of learning a cross-view metric between the two source heterogeneous spaces can be expressed as learning a single-view Euclidean distance metric in the target common Euclidean space.
A Riemannian Network for SPD Matrix Learning
Symmetric Positive Definite (SPD) matrix learning methods have become popular in many image and video processing tasks, thanks to their ability to learn appropriate statistical representations while respecting Riemannian geometry of underlying SPD manifolds.
Geometry-aware Similarity Learning on SPD Manifolds for Visual Recognition
Specifically, by exploiting the Riemannian geometry of the manifold of fixed-rank Positive Semidefinite (PSD) matrices, we present a new solution to reduce optimizing over the space of column full-rank transformation matrices to optimizing on the PSD manifold which has a well-established Riemannian structure.
Learning Euclidean-to-Riemannian Metric for Point-to-Set Classification
Since the points commonly lie in Euclidean space while the sets are typically modeled as elements on Riemannian manifold, they can be treated as Euclidean points and Riemannian points respectively.
Projection Metric Learning on Grassmann Manifold With Application to Video Based Face Recognition
In video based face recognition, great success has been made by representing videos as linear subspaces, which typically lie in a special type of non-Euclidean space known as Grassmann manifold.
Discriminant Analysis on Riemannian Manifold of Gaussian Distributions for Face Recognition With Image Sets
This paper presents a method named Discriminant Analysis on Riemannian manifold of Gaussian distributions (DARG) to solve the problem of face recognition with image sets.
Face Video Retrieval With Image Query via Hashing Across Euclidean Space and Riemannian Manifold
Retrieving videos of a specific person given his/her face image as query becomes more and more appealing for applications like smart movie fast-forwards and suspect searching.
Divide-and-Conquer Adversarial Learning for High-Resolution Image and Video Enhancement
This paper introduces a divide-and-conquer inspired adversarial learning (DACAL) approach for photo enhancement.
NTIRE 2020 Challenge on Video Quality Mapping: Methods and Results
For track 2, some existing methods are evaluated, showing promising solutions to the weakly-supervised video quality mapping problem.
Neural Architecture Search as Sparse Supernet
This paper aims at enlarging the problem of Neural Architecture Search (NAS) from Single-Path and Multi-Path Search to automated Mixed-Path Search.
AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results
Missing information can be restored well in this region, especially in HR videos, where the high-frequency content mostly consists of texture details.
An Efficient Recurrent Adversarial Framework for Unsupervised Real-Time Video Enhancement
Video enhancement is a challenging problem, more than that of stills, mainly due to high computational cost, larger data volumes and the difficulty of achieving consistency in the spatio-temporal domain.
Trilevel Neural Architecture Search for Efficient Single Image Super-Resolution
Modern solutions to the single image super-resolution (SISR) problem using deep neural networks aim not only at better performance accuracy but also at a lighter and computationally efficient model.
Generative Flows with Invertible Attentions
The key idea is to exploit a masked scheme of these two attentions to learn long-range data dependencies in the context of generative flows.
GANmut: Learning Interpretable Conditional Space for Gamut of Emotions
On the other hand, learning from inexpensive and intuitive basic categorical emotion labels leads to limited emotion variability.
Neural Architecture Search for Efficient Uncalibrated Deep Photometric Stereo
We then perform a continuous relaxation of this search space and present a gradient-based optimization strategy to find an efficient light calibration and normal estimation network.
Multi-agent Actor-Critic with Time Dynamical Opponent Model
In this paper, we propose to exploit the fact that the agents seek to improve their expected cumulative reward and introduce a novel \textit{Time Dynamical Opponent Model} (TDOM) to encode the knowledge that the opponent policies tend to improve over time.
Benchmarking Deepart Detection
To address potentially appeared ethics questions, this paper establishes a deepart detection database (DDDB) that consists of a set of high-quality conventional art images (conarts) and five sets of deepart images generated by five state-of-the-art deepfake models.
Adaptive Riemannian Metrics on SPD Manifolds
Symmetric Positive Definite (SPD) matrices have received wide attention in machine learning due to their intrinsic capacity of encoding underlying structural correlation in data.
Optimal battery thermal management for electric vehicles with battery degradation minimization
A control-oriented onboard BTMS model is proposed and verified under different speed profiles and temperatures.
In-Context Translation: Towards Unifying Image Recognition, Processing, and Generation
Secondly, it standardizes the training of different tasks into a general in-context learning, where "in-context" means the input comprises an example input-output pair of the target task and a query image.
