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Found 23 papers, 13 papers with code
A Unified Objective for Novel Class Discovery
In this paper, we study the problem of Novel Class Discovery (NCD).
Smoothing the Disentangled Latent Style Space for Unsupervised Image-to-Image Translation
In this paper, we propose a new training protocol based on three specific losses which help a translation network to learn a smooth and disentangled latent style space in which: 1) Both intra- and inter-domain interpolations correspond to gradual changes in the generated images and 2) The content of the source image is better preserved during the translation.
Efficient Training of Visual Transformers with Small-Size Datasets
Visual Transformers (VTs) are emerging as an architectural paradigm alternative to Convolutional networks (CNNs).
Controllable Person Image Synthesis with Spatially-Adaptive Warped Normalization
Controllable person image generation aims to produce realistic human images with desirable attributes (e. g., the given pose, cloth textures or hair style).
Dual In-painting Model for Unsupervised Gaze Correction and Animation in the Wild
In this paper we address the problem of unsupervised gaze correction in the wild, presenting a solution that works without the need for precise annotations of the gaze angle and the head pose.
Online Continual Learning under Extreme Memory Constraints
Continual Learning (CL) aims to develop agents emulating the human ability to sequentially learn new tasks while being able to retain knowledge obtained from past experiences.
Whitening for Self-Supervised Representation Learning
Most of the current self-supervised representation learning (SSL) methods are based on the contrastive loss and the instance-discrimination task, where augmented versions of the same image instance ("positives") are contrasted with instances extracted from other images ("negatives").
TriGAN: Image-to-Image Translation for Multi-Source Domain Adaptation
In this paper we propose the first approach for Multi-Source Domain Adaptation (MSDA) based on Generative Adversarial Networks.
Coarse-to-Fine Gaze Redirection with Numerical and Pictorial Guidance
In this paper, we propose to alleviate these problems by means of a novel gaze redirection framework which exploits both a numerical and a pictorial direction guidance, jointly with a coarse-to-fine learning strategy.
Attention-based Fusion for Multi-source Human Image Generation
We present a generalization of the person-image generation task, in which a human image is generated conditioned on a target pose and a set X of source appearance images.
Whitening and Coloring transform for GANs
In this paper we propose to generalize both BN and cBN using a Whitening and Coloring based batch normalization.
Appearance and Pose-Conditioned Human Image Generation using Deformable GANs
Specifically, given an image xa of a person and a target pose P(xb), extracted from a different image xb, we synthesize a new image of that person in pose P(xb), while preserving the visual details in xa.
Metric-Learning based Deep Hashing Network for Content Based Retrieval of Remote Sensing Images
Hashing methods have been recently found very effective in retrieval of remote sensing (RS) images due to their computational efficiency and fast search speed.
Unsupervised Domain Adaptation using Feature-Whitening and Consensus Loss
A classifier trained on a dataset seldom works on other datasets obtained under different conditions due to domain shift.
Whitening and Coloring batch transform for GANs
In this paper we propose to generalize both BN and cBN using a Whitening and Coloring based batch normalization.
Deformable GANs for Pose-based Human Image Generation
Specifically, given an image of a person and a target pose, we synthesize a new image of that person in the novel pose.
Ranked #5 on
Gesture-to-Gesture Translation
on NTU Hand Digit
Gesture-to-Gesture Translation
Image-to-Image Translation
+1
Abnormal Event Detection in Videos using Generative Adversarial Nets
In this paper we address the abnormality detection problem in crowded scenes.
Ranked #1 on
Abnormal Event Detection In Video
on UCSD
Training Adversarial Discriminators for Cross-channel Abnormal Event Detection in Crowds
Abnormal crowd behaviour detection attracts a large interest due to its importance in video surveillance scenarios.
FOIL it! Find One mismatch between Image and Language caption
In this paper, we aim to understand whether current language and vision (LaVi) models truly grasp the interaction between the two modalities.
Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detection
In this paper, we show that keeping track of the changes in the CNN feature across time can facilitate capturing the local abnormality.
Self Paced Deep Learning for Weakly Supervised Object Detection
The main idea is to iteratively select a subset of images and boxes that are the most reliable, and use them for training.
Ranked #26 on
Weakly Supervised Object Detection
on PASCAL VOC 2007
Multiple Instance Learning
Weakly Supervised Object Detection
Unsupervised Tube Extraction Using Transductive Learning and Dense Trajectories
The combination of appearance-based static ''objectness'' (Selective Search), motion information (Dense Trajectories) and transductive learning (detectors are forced to "overfit" on the unsupervised data used for training) makes the proposed approach extremely robust.
