Search Results for author:
Found 202 papers, 123 papers with code
Weakly-Supervised Crowd Counting Learns from Sorting rather than Locations
We propose a soft-label sorting network along with the counting network, which sorts the given images by their crowd numbers.
Zero-Shot Point Cloud Registration
The cornerstone of ZeroReg is the novel transfer of image features from keypoints to the point cloud, enriched by aggregating information from 3D geometric neighborhoods.
Diversified in-domain synthesis with efficient fine-tuning for few-shot classification
Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class.
RankFeat&RankWeight: Rank-1 Feature/Weight Removal for Out-of-distribution Detection
This observation motivates us to propose \texttt{RankFeat}, a simple yet effective \emph{post hoc} approach for OOD detection by removing the rank-1 matrix composed of the largest singular value and the associated singular vectors from the high-level feature.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Hourglass Tokenizer for Efficient Transformer-Based 3D Human Pose Estimation
Our HoT begins with pruning pose tokens of redundant frames and ends with recovering full-length tokens, resulting in a few pose tokens in the intermediate transformer blocks and thus improving the model efficiency.
Improving Fairness using Vision-Language Driven Image Augmentation
These paths are then applied to augment images to improve the fairness of a given dataset.
CNNs for JPEGs: A Study in Computational Cost
In this paper, we propose a further study of the computational cost of deep models designed for the frequency domain, evaluating the cost of decoding and passing the images through the network.
Budget-Aware Pruning: Handling Multiple Domains with Less Parameters
Nevertheless, the models are usually larger than the baseline for a single domain.
Tightening Classification Boundaries in Open Set Domain Adaptation through Unknown Exploitation
Convolutional Neural Networks (CNNs) have brought revolutionary advances to many research areas due to their capacity of learning from raw data.
Turn Fake into Real: Adversarial Head Turn Attacks Against Deepfake Detection
Although effective deepfake detectors have been proposed, they are substantially vulnerable to adversarial attacks.
Compositional Semantic Mix for Domain Adaptation in Point Cloud Segmentation
In this study, we introduce compositional semantic mixing for point cloud domain adaptation, representing the first unsupervised domain adaptation technique for point cloud segmentation based on semantic and geometric sample mixing.
Interactive Neural Painting
This paper advances the state of the art in this emerging research domain by proposing the first approach for Interactive NP.
Edge Guided GANs with Multi-Scale Contrastive Learning for Semantic Image Synthesis
To tackle 2), we design an effective module to selectively highlight class-dependent feature maps according to the original semantic layout to preserve the semantic information.
Let's ViCE! Mimicking Human Cognitive Behavior in Image Generation Evaluation
Research in Image Generation has recently made significant progress, particularly boosted by the introduction of Vision-Language models which are able to produce high-quality visual content based on textual inputs.
Householder Projector for Unsupervised Latent Semantics Discovery
Generative Adversarial Networks (GANs), especially the recent style-based generators (StyleGANs), have versatile semantics in the structured latent space.
Point-PC: Point Cloud Completion Guided by Prior Knowledge via Causal Inference
To retrieve similar shapes from the partial input, we also apply a contrastive learning-based pre-training scheme to transfer features of incomplete shapes into the domain of complete shape features.
T2TD: Text-3D Generation Model based on Prior Knowledge Guidance
Meanwhile, to effectively integrate multi-modal prior knowledge into textual information, we adopt a novel multi-layer transformer structure to progressively fuse related shape and textual information, which can effectively compensate for the lack of structural information in the text and enhance the final performance of the 3D generation model.
Federated Generalized Category Discovery
On each client, GCL builds class-level contrastive learning with both local and global GMMs.
Riemannian Multiclass Logistics Regression for SPD Neural Networks
Moreover, we encompass the most popular classifier in existing SPD networks as a special case of our framework.
Latent Traversals in Generative Models as Potential Flows
In this work, we instead propose to model latent structures with a learned dynamic potential landscape, thereby performing latent traversals as the flow of samples down the landscape's gradient.
PAIR-Diffusion: A Comprehensive Multimodal Object-Level Image Editor
We propose \textbf{PAIR} Diffusion, a generic framework that can enable a diffusion model to control the structure and appearance properties of each object in the image.
Dynamic Conceptional Contrastive Learning for Generalized Category Discovery
This leads traditional novel category discovery (NCD) methods to be incapacitated for GCD, due to their assumption of unlabeled data are only from novel categories.
Large-scale Pre-trained Models are Surprisingly Strong in Incremental Novel Class Discovery
Discovering novel concepts from unlabelled data and in a continuous manner is an important desideratum of lifelong learners.
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.
Attribute-preserving Face Dataset Anonymization via Latent Code Optimization
By optimizing the latent codes directly, we ensure both that the identity is of a desired distance away from the original (with an identity obfuscation loss), whilst preserving the facial attributes (using a novel feature-matching loss in FaRL's deep feature space).
SpectralCLIP: Preventing Artifacts in Text-Guided Style Transfer from a Spectral Perspective
However, directly using CLIP to guide style transfer leads to undesirable artifacts (mainly written words and unrelated visual entities) spread over the image.
StylerDALLE: Language-Guided Style Transfer Using a Vector-Quantized Tokenizer of a Large-Scale Generative Model
Despite the progress made in the style transfer task, most previous work focus on transferring only relatively simple features like color or texture, while missing more abstract concepts such as overall art expression or painter-specific traits.
Graph Transformer GANs for Graph-Constrained House Generation
We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations in an end-to-end fashion for the challenging graph-constrained house generation task.
Logit Margin Matters: Improving Transferable Targeted Adversarial Attack by Logit Calibration
In this work, we provide a comprehensive investigation of the CE loss function and find that the logit margin between the targeted and untargeted classes will quickly obtain saturation in CE, which largely limits the transferability.
Modiff: Action-Conditioned 3D Motion Generation with Denoising Diffusion Probabilistic Models
Diffusion-based generative models have recently emerged as powerful solutions for high-quality synthesis in multiple domains.
Dynamically Instance-Guided Adaptation: A Backward-Free Approach for Test-Time Domain Adaptive Semantic Segmentation
In this paper, we study the application of Test-time domain adaptation in semantic segmentation (TTDA-Seg) where both efficiency and effectiveness are crucial.
Style-Hallucinated Dual Consistency Learning: A Unified Framework for Visual Domain Generalization
Furthermore, we present a novel style hallucination module (SHM) to generate style-diversified samples that are essential to consistency learning.
Orthogonal SVD Covariance Conditioning and Latent Disentanglement
Extensive experiments on visual recognition demonstrate that our methods can simultaneously improve covariance conditioning and generalization.
Consistency-Aware Anchor Pyramid Network for Crowd Localization
Crowd localization aims to predict the spatial position of humans in a crowd scenario.
A Structure-Guided Diffusion Model for Large-Hole Image Completion
The structure generator generates an edge image representing plausible structures within the holes, which is then used for guiding the texture generation process.
Bipartite Graph Reasoning GANs for Person Pose and Facial Image Synthesis
To further capture the change in pose of each part more precisely, we propose a novel part-aware bipartite graph reasoning (PBGR) block to decompose the task of reasoning the global structure transformation with a bipartite graph into learning different local transformations for different semantic body/face parts.
Deep Unsupervised Key Frame Extraction for Efficient Video Classification
The proposed TSDPC is a generic and powerful framework and it has two advantages compared with previous works, one is that it can calculate the number of key frames automatically.
Overlap-guided Gaussian Mixture Models for Point Cloud Registration
Probabilistic 3D point cloud registration methods have shown competitive performance in overcoming noise, outliers, and density variations.
Budget-Aware Pruning for Multi-Domain Learning
Nevertheless, the models are usually larger than the baseline for a single domain.
Data Augmentation-free Unsupervised Learning for 3D Point Cloud Understanding
Unsupervised learning on 3D point clouds has undergone a rapid evolution, especially thanks to data augmentation-based contrastive methods.
Vision+X: A Survey on Multimodal Learning in the Light of Data
We are perceiving and communicating with the world in a multisensory manner, where different information sources are sophisticatedly processed and interpreted by separate parts of the human brain to constitute a complex, yet harmonious and unified sensing system.
Smooth image-to-image translations with latent space interpolations
Multi-domain image-to-image (I2I) translations can transform a source image according to the style of a target domain.
Rethinking the Learning Paradigm for Facial Expression Recognition
Due to the subjective crowdsourcing annotations and the inherent inter-class similarity of facial expressions, the real-world Facial Expression Recognition (FER) datasets usually exhibit ambiguous annotation.
Facial Expression Recognition
Facial Expression Recognition (FER)
RankFeat: Rank-1 Feature Removal for Out-of-distribution Detection
The task of out-of-distribution (OOD) detection is crucial for deploying machine learning models in real-world settings.
Facial Expression Translation using Landmark Guided GANs
We propose a simple yet powerful Landmark guided Generative Adversarial Network (LandmarkGAN) for the facial expression-to-expression translation using a single image, which is an important and challenging task in computer vision since the expression-to-expression translation is a non-linear and non-aligned problem.
Training and Tuning Generative Neural Radiance Fields for Attribute-Conditional 3D-Aware Face Generation
Generative Neural Radiance Fields (GNeRF) based 3D-aware GANs have demonstrated remarkable capabilities in generating high-quality images while maintaining strong 3D consistency.
Uncertainty-guided Source-free Domain Adaptation
Source-free domain adaptation (SFDA) aims to adapt a classifier to an unlabelled target data set by only using a pre-trained source model.
Unsupervised Domain Adaptation for Video Transformers in Action Recognition
On the other hand, the performance of a model in action recognition is heavily affected by domain shift.
GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR Segmentation
Our experiments show the effectiveness of our segmentation approach on thousands of real-world point clouds.
CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentation
We propose a new approach of sample mixing for point cloud UDA, namely Compositional Semantic Mix (CoSMix), the first UDA approach for point cloud segmentation based on sample mixing.
Class-incremental Novel Class Discovery
We study the new task of class-incremental Novel Class Discovery (class-iNCD), which refers to the problem of discovering novel categories in an unlabelled data set by leveraging a pre-trained model that has been trained on a labelled data set containing disjoint yet related categories.
Adversarial Style Augmentation for Domain Generalized Urban-Scene Segmentation
Experiments on two synthetic-to-real semantic segmentation benchmarks demonstrate that AdvStyle can significantly improve the model performance on unseen real domains and show that we can achieve the state of the art.
Batch-efficient EigenDecomposition for Small and Medium Matrices
EigenDecomposition (ED) is at the heart of many computer vision algorithms and applications.
PI-Trans: Parallel-ConvMLP and Implicit-Transformation Based GAN for Cross-View Image Translation
For semantic-guided cross-view image translation, it is crucial to learn where to sample pixels from the source view image and where to reallocate them guided by the target view semantic map, especially when there is little overlap or drastic view difference between the source and target images.
Improving Covariance Conditioning of the SVD Meta-layer by Orthogonality
Inserting an SVD meta-layer into neural networks is prone to make the covariance ill-conditioned, which could harm the model in the training stability and generalization abilities.
Interaction Transformer for Human Reaction Generation
To address this limitation, we propose a novel interaction Transformer (InterFormer) consisting of a Transformer network with both temporal and spatial attention.
Unsupervised High-Resolution Portrait Gaze Correction and Animation
Solving this problem using an unsupervised method remains an open problem, especially for high-resolution face images in the wild, which are not easy to annotate with gaze and head pose labels.
Spatial Entropy as an Inductive Bias for Vision Transformers
In this work, we propose a different and complementary direction, in which a local bias is introduced using an auxiliary self-supervised task, performed jointly with standard supervised training.
On the Eigenvalues of Global Covariance Pooling for Fine-grained Visual Recognition
Inspired by this observation, we propose a network branch dedicated to magnifying the importance of small eigenvalues.
Ranked #4 on
Fine-Grained Image Classification
on Stanford Dogs
Fine-Grained Image Classification
Fine-Grained Visual Categorization
+1
Masked Jigsaw Puzzle: A Versatile Position Embedding for Vision Transformers
In particular, MJP first shuffles the selected patches via our block-wise random jigsaw puzzle shuffle algorithm, and their corresponding PEs are occluded.
LeRaC: Learning Rate Curriculum
In this work, we propose a novel curriculum learning approach termed Learning Rate Curriculum (LeRaC), which leverages the use of a different learning rate for each layer of a neural network to create a data-free curriculum during the initial training epochs.
Ranked #2 on
Speech Emotion Recognition
on CREMA-D
Joint Representation Learning and Keypoint Detection for Cross-view Geo-localization
Inspired by the human visual system for mining local patterns, we propose a new framework called RK-Net to jointly learn the discriminative Representation and detect salient Keypoints with a single Network.
Ranked #2 on
Drone navigation
on University-1652
Temporal Alignment for History Representation in Reinforcement Learning
Inspired by human memory, we propose to represent history with only important changes in the environment and, in our approach, to obtain automatically this representation using self-supervision.
Style-Hallucinated Dual Consistency Learning for Domain Generalized Semantic Segmentation
Furthermore, we present a novel style hallucination module (SHM) to generate style-diversified samples that are essential to consistency learning.
Cross-View Panorama Image Synthesis
In this paper, we tackle the problem of synthesizing a ground-view panorama image conditioned on a top-view aerial image, which is a challenging problem due to the large gap between the two image domains with different view-points.
Hyperbolic Vision Transformers: Combining Improvements in Metric Learning
Following this line of work, we propose a new hyperbolic-based model for metric learning.
Ranked #1 on
Metric Learning
on CUB-200-2011
Federated and Generalized Person Re-identification through Domain and Feature Hallucinating
During local training, the DFS are used to synthesize novel domain statistics with the proposed domain hallucinating, which is achieved by re-weighting DFS with random weights.
Cross-Modality Earth Mover's Distance for Visible Thermal Person Re-Identification
In this manner, the model will focus on reducing the inter-modality discrepancy while paying less attention to intra-identity variations, leading to a more effective modality alignment.
Local and Global GANs with Semantic-Aware Upsampling for Image Generation
To learn more discriminative class-specific feature representations for the local generation, we also propose a novel classification module.
Relation Regularized Scene Graph Generation
Inspired by this observation, in this article, we propose a relation regularized network (R2-Net), which can predict whether there is a relationship between two objects and encode this relation into object feature refinement and better SGG.
Disentangle Saliency Detection into Cascaded Detail Modeling and Body Filling
Specifically, the detail modeling focuses on capturing the object edges by supervision of explicitly decomposed detail label that consists of the pixels that are nested on the edge and near the edge.
Fast Differentiable Matrix Square Root and Inverse Square Root
Computing the matrix square root and its inverse in a differentiable manner is important in a variety of computer vision tasks.
Fast Differentiable Matrix Square Root
Previous methods either adopt the Singular Value Decomposition (SVD) to explicitly factorize the matrix or use the Newton-Schulz iteration (NS iteration) to derive the approximate solution.
Geometry-Contrastive Transformer for Generalized 3D Pose Transfer
Specifically, we propose a novel geometry-contrastive Transformer that has an efficient 3D structured perceiving ability to the global geometric inconsistencies across the given meshes.
Novel Class Discovery in Semantic Segmentation
We introduce a new setting of Novel Class Discovery in Semantic Segmentation (NCDSS), which aims at segmenting unlabeled images containing new classes given prior knowledge from a labeled set of disjoint classes.
3D-Aware Semantic-Guided Generative Model for Human Synthesis
However, they usually struggle to generate high-quality images representing non-rigid objects, such as the human body, which is of a great interest for many computer graphics applications.
Predict, Prevent, and Evaluate: Disentangled Text-Driven Image Manipulation Empowered by Pre-Trained Vision-Language Model
We propose a novel framework, i. e., Predict, Prevent, and Evaluate (PPE), for disentangled text-driven image manipulation that requires little manual annotation while being applicable to a wide variety of manipulations.
Global and Local Alignment Networks for Unpaired Image-to-Image Translation
The global alignment network aims to transfer the input image from the source domain to the target domain.
Bi-Mix: Bidirectional Mixing for Domain Adaptive Nighttime Semantic Segmentation
Specifically, in the image translation stage, Bi-Mix leverages the knowledge of day-night image pairs to improve the quality of nighttime image relighting.
AniFormer: Data-driven 3D Animation with Transformer
Instead, we introduce AniFormer, a novel Transformer-based architecture, that generates animated 3D sequences by directly taking the raw driving sequences and arbitrary same-type target meshes as inputs.
Cascaded Cross MLP-Mixer GANs for Cross-View Image Translation
To ease this problem, we propose a novel two-stage framework with a new Cascaded Cross MLP-Mixer (CrossMLP) sub-network in the first stage and one refined pixel-level loss in the second stage.
ISF-GAN: An Implicit Style Function for High-Resolution Image-to-Image Translation
The ISF manipulates the semantics of an input latent code to make the image generated from it lying in the desired visual domain.
Layout-to-Image Translation with Double Pooling Generative Adversarial Networks
In this paper, we address the task of layout-to-image translation, which aims to translate an input semantic layout to a realistic image.
Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfer
With the strength of deep generative models, 3D pose transfer regains intensive research interests in recent years.
Solo-learn: A Library of Self-supervised Methods for Visual Representation Learning
This paper presents solo-learn, a library of self-supervised methods for visual representation learning.
Total Generate: Cycle in Cycle Generative Adversarial Networks for Generating Human Faces, Hands, Bodies, and Natural Scenes
Both generators are mutually connected and trained in an end-to-end fashion and explicitly form three cycled subnets, i. e., one image generation cycle and two guidance generation cycles.
Neighborhood Contrastive Learning for Novel Class Discovery
In this paper, we address Novel Class Discovery (NCD), the task of unveiling new classes in a set of unlabeled samples given a labeled dataset with known classes.
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.
Source-Free Open Compound Domain Adaptation in Semantic Segmentation
Second, CPSS can reduce the influence of noisy pseudo-labels and also avoid the model overfitting to the target domain during self-supervised learning, consistently boosting the performance on the target and open domains.
Efficient Training of Visual Transformers with Small Datasets
This task encourages the VTs to learn spatial relations within an image and makes the VT training much more robust when training data are scarce.
Controllable Person Image Synthesis with Spatially-Adaptive Warped Normalization
Moreover, we propose a novel Self-Training Part Replacement (STPR) strategy to refine the model for the texture-transfer task, which improves the quality of the generated clothes and the preservation ability of non-target regions.
Transformer-Based Source-Free Domain Adaptation
In this paper, we study the task of source-free domain adaptation (SFDA), where the source data are not available during target adaptation.
Why Approximate Matrix Square Root Outperforms Accurate SVD in Global Covariance Pooling?
Singular Value Decomposition (SVD) is used in GCP to compute the matrix square root.
Cloth Interactive Transformer for Virtual Try-On
In the second stage, we put forth a CIT reasoning block for establishing global mutual interactive dependencies among person representation, the warped clothing item, and the corresponding warped cloth mask.
Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation
In this paper we address multi-target domain adaptation (MTDA), where given one labeled source dataset and multiple unlabeled target datasets that differ in data distributions, the task is to learn a robust predictor for all the target domains.
Ranked #2 on
Blended-target Domain Adaptation
on DomainNet
Blended-target Domain Adaptation
Multi-target Domain Adaptation
Transformer-Based Attention Networks for Continuous Pixel-Wise Prediction
While convolutional neural networks have shown a tremendous impact on various computer vision tasks, they generally demonstrate limitations in explicitly modeling long-range dependencies due to the intrinsic locality of the convolution operation.
Ranked #7 on
Depth Estimation
on NYU-Depth V2
Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification
This paper considers the problem of unsupervised person re-identification (re-ID), which aims to learn discriminative models with unlabeled data.
Viewpoint and Scale Consistency Reinforcement for UAV Vehicle Re-Identification
Moreover, our method also achieves competitive performance compared with recent works on existing vehicle ReID datasets including VehicleID, VeRi-776 and VERI-Wild.
Curriculum Learning: A Survey
Training machine learning models in a meaningful order, from the easy samples to the hard ones, using curriculum learning can provide performance improvements over the standard training approach based on random data shuffling, without any additional computational costs.
Probabilistic Graph Attention Network with Conditional Kernels for Pixel-Wise Prediction
In contrast to previous works directly considering multi-scale feature maps obtained from the inner layers of a primary CNN architecture, and simply fusing the features with weighted averaging or concatenation, we propose a probabilistic graph attention network structure based on a novel Attention-Gated Conditional Random Fields (AG-CRFs) model for learning and fusing multi-scale representations in a principled manner.
Exploiting Sample Correlation for Crowd Counting With Multi-Expert Network
Specifically, we propose a task-driven similarity metric based on sample's mutual enhancement, referred as co-fine-tune similarity, which can find a more efficient subset of data for training the expert network.
Semantically-Adaptive Upsampling for Layout-to-Image Translation
We propose the Semantically-Adaptive UpSampling (SA-UpSample), a general and highly effective upsampling method for the layout-to-image translation task.
CNNs for JPEGs: A Study in Computational Cost
In this paper, we propose a further study of the computational cost of deep models designed for the frequency domain, evaluating the cost of decoding and passing the images through the network.
Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification
In this paper, we study the problem of multi-source domain generalization in ReID, which aims to learn a model that can perform well on unseen domains with only several labeled source domains.
Deep traffic light detection by overlaying synthetic context on arbitrary natural images
By providing real image samples with traffic context to the network, the model learns to detect and classify elements of interest, such as pedestrians, traffic signs, and traffic lights.
SF-UDA$^{3D}$: Source-Free Unsupervised Domain Adaptation for LiDAR-Based 3D Object Detection
In the case of LiDAR, in fact, domain shift is not only due to changes in the environment and in the object appearances, as for visual data from RGB cameras, but is also related to the geometry of the point clouds (e. g., point density variations).
Latent World Models For Intrinsically Motivated Exploration
In this work we consider partially observable environments with sparse rewards.
Dual Attention GANs for Semantic Image Synthesis
We also propose two novel modules, i. e., position-wise Spatial Attention Module (SAM) and scale-wise Channel Attention Module (CAM), to capture semantic structure attention in spatial and channel dimensions, respectively.
Retrieval Guided Unsupervised Multi-domain Image-to-Image Translation
In this paper we propose the use of an image retrieval system to assist the image-to-image translation task.
Bipartite Graph Reasoning GANs for Person Image Generation
We present a novel Bipartite Graph Reasoning GAN (BiGraphGAN) for the challenging person image generation task.
Ranked #1 on
Pose Transfer
on Market-1501
(PCKh metric)
Describe What to Change: A Text-guided Unsupervised Image-to-Image Translation Approach
Our proposed model disentangles the image content from the visual attributes, and it learns to modify the latter using the textual description, before generating a new image from the content and the modified attribute representation.
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.
Deep Traffic Sign Detection and Recognition Without Target Domain Real Images
The method does not aim at overcoming the training with real data, but to be a compatible alternative when the real data is not available.
XingGAN for Person Image Generation
We propose a novel Generative Adversarial Network (XingGAN or CrossingGAN) for person image generation tasks, i. e., translating the pose of a given person to a desired one.
Ranked #1 on
Pose Transfer
on Market-1501
(IS metric)
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").
When Dictionary Learning Meets Deep Learning: Deep Dictionary Learning and Coding Network for Image Recognition with Limited Data
Then the activated dictionary atoms are assembled and passed to the compound dictionary learning and coding layers.
Human in Events: A Large-Scale Benchmark for Human-centric Video Analysis in Complex Events
To this end, we present a new large-scale dataset with comprehensive annotations, named Human-in-Events or HiEve (Human-centric video analysis in complex Events), for the understanding of human motions, poses, and actions in a variety of realistic events, especially in crowd & complex events.
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.
Speak2Label: Using Domain Knowledge for Creating a Large Scale Driver Gaze Zone Estimation Dataset
In this paper, a fully automatic technique for labelling an image based gaze behavior dataset for driver gaze zone estimation is proposed.
OpenMix: Reviving Known Knowledge for Discovering Novel Visual Categories in An Open World
In this paper, we tackle the problem of discovering new classes in unlabeled visual data given labeled data from disjoint classes.
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.
Motion-supervised Co-Part Segmentation
To overcome this limitation, we propose a self-supervised deep learning method for co-part segmentation.
Ranked #2 on
Unsupervised Human Pose Estimation
on Tai-Chi-HD
Binary Neural Networks: A Survey
The binary neural network, largely saving the storage and computation, serves as a promising technique for deploying deep models on resource-limited devices.
Edge Guided GANs with Contrastive Learning for Semantic Image Synthesis
We propose a novel ECGAN for the challenging semantic image synthesis task.
GMM-UNIT: Unsupervised Multi-Domain and Multi-Modal Image-to-Image Translation via Attribute Gaussian Mixture Modeling
Unsupervised image-to-image translation (UNIT) aims at learning a mapping between several visual domains by using unpaired training images.
First Order Motion Model for Image Animation
To achieve this, we decouple appearance and motion information using a self-supervised formulation.
Ranked #1 on
Video Reconstruction
on Tai-Chi-HD
Multi-Channel Attention Selection GANs for Guided Image-to-Image Translation
In the first stage, the input image and the conditional semantic guidance are fed into a cycled semantic-guided generation network to produce initial coarse results.
Non-linear Neurons with Human-like Apical Dendrite Activations
In order to classify linearly non-separable data, neurons are typically organized into multi-layer neural networks that are equipped with at least one hidden layer.
Ranked #6 on
Speech Emotion Recognition
on CREMA-D
Low-Budget Label Query through Domain Alignment Enforcement
Deep learning revolution happened thanks to the availability of a massive amount of labelled data which have contributed to the development of models with extraordinary inference capabilities.
Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation
To tackle this issue, in this work we consider learning the scene generation in a local context, and correspondingly design a local class-specific generative network with semantic maps as a guidance, which separately constructs and learns sub-generators concentrating on the generation of different classes, and is able to provide more scene details.
Asymmetric Generative Adversarial Networks for Image-to-Image Translation
In this paper, we analyze the limitation of the existing symmetric GAN models in asymmetric translation tasks, and propose an AsymmetricGAN model with both translation and reconstruction generators of unequal sizes and different parameter-sharing strategy to adapt to the asymmetric need in both unsupervised and supervised image-to-image translation tasks.
Unified Generative Adversarial Networks for Controllable Image-to-Image Translation
The proposed model consists of a single generator and a discriminator taking a conditional image and the target controllable structure as input.
Ranked #1 on
Cross-View Image-to-Image Translation
on cvusa
Facial Expression Translation
Gesture-to-Gesture Translation
+2
AttentionGAN: Unpaired Image-to-Image Translation using Attention-Guided Generative Adversarial Networks
State-of-the-art methods in image-to-image translation are capable of learning a mapping from a source domain to a target domain with unpaired image data.
Ranked #1 on
Facial Expression Translation
on CelebA
Curriculum Self-Paced Learning for Cross-Domain Object Detection
To alleviate this problem, researchers proposed various domain adaptation methods to improve object detection results in the cross-domain setting, e. g. by translating images with ground-truth labels from the source domain to the target domain using Cycle-GAN.
Event Discovery for History Representation in Reinforcement Learning
To address this problem, a possible solution is to provide the agent with information about past observations.
Progressive Fusion for Unsupervised Binocular Depth Estimation using Cycled Networks
Extensive experiments on the publicly available datasets KITTI, Cityscapes and ApolloScape demonstrate the effectiveness of the proposed model which is competitive with other unsupervised deep learning methods for depth prediction.
Structured Coupled Generative Adversarial Networks for Unsupervised Monocular Depth Estimation
Inspired by the success of adversarial learning, we propose a new end-to-end unsupervised deep learning framework for monocular depth estimation consisting of two Generative Adversarial Networks (GAN), deeply coupled with a structured Conditional Random Field (CRF) model.
Monocular Depth Estimation
Unsupervised Monocular Depth Estimation
Cycle In Cycle Generative Adversarial Networks for Keypoint-Guided Image Generation
In this work, we propose a novel Cycle In Cycle Generative Adversarial Network (C$^2$GAN) for the task of keypoint-guided image generation.
Effortless Deep Training for Traffic Sign Detection Using Templates and Arbitrary Natural Images
Deep learning has been successfully applied to several problems related to autonomous driving.
Cross-Domain Car Detection Using Unsupervised Image-to-Image Translation: From Day to Night
In this work, a method for training a car detection system with annotated data from a source domain (day images) without requiring the image annotations of the target domain (night images) is presented.
Gesture-to-Gesture Translation in the Wild via Category-Independent Conditional Maps
In this work, we propose a novel GAN architecture that decouples the required annotations into a category label - that specifies the gesture type - and a simple-to-draw category-independent conditional map - that expresses the location, rotation and size of the hand gesture.
Pattern-Affinitive Propagation across Depth, Surface Normal and Semantic Segmentation
In this paper, we propose a novel Pattern-Affinitive Propagation (PAP) framework to jointly predict depth, surface normal and semantic segmentation.
Ranked #44 on
Monocular Depth Estimation
on NYU-Depth V2
GazeCorrection:Self-Guided Eye Manipulation in the wild using Self-Supervised Generative Adversarial Networks
Gaze correction aims to redirect the person's gaze into the camera by manipulating the eye region, and it can be considered as a specific image resynthesis problem.
Impact of facial landmark localization on facial expression recognition
Although facial landmark localization (FLL) approaches are becoming increasingly accurate for characterizing facial regions, one question remains unanswered: what is the impact of these approaches on subsequent related tasks?
Regularized Evolutionary Algorithm for Dynamic Neural Topology Search
Although very effective, evolutionary algorithms rely heavily on having a large population of individuals (i. e., network architectures) and is therefore memory expensive.
Budget-Aware Adapters for Multi-Domain Learning
To implement this idea we derive specialized deep models for each domain by adapting a pre-trained architecture but, differently from other methods, we propose a novel strategy to automatically adjust the computational complexity of the network.
Expression Conditional GAN for Facial Expression-to-Expression Translation
In this paper, we focus on the facial expression translation task and propose a novel Expression Conditional GAN (ECGAN) which can learn the mapping from one image domain to another one based on an additional expression attribute.
Facial expression generation
Facial Expression Translation
+1
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.
Online Adaptation through Meta-Learning for Stereo Depth Estimation
Our proposal is evaluated on the wellestablished KITTI dataset, where we show that our online method is competitive withstate of the art algorithms trained in a batch setting.
Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation
In this paper, we propose a novel approach named Multi-Channel Attention SelectionGAN (SelectionGAN) that makes it possible to generate images of natural scenes in arbitrary viewpoints, based on an image of the scene and a novel semantic map.
Bird View Synthesis
Cross-View Image-to-Image Translation
+1
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.
Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translation
To handle the limitation, in this paper we propose a novel Attention-Guided Generative Adversarial Network (AGGAN), which can detect the most discriminative semantic object and minimize changes of unwanted part for semantic manipulation problems without using extra data and models.
Ranked #1 on
Facial Expression Translation
on Bu3dfe
Refine and Distill: Exploiting Cycle-Inconsistency and Knowledge Distillation for Unsupervised Monocular Depth Estimation
Therefore, recent works have proposed deep architectures for addressing the monocular depth prediction task as a reconstruction problem, thus avoiding the need of collecting ground-truth depth.
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.
Attribute-Guided Sketch Generation
To this end, we propose a novel Attribute-Guided Sketch Generative Adversarial Network (ASGAN) which is an end-to-end framework and contains two pairs of generators and discriminators, one of which is used to generate faces with attributes while the other one is employed for image-to-sketch translation.
Fast and Robust Dynamic Hand Gesture Recognition via Key Frames Extraction and Feature Fusion
Gesture recognition is a hot topic in computer vision and pattern recognition, which plays a vitally important role in natural human-computer interface.
Ranked #1 on
Hand Gesture Recognition
on Cambridge
Dual Generator Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
State-of-the-art methods for image-to-image translation with Generative Adversarial Networks (GANs) can learn a mapping from one domain to another domain using unpaired image data.