Search Results for author:
Found 250 papers, 154 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.
Enhanced Multi-Scale Cross-Attention for Person Image Generation
Moreover, we propose two novel cross-attention blocks to effectively transfer and update the person's shape and appearance embeddings for mutual improvement.
Beyond the Known: Enhancing Open Set Domain Adaptation with Unknown Exploration
In this work, we introduce a new approach to improve OSDA techniques by extracting a set of high-confidence unknown instances and using it as a hard constraint to tighten the classification boundaries.
3D Part Segmentation via Geometric Aggregation of 2D Visual Features
Supervised 3D part segmentation models are tailored for a fixed set of objects and parts, limiting their transferability to open-set, real-world scenarios.
Hierarchical Information Flow for Generalized Efficient Image Restoration
To strike a balance between efficiency and model capacity for a generalized transformer-based IR method, we propose a hierarchical information flow mechanism for image restoration, dubbed Hi-IR, which progressively propagates information among pixels in a bottom-up manner.
Ranked #1 on
Image Deblurring
on HIDE (trained on GOPRO)
Hierarchical Cross-Attention Network for Virtual Try-On
In this paper, we present an innovative solution for the challenges of the virtual try-on task: our novel Hierarchical Cross-Attention Network (HCANet).
Anti-Forgetting Adaptation for Unsupervised Person Re-identification
In this paper, we propose a Dual-level Joint Adaptation and Anti-forgetting (DJAA) framework, which incrementally adapts a model to new domains without forgetting source domain and each adapted target domain.
Domain Adaptive Person Re-Identification
Unsupervised Person Re-Identification
Face Anonymization Made Simple
Current face anonymization techniques often depend on identity loss calculated by face recognition models, which can be inaccurate and unreliable.
Prototypical Hash Encoding for On-the-Fly Fine-Grained Category Discovery
To address these issues, we propose a novel Prototypical Hash Encoding (PHE) framework consisting of Category-aware Prototype Generation (CPG) and Discriminative Category Encoding (DCE) to mitigate the sensitivity of hash code while preserving rich discriminative information contained in high-dimension feature space, in a two-stage projection fashion.
LESS: Label-Efficient and Single-Stage Referring 3D Segmentation
Specifically, we design a Point-Word Cross-Modal Alignment module for aligning the fine-grained features of points and textual embedding.
RMLR: Extending Multinomial Logistic Regression into General Geometries
Specifically, we showcase our framework on the Symmetric Positive Definite (SPD) manifold and special orthogonal group, i. e., the set of rotation matrices.
Discriminative Anchor Learning for Efficient Multi-view Clustering
We learn discriminative view-specific feature representations according to the original dataset and build anchors from different views based on these representations, which increase the quality of the shared anchor graph.
Optimizing Resource Consumption in Diffusion Models through Hallucination Early Detection
Diffusion models have significantly advanced generative AI, but they encounter difficulties when generating complex combinations of multiple objects.
GradBias: Unveiling Word Influence on Bias in Text-to-Image Generative Models
OpenBias detects and quantifies biases, while GradBias determines the contribution of individual prompt words on biases.
PVAFN: Point-Voxel Attention Fusion Network with Multi-Pooling Enhancing for 3D Object Detection
The integration of point and voxel representations is becoming more common in LiDAR-based 3D object detection.
Global-Local Distillation Network-Based Audio-Visual Speaker Tracking with Incomplete Modalities
By transferring knowledge from teacher to student, the student network can better adapt to complex dynamic scenes with incomplete observations.
Large Language Models for Multimodal Deformable Image Registration
Specifically, we first utilize a CNN encoder to extract deep visual features from cross-modal image pairs, then we use the first adapter to adjust these tokens, and use LoRA in pre-trained LLMs to fine-tune their weights, both aimed at eliminating the domain gap between the pre-trained LLMs and the MDIR task.
ShapeSplat: A Large-scale Dataset of Gaussian Splats and Their Self-Supervised Pretraining
In particular, we show that (1) the distribution of the optimized GS centroids significantly differs from the uniformly sampled point cloud (used for initialization) counterpart; (2) this change in distribution results in degradation in classification but improvement in segmentation tasks when using only the centroids; (3) to leverage additional Gaussian parameters, we propose Gaussian feature grouping in a normalized feature space, along with splats pooling layer, offering a tailored solution to effectively group and embed similar Gaussians, which leads to notable improvement in finetuning tasks.
When Video Coding Meets Multimodal Large Language Models: A Unified Paradigm for Video Coding
Existing codecs are designed to eliminate intrinsic redundancies to create a compact representation for compression.
Masked Image Modeling: A Survey
In this work, we survey recent studies on masked image modeling (MIM), an approach that emerged as a powerful self-supervised learning technique in computer vision.
Towards End-to-End Explainable Facial Action Unit Recognition via Vision-Language Joint Learning
Facial action units (AUs), as defined in the Facial Action Coding System (FACS), have received significant research interest owing to their diverse range of applications in facial state analysis.
Towards Localized Fine-Grained Control for Facial Expression Generation
In this work, we propose the use of AUs (action units) for facial expression control in face generation.
Restore Anything Model via Efficient Degradation Adaptation
With the proliferation of mobile devices, the need for an efficient model to restore any degraded image has become increasingly significant and impactful.
Ranked #5 on
5-Degradation Blind All-in-One Image Restoration
on 5-Degradation Blind All-in-One Image Restoration
5-Degradation Blind All-in-One Image Restoration
Benchmarking
Understanding Matrix Function Normalizations in Covariance Pooling through the Lens of Riemannian Geometry
Global Covariance Pooling (GCP) has been demonstrated to improve the performance of Deep Neural Networks (DNNs) by exploiting second-order statistics of high-level representations.
3D Weakly Supervised Semantic Segmentation with 2D Vision-Language Guidance
In this paper, we propose 3DSS-VLG, a weakly supervised approach for 3D Semantic Segmentation with 2D Vision-Language Guidance, an alternative approach that a 3D model predicts dense-embedding for each point which is co-embedded with both the aligned image and text spaces from the 2D vision-language model.
Enhancing Robustness of Vision-Language Models through Orthogonality Learning and Self-Regularization
Previous works primarily focus on prompt learning to adapt the CLIP into a variety of downstream tasks, however, suffering from task overfitting when fine-tuned on a small data set.
Bringing Masked Autoencoders Explicit Contrastive Properties for Point Cloud Self-Supervised Learning
To answer this question, we first empirically validate that integrating MAE-based point cloud pre-training with the standard contrastive learning paradigm, even with meticulous design, can lead to a decrease in performance.
Product Geometries on Cholesky Manifolds with Applications to SPD Manifolds
Further, by Cholesky decomposition, the proposed deformed metrics and gyro structures are pulled back to SPD manifolds.
Transferable-guided Attention Is All You Need for Video Domain Adaptation
DTAB compels ViT to focus on the spatio-temporal transferability relationship among video frames by changing the self-attention mechanism to a transferability attention mechanism.
Stable Neighbor Denoising for Source-free Domain Adaptive Segmentation
Specifically, we introduce the Stable Neighbor Denoising (SND) approach, which effectively discovers highly correlated stable and unstable samples by nearest neighbor retrieval and guides the reliable optimization of unstable samples by bi-level learning.
Sharing Key Semantics in Transformer Makes Efficient Image Restoration
Additionally, for IR, it is commonly noted that small segments of a degraded image, particularly those closely aligned semantically, provide particularly relevant information to aid in the restoration process, as they contribute essential contextual cues crucial for accurate reconstruction.
Curriculum Direct Preference Optimization for Diffusion and Consistency Models
Our approach, Curriculum DPO, is compared against state-of-the-art fine-tuning approaches on three benchmarks, outperforming the competing methods in terms of text alignment, aesthetics and human preference.
Deep Learning-Based Object Pose Estimation: A Comprehensive Survey
Object pose estimation is a fundamental computer vision problem with broad applications in augmented reality and robotics.
UVMap-ID: A Controllable and Personalized UV Map Generative Model
To solve the above problems, we introduce a novel method, UVMap-ID, which is a controllable and personalized UV Map generative model.
OpenBias: Open-set Bias Detection in Text-to-Image Generative Models
In this paper, we tackle the challenge of open-set bias detection in text-to-image generative models presenting OpenBias, a new pipeline that identifies and quantifies the severity of biases agnostically, without access to any precompiled set.
Socially Pertinent Robots in Gerontological Healthcare
Despite the many recent achievements in developing and deploying social robotics, there are still many underexplored environments and applications for which systematic evaluation of such systems by end-users is necessary.
A Lie Group Approach to Riemannian Batch Normalization
Using the deformation concept, we generalize the existing Lie groups on SPD manifolds into three families of parameterized Lie groups.
SM4Depth: Seamless Monocular Metric Depth Estimation across Multiple Cameras and Scenes by One Model
Firstly, to obtain consistent depth across diverse scenes, we propose a novel metric scale modeling, i. e., variation-based unnormalized depth bins.
Textual Knowledge Matters: Cross-Modality Co-Teaching for Generalized Visual Class Discovery
In this paper, we study the problem of Generalized Category Discovery (GCD), which aims to cluster unlabeled data from both known and unknown categories using the knowledge of labeled data from known categories.
An Efficient Learning-based Solver Comparable to Metaheuristics for the Capacitated Arc Routing Problem
Recently, neural networks (NN) have made great strides in combinatorial optimization.
Uncertainty-Aware Testing-Time Optimization for 3D Human Pose Estimation
We observe that previous optimization-based methods commonly rely on projection constraint, which only ensures alignment in 2D space, potentially leading to the overfitting problem.
Key-Graph Transformer for Image Restoration
While it is crucial to capture global information for effective image restoration (IR), integrating such cues into transformer-based methods becomes computationally expensive, especially with high input resolution.
Democratizing Fine-grained Visual Recognition with Large Language Models
Identifying subordinate-level categories from images is a longstanding task in computer vision and is referred to as fine-grained visual recognition (FGVR).
Graph Transformer GANs with Graph Masked Modeling for Architectural Layout Generation
Finally, we propose a novel self-guided pre-training method for graph representation learning.
Generative Adversarial Network
Graph Representation Learning
+1
Bilateral Reference for High-Resolution Dichotomous Image Segmentation
It comprises two essential components: the localization module (LM) and the reconstruction module (RM) with our proposed bilateral reference (BiRef).
Ranked #1 on
Camouflaged Object Segmentation
on COD
(using extra training data)
Camouflaged Object Segmentation
Dichotomous Image Segmentation
+3
VASE: Object-Centric Appearance and Shape Manipulation of Real Videos
Recently, several works tackled the video editing task fostered by the success of large-scale text-to-image generative models.
PAIR Diffusion: A Comprehensive Multimodal Object-Level Image Editor
We propose PAIR Diffusion a generic framework that enables a diffusion model to control the structure and appearance properties of each object in the image.
Semantic Connectivity-Driven Pseudo-labeling for Cross-domain Segmentation
This approach formulates pseudo-labels at the connectivity level and thus can facilitate learning structured and low-noise semantics.
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.
ZeroReg: Zero-Shot Point Cloud Registration with Foundation Models
To address this, we construct scene graphs to capture spatial relationships among objects and apply a graph matching algorithm to these graphs to accurately identify matched objects.
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
Transformers have been successfully applied in the field of video-based 3D human pose estimation.
Improving Fairness using Vision-Language Driven Image Augmentation
These paths are then applied to augment images to improve the fairness of a given dataset.
Flow Factorized Representation Learning
A prominent goal of representation learning research is to achieve representations which are factorized in a useful manner with respect to the ground truth factors of variation.
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
We achieve this by encouraging all domains to use a similar subset of filters from the baseline model, up to the amount defined by the user's budget.
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.
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 Multinomial Logistics Regression for SPD Neural Networks
Besides, our framework offers a novel intrinsic explanation for the most popular LogEig classifier in existing SPD networks.
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.
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.
PAIR-Diffusion: A Comprehensive Multimodal Object-Level Image Editor
We propose PAIR Diffusion, a generic framework that can enable a diffusion model to control the structure and appearance properties of each object in the image.
Large-scale Pre-trained Models are Surprisingly Strong in Incremental Novel Class Discovery
Discovering novel concepts in unlabelled datasets and in a continuous manner is an important desideratum of lifelong learners.
Adaptive Log-Euclidean Metrics for SPD Matrix Learning
Symmetric Positive Definite (SPD) matrices have received wide attention in machine learning due to their intrinsic capacity to encode 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.
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.
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.
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.
Facial Expression Translation
Generative Adversarial Network
+1
Training and Tuning Generative Neural Radiance Fields for Attribute-Conditional 3D-Aware Face Generation
Generative Neural Radiance Fields (GNeRF)-based 3D-aware GANs have showcased remarkable prowess in crafting high-fidelity images while upholding robust 3D consistency, particularly face generation.
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.
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.
Batch-efficient EigenDecomposition for Small and Medium Matrices
EigenDecomposition (ED) is at the heart of many computer vision algorithms and applications.
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.
GraphMLP: A Graph MLP-Like Architecture for 3D Human Pose Estimation
To the best of our knowledge, this is the first MLP-Like architecture for 3D human pose estimation in a single frame and a video sequence.
Ranked #62 on
3D Human Pose Estimation
on Human3.6M
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 #7 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.
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-agnostic curriculum during the initial training epochs.
Ranked #4 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.
Ranked #4 on
Robust Object Detection
on DWD
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.
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.
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.
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.
Generative Adversarial Network
Image-to-Image Translation
+1
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.
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.
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.
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.