Search Results for author:
Found 50 papers, 25 papers with code
Unsupervised Domain Adaptation with Noise Resistible Mutual-Training for Person Re-identification
On the other hand, mutual instance selection further selects reliable and informative instances for training according to the peer-confidence and relationship disagreement of the networks.
Exclusivity-Consistency Regularized Knowledge Distillation for Face Recognition
Knowledge distillation is an effective tool to compress large pre-trained Convolutional Neural Networks (CNNs) or their ensembles into models applicable to mobile and embedded devices.
ConsistentID: Portrait Generation with Multimodal Fine-Grained Identity Preserving
ConsistentID comprises two key components: a multimodal facial prompt generator that combines facial features, corresponding facial descriptions and the overall facial context to enhance precision in facial details, and an ID-preservation network optimized through the facial attention localization strategy, aimed at preserving ID consistency in facial regions.
Any-Shift Prompting for Generalization over Distributions
To effectively encode the distribution information and their relationships, we further introduce a transformer inference network with a pseudo-shift training mechanism.
ProtoDiff: Learning to Learn Prototypical Networks by Task-Guided Diffusion
Furthermore, we introduce a task-guided diffusion process within the prototype space, enabling the meta-learning of a generative process that transitions from a vanilla prototype to an overfitted prototype.
RealignDiff: Boosting Text-to-Image Diffusion Model with Coarse-to-fine Semantic Re-alignment
Recent advances in text-to-image diffusion models have achieved remarkable success in generating high-quality, realistic images from textual descriptions.
POCE: Pose-Controllable Expression Editing
POCE achieves the more accessible and realistic pose-controllable expression editing by mapping face images into UV space, where facial expressions and head poses can be disentangled and edited separately.
KD-DLGAN: Data Limited Image Generation via Knowledge Distillation
The first is aggregated generative KD that mitigates the discriminator overfitting by challenging the discriminator with harder learning tasks and distilling more generalizable knowledge from the pre-trained models.
Self-Paced Learning for Open-Set Domain Adaptation
Open-set domain adaptation aims to not only recognize target samples belonging to common classes shared by source and target domains but also perceive unknown class samples.
Energy-Based Test Sample Adaptation for Domain Generalization
In this paper, we propose energy-based sample adaptation at test time for domain generalization.
Pseudo-Labeling Based Practical Semi-Supervised Meta-Training for Few-Shot Learning
Most existing few-shot learning (FSL) methods require a large amount of labeled data in meta-training, which is a major limit.
RePFormer: Refinement Pyramid Transformer for Robust Facial Landmark Detection
This paper presents a Refinement Pyramid Transformer (RePFormer) for robust facial landmark detection.
Cloning Outfits from Real-World Images to 3D Characters for Generalizable Person Re-Identification
To address this, in this work, an automatic approach is proposed to directly clone the whole outfits from real-world person images to virtual 3D characters, such that any virtual person thus created will appear very similar to its real-world counterpart.
Ranked #1 on
Unsupervised Domain Adaptation
on ClonedPerson
(using extra training data)
Generalizable Person Re-identification
Unsupervised Domain Adaptation
+1
Pedestrian Detection: Domain Generalization, CNNs, Transformers and Beyond
As for the data, we show that the autonomous driving benchmarks are monotonous in nature, that is, they are not diverse in scenarios and dense in pedestrians.
TAL: Two-stream Adaptive Learning for Generalizable Person Re-identification
In the meantime, we design an adaptive BN layer in the domain-invariant stream, to approximate the statistics of various unseen domains.
Domain Generalization
Generalizable Person Re-identification
+1
Efficient Person Search: An Anchor-Free Approach
Third, by investigating the advantages of both anchor-based and anchor-free models, we further augment AlignPS with an ROI-Align head, which significantly improves the robustness of re-id features while still keeping our model highly efficient.
Ranked #4 on
Person Search
on PRW
Learning Anchored Unsigned Distance Functions with Gradient Direction Alignment for Single-view Garment Reconstruction
While single-view 3D reconstruction has made significant progress benefiting from deep shape representations in recent years, garment reconstruction is still not solved well due to open surfaces, diverse topologies and complex geometric details.
Local-to-Global Self-Attention in Vision Transformers
Transformers have demonstrated great potential in computer vision tasks.
Exploring Visual Context for Weakly Supervised Person Search
This paper inventively considers weakly supervised person search with only bounding box annotations.
AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection
Last but not least, we introduce a region feature alignment and an instance discriminator to learn domain-invariant features for object proposals.
TransMatcher: Deep Image Matching Through Transformers for Generalizable Person Re-identification
In this work, we further investigate the possibility of applying Transformers for image matching and metric learning given pairs of images.
Ranked #1 on
Generalizable Person Re-identification
on Market-1501
(using extra training data)
Generalizable Person Re-identification
Image Classification
+2
When Liebig's Barrel Meets Facial Landmark Detection: A Practical Model
To this end, we first propose a baseline model equipped with one transformer decoder as detection head.
Ranked #5 on
Face Alignment
on COFW
Graph Sampling Based Deep Metric Learning for Generalizable Person Re-Identification
Though online hard example mining has improved the learning efficiency to some extent, the mining in mini batches after random sampling is still limited.
Ranked #2 on
Generalizable Person Re-identification
on Market-1501
(using extra training data)
Anchor-Free Person Search
Person search aims to simultaneously localize and identify a query person from realistic, uncropped images, which can be regarded as the unified task of pedestrian detection and person re-identification (re-id).
Ranked #10 on
Person Search
on CUHK-SYSU
Human Parsing Based Texture Transfer from Single Image to 3D Human via Cross-View Consistency
This paper proposes a human parsing based texture transfer model via cross-view consistency learning to generate the texture of 3D human body from a single image.
DomainMix: Learning Generalizable Person Re-Identification Without Human Annotations
In this way, human annotations are no longer required, and it is scalable to large and diverse real-world datasets.
Generalizable Person Re-identification
Unsupervised Domain Adaptation
Surpassing Real-World Source Training Data: Random 3D Characters for Generalizable Person Re-Identification
To address this, we propose to automatically synthesize a large-scale person re-identification dataset following a set-up similar to real surveillance but with virtual environments, and then use the synthesized person images to train a generalizable person re-identification model.
Domain Generalization
Generalizable Person Re-identification
+1
Attentive WaveBlock: Complementarity-enhanced Mutual Networks for Unsupervised Domain Adaptation in Person Re-identification and Beyond
This paper proposes a novel light-weight module, the Attentive WaveBlock (AWB), which can be integrated into the dual networks of mutual learning to enhance the complementarity and further depress noise in the pseudo-labels.
Ranked #2 on
Unsupervised Domain Adaptation
on Duke to MSMT
Generalizable Pedestrian Detection: The Elephant In The Room
Furthermore, we illustrate that diverse and dense datasets, collected by crawling the web, serve to be an efficient source of pre-training for pedestrian detection.
Ranked #3 on
Pedestrian Detection
on CityPersons
(using extra training data)
Pixel-in-Pixel Net: Towards Efficient Facial Landmark Detection in the Wild
The proposed model is equipped with a novel detection head based on heatmap regression, which conducts score and offset predictions simultaneously on low-resolution feature maps.
Ranked #4 on
Face Alignment
on COFW
Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting
In this paper, beyond representation learning, we consider how to formulate person image matching directly in deep feature maps.
Ranked #3 on
Generalizable Person Re-identification
on Market-1501
(MSMT17-All->mAP metric, using extra
training data)
Domain Generalization
Generalizable Person Re-identification
+1
Center and Scale Prediction: Anchor-free Approach for Pedestrian and Face Detection
Like edges, corners, blobs and other feature detectors, the proposed detector scans for feature points all over the image, for which the convolution is naturally suited.
Ranked #8 on
Pedestrian Detection
on Caltech
(using extra training data)
Pixel-aware Deep Function-mixture Network for Spectral Super-Resolution
To address this problem, we propose a pixel-aware deep function-mixture network for SSR, which is composed of a new class of modules, termed function-mixture (FM) blocks.
Vehicle Re-identification Using Quadruple Directional Deep Learning Features
Specifically, the same basic deep learning architecture is a shortly and densely connected convolutional neural network to extract basic feature maps of an input square vehicle image in the first stage.
Ranked #3 on
Vehicle Re-Identification
on VehicleID Large
(mAP metric)
Learning Efficient Single-stage Pedestrian Detectors by Asymptotic Localization Fitting
However, current single-stage detectors (e. g. SSD) have not presented competitive accuracy on common pedestrian detection benchmarks.
Ranked #11 on
Pedestrian Detection
on Caltech
(using extra training data)
Learning Efficient Image Representation for Person Re-Identification
Then, a robust image representation based on color names is obtained by concatenating the statistical descriptors in each stripe.
Deep Person Re-Identification with Improved Embedding and Efficient Training
However, to achieve this, existing deep models prefer to adopt image pairs or triplets to form verification loss, which is inefficient and unstable since the number of training pairs or triplets grows rapidly as the number of training data grows.
Deep Hybrid Similarity Learning for Person Re-identification
In this paper, a deep hybrid similarity learning (DHSL) method for person Re-ID based on a convolution neural network (CNN) is proposed.
Embedding Deep Metric for Person Re-identication A Study Against Large Variations
From this point of view, selecting suitable positive i. e. intra-class) training samples within a local range is critical for training the CNN embedding, especially when the data has large intra-class variations.
Learning Discriminative Features with Class Encoder
Deep neural networks usually benefit from unsupervised pre-training, e. g. auto-encoders.
Partial Person Re-Identification
We address a new partial person re-identification (re-id) problem, where only a partial observation of a person is available for matching across different non-overlapping camera views.
Efficient PSD Constrained Asymmetric Metric Learning for Person Re-Identification
We argue that the PSD constraint provides a useful regularization to smooth the solution of the metric, and hence the learned metric is more robust than without the PSD constraint.
Constrained Deep Metric Learning for Person Re-identification
In this paper, we propose a novel CNN-based method to learn a discriminative metric with good robustness to the over-fitting problem in person re-identification.
Learning Face Representation from Scratch
The current situation in the field of face recognition is that data is more important than algorithm.
Cross-Modal Similarity Learning : A Low Rank Bilinear Formulation
A new approach to the problem has been raised which intends to match features of different modalities directly.
A Fast and Accurate Unconstrained Face Detector
First, a new image feature called Normalized Pixel Difference (NPD) is proposed.
Ranked #6 on
Face Detection
on PASCAL Face
Open-set Person Re-identification
Person re-identification is becoming a hot research for developing both machine learning algorithms and video surveillance applications.
Person Re-identification by Local Maximal Occurrence Representation and Metric Learning
In this paper, we propose an effective feature representation called Local Maximal Occurrence (LOMO), and a subspace and metric learning method called Cross-view Quadratic Discriminant Analysis (XQDA).
Ranked #88 on
Person Re-Identification
on DukeMTMC-reID
Shared Representation Learning for Heterogeneous Face Recognition
For NIR-VIS problem, we produce new state-of-the-art performance on the CASIA HFB and NIR-VIS 2. 0 databases.
Robust Multi-resolution Pedestrian Detection in Traffic Scenes
The model contains resolution aware transformations to map pedestrians in different resolutions to a common space, where a shared detector is constructed to distinguish pedestrians from background.
