Search Results for author:
Found 42 papers, 27 papers with code
Attribute-Aware Deep Hashing with Self-Consistency for Large-Scale Fine-Grained Image Retrieval
Our work focuses on tackling large-scale fine-grained image retrieval as ranking the images depicting the concept of interests (i. e., the same sub-category labels) highest based on the fine-grained details in the query.
Hawkeye: A PyTorch-based Library for Fine-Grained Image Recognition with Deep Learning
However, the absence of a unified open-source software library covering various paradigms in FGIR poses a significant challenge for researchers and practitioners in the field.
Watch out Venomous Snake Species: A Solution to SnakeCLEF2023
The SnakeCLEF2023 competition aims to the development of advanced algorithms for snake species identification through the analysis of images and accompanying metadata.
Equiangular Basis Vectors
The learning objective of these methods can be summarized as mapping the learned feature representations to the samples' label space.
Delving Deep into Simplicity Bias for Long-Tailed Image Recognition
Simplicity Bias (SB) is a phenomenon that deep neural networks tend to rely favorably on simpler predictive patterns but ignore some complex features when applied to supervised discriminative tasks.
Ranked #4 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
Automatic Check-Out via Prototype-based Classifier Learning from Single-Product Exemplars
Automatic Check-Out (ACO) aims to accurately predict the presence and count of each category of products in check-out images, where a major challenge is the significant domain gap between training data (single-product exemplars) and test data (check-out images).
SEMICON: A Learning-to-hash Solution for Large-scale Fine-grained Image Retrieval
In this paper, we propose Suppression-Enhancing Mask based attention and Interactive Channel transformatiON (SEMICON) to learn binary hash codes for dealing with large-scale fine-grained image retrieval tasks.
An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning
Semi-supervised few-shot learning consists in training a classifier to adapt to new tasks with limited labeled data and a fixed quantity of unlabeled data.
A Channel Mix Method for Fine-Grained Cross-Modal Retrieval
In this paper, we propose a simple but effective method for dealing with the challenging fine-grained cross-modal retrieval task where it aims to enable flexible retrieval among subor-dinate categories across different modalities.
Dual Attention Networks for Few-Shot Fine-Grained Recognition
Specifically, by producing attention guidance from deep activations of input images, our hard-attention is realized by keeping a few useful deep descriptors and forming them as a bag of multi-instance learning.
Bridge the Gap between Supervised and Unsupervised Learning for Fine-Grained Classification
Unsupervised learning technology has caught up with or even surpassed supervised learning technology in general object classification (GOC) and person re-identification (re-ID).
Webly-Supervised Fine-Grained Recognition with Partial Label Learning
The task of webly-supervised fne-grained recognition is to boost recognition accuracy of classifying subordinate categories (e. g., different bird species)by utilizing freely available but noisy web data. As the label noises signifcantly hurt the network training, it is desirable to distinguish and eliminate noisy images.
Relieving Long-tailed Instance Segmentation via Pairwise Class Balance
In this paper, we explore to excavate the confusion matrix, which carries the fine-grained misclassification details, to relieve the pairwise biases, generalizing the coarse one.
A$^2$-Net: Learning Attribute-Aware Hash Codes for Large-Scale Fine-Grained Image Retrieval
Specifically, based on the captured visual representations by attention, we develop an encoder-decoder structure network of a reconstruction task to unsupervisedly distill high-level attribute-specific vectors from the appearance-specific visual representations without attribute annotations.
Fine-Grained Image Analysis with Deep Learning: A Survey
Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications.
Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach
Learning from the web can ease the extreme dependence of deep learning on large-scale manually labeled datasets.
Contextualizing Meta-Learning via Learning to Decompose
To this end, we propose Learning to Decompose Network (LeadNet) to contextualize the meta-learned ``support-to-target'' strategy, leveraging the context of instances with one or mixed latent attributes in a support set.
Bag of Tricks for Long-Tailed Visual Recognition with Deep Convolutional Neural Networks
In recent years, visual recognition on challenging long-tailed distributions, where classes often exhibit extremely imbalanced frequencies, has made great progress mostly based on various complex paradigms (e. g., meta learning).
Distilling Virtual Examples for Long-tailed Recognition
We tackle the long-tailed visual recognition problem from the knowledge distillation perspective by proposing a Distill the Virtual Examples (DiVE) method.
Ranked #19 on
Long-tail Learning
on iNaturalist 2018
Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification
Learning discriminative image representations plays a vital role in long-tailed image classification because it can ease the classifier learning in imbalanced cases.
Ranked #10 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
Learning Graph Convolutional Networks for Multi-Label Recognition and Applications
The task of multi-label image recognition is to predict a set of object labels that present in an image.
Multi-Label Classification
Multi-label Image Recognition with Partial Labels
Tips and Tricks for Webly-Supervised Fine-Grained Recognition: Learning from the WebFG 2020 Challenge
WebFG 2020 is an international challenge hosted by Nanjing University of Science and Technology, University of Edinburgh, Nanjing University, The University of Adelaide, Waseda University, etc.
Salvage Reusable Samples from Noisy Data for Robust Learning
To this end, we propose a certainty-based reusable sample selection and correction approach, termed as CRSSC, for coping with label noise in training deep FG models with web images.
ExchNet: A Unified Hashing Network for Large-Scale Fine-Grained Image Retrieval
Retrieving content relevant images from a large-scale fine-grained dataset could suffer from intolerably slow query speed and highly redundant storage cost, due to high-dimensional real-valued embeddings which aim to distinguish subtle visual differences of fine-grained objects.
Hierarchical Context Embedding for Region-based Object Detection
To address this issue, we present a simple but effective Hierarchical Context Embedding (HCE) framework, which can be applied as a plug-and-play component, to facilitate the classification ability of a series of region-based detectors by mining contextual cues.
Learning Semantically Enhanced Feature for Fine-Grained Image Classification
We aim to provide a computationally cheap yet effective approach for fine-grained image classification (FGIC) in this letter.
Ranked #14 on
Fine-Grained Image Classification
on Stanford Dogs
PyRetri: A PyTorch-based Library for Unsupervised Image Retrieval by Deep Convolutional Neural Networks
Despite significant progress of applying deep learning methods to the field of content-based image retrieval, there has not been a software library that covers these methods in a unified manner.
Exploring Categorical Regularization for Domain Adaptive Object Detection
Specifically, by integrating an image-level multi-label classifier upon the detection backbone, we can obtain the sparse but crucial image regions corresponding to categorical information, thanks to the weakly localization ability of the classification manner.
BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition
Extensive experiments on four benchmark datasets, including the large-scale iNaturalist ones, justify that the proposed BBN can significantly outperform state-of-the-art methods.
Ranked #37 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
Deep Learning for Fine-Grained Image Analysis: A Survey
Among various research areas of CV, fine-grained image analysis (FGIA) is a longstanding and fundamental problem, and has become ubiquitous in diverse real-world applications.
Multi-Label Image Recognition with Graph Convolutional Networks
The task of multi-label image recognition is to predict a set of object labels that present in an image.
Ranked #12 on
Multi-Label Classification
on PASCAL VOC 2007
RPC: A Large-Scale Retail Product Checkout Dataset
The main challenge of this problem comes from the large scale and the fine-grained nature of the product categories as well as the difficulty for collecting training images that reflect the realistic checkout scenarios due to continuous update of the products.
Coarse-to-fine: A RNN-based hierarchical attention model for vehicle re-identification
Inspired by the coarse-to-fine hierarchical process, we propose an end-to-end RNN-based Hierarchical Attention (RNN-HA) classification model for vehicle re-identification.
Piecewise classifier mappings: Learning fine-grained learners for novel categories with few examples
To solve this problem, we propose an end-to-end trainable deep network which is inspired by the state-of-the-art fine-grained recognition model and is tailored for the FSFG task.
Adversarial Learning of Structure-Aware Fully Convolutional Networks for Landmark Localization
In contrast, human vision is able to predict poses by exploiting geometric constraints of landmark point inter-connectivity.
Unsupervised Object Discovery and Co-Localization by Deep Descriptor Transforming
Reusable model design becomes desirable with the rapid expansion of computer vision and machine learning applications.
Ranked #11 on
Single-object discovery
on COCO_20k
Deep Descriptor Transforming for Image Co-Localization
Reusable model design becomes desirable with the rapid expansion of machine learning applications.
Adversarial PoseNet: A Structure-aware Convolutional Network for Human Pose Estimation
In contrast, human vision is able to predict poses by exploiting geometric constraints of joint inter-connectivity.
Ranked #15 on
Pose Estimation
on MPII Human Pose
Mask-CNN: Localizing Parts and Selecting Descriptors for Fine-Grained Image Recognition
Fine-grained image recognition is a challenging computer vision problem, due to the small inter-class variations caused by highly similar subordinate categories, and the large intra-class variations in poses, scales and rotations.
Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval
Moreover, on general image retrieval datasets, SCDA achieves comparable retrieval results with state-of-the-art general image retrieval approaches.
Deep Spatial Pyramid: The Devil is Once Again in the Details
In this paper we show that by carefully making good choices for various detailed but important factors in a visual recognition framework using deep learning features, one can achieve a simple, efficient, yet highly accurate image classification system.
Weakly Supervised Fine-Grained Image Categorization
Most existing works heavily rely on object / part detectors to build the correspondence between object parts by using object or object part annotations inside training images.
