Search Results for author:
Found 24 papers, 18 papers with code
DemoFusion: Democratising High-Resolution Image Generation With No $$$
High-resolution image generation with Generative Artificial Intelligence (GenAI) has immense potential but, due to the enormous capital investment required for training, it is increasingly centralised to a few large corporations, and hidden behind paywalls.
An Erudite Fine-Grained Visual Classification Model
Therefore, we first propose a feature disentanglement module and a feature re-fusion module to reduce negative transfer and boost positive transfer between different datasets.
On-the-Fly Category Discovery
Our code is available at https://github. com/PRIS-CV/On-the-fly-Category-Discovery.
Multi-View Active Fine-Grained Visual Recognition
Despite the remarkable progress of Fine-grained visual classification (FGVC) with years of history, it is still limited to recognizing 2 images.
Fine-Grained Image Classification
Fine-Grained Visual Recognition
Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image Classification
Conventional few-shot learning methods however cannot be naively adopted for this fine-grained setting -- a quick pilot study reveals that they in fact push for the opposite (i. e., lower inter-class variations and higher intra-class variations).
Multi-View Active Fine-Grained Recognition
As fine-grained visual classification (FGVC) being developed for decades, great works related have exposed a key direction -- finding discriminative local regions and revealing subtle differences.
Domain Generalization via Frequency-domain-based Feature Disentanglement and Interaction
Adaptation to out-of-distribution data is a meta-challenge for all statistical learning algorithms that strongly rely on the i. i. d.
Clue Me In: Semi-Supervised FGVC with Out-of-Distribution Data
Despite great strides made on fine-grained visual classification (FGVC), current methods are still heavily reliant on fully-supervised paradigms where ample expert labels are called for.
Fine-Grained Visual Classification via Simultaneously Learning of Multi-regional Multi-grained Features
Finally, we can obtain multiple discriminative regions on high-level feature channels and obtain multiple more minute regions within these discriminative regions on middle-level feature channels.
Grad-CAM guided channel-spatial attention module for fine-grained visual classification
The proposed method outperforms the SOTA attention modules in the FGVC task.
Ranked #21 on
Fine-Grained Image Classification
on FGVC Aircraft
Progressive Co-Attention Network for Fine-grained Visual Classification
Fine-grained visual classification aims to recognize images belonging to multiple sub-categories within a same category.
Ranked #32 on
Fine-Grained Image Classification
on FGVC Aircraft
Knowledge Transfer Based Fine-grained Visual Classification
Convolution neural networks (CNNs), which employ the cross entropy loss (CE-loss) as the loss function, show poor performance since the model can only learn the most discriminative part and ignore other meaningful regions.
Ranked #36 on
Fine-Grained Image Classification
on CUB-200-2011
Your "Flamingo" is My "Bird": Fine-Grained, or Not
For that, we re-envisage the traditional setting of FGVC, from single-label classification, to that of top-down traversal of a pre-defined coarse-to-fine label hierarchy -- so that our answer becomes "bird"-->"Phoenicopteriformes"-->"Phoenicopteridae"-->"flamingo".
Ranked #16 on
Fine-Grained Image Classification
on FGVC Aircraft
CC-Loss: Channel Correlation Loss For Image Classification
The loss function is a key component in deep learning models.
OSLNet: Deep Small-Sample Classification with an Orthogonal Softmax Layer
A deep neural network of multiple nonlinear layers forms a large function space, which can easily lead to overfitting when it encounters small-sample data.
GPCA: A Probabilistic Framework for Gaussian Process Embedded Channel Attention
In this paper, we propose Gaussian process embedded channel attention (GPCA) module and further interpret the channel attention schemes in a probabilistic way.
Dual-attention Guided Dropblock Module for Weakly Supervised Object Localization
This module contains two key components, the channel attention guided dropout (CAGD) and the spatial attention guided dropblock (SAGD).
Mind the Gap: Enlarging the Domain Gap in Open Set Domain Adaptation
The key insight lies with how we exploit the mutually beneficial information between two networks; (a) to separate samples of known and unknown classes, (b) to maximize the domain confusion between source and target domain without the influence of unknown samples.
Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches
In this work, we propose a novel framework for fine-grained visual classification to tackle these problems.
Ranked #17 on
Fine-Grained Image Classification
on Stanford Cars
The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification
The proposed loss function, termed as mutual-channel loss (MC-Loss), consists of two channel-specific components: a discriminality component and a diversity component.
Ranked #29 on
Fine-Grained Image Classification
on FGVC Aircraft
Weakly Supervised Attention Pyramid Convolutional Neural Network for Fine-Grained Visual Classification
Classifying the sub-categories of an object from the same super-category (e. g. bird species, car and aircraft models) in fine-grained visual classification (FGVC) highly relies on discriminative feature representation and accurate region localization.
Competing Ratio Loss for Discriminative Multi-class Image Classification
However, during the training of the deep convolutional neural network, the value of NLLR is not always positive or negative, which severely affects the convergence of NLLR.
Channel Max Pooling Layer for Fine-Grained Vehicle Classification
Experimental results on two fine-grained vehicle datasets, the Stanford Cars-196 dataset and the Comp Cars dataset, demonstrate that the proposed layer could improve classification accuracies of deep neural networks on fine-grained vehicle classification in the situation that a massive of parameters are reduced.
