415 citations · 795 across the 30 of their papers we have counts for
32 papers
FlexiGrad: Adaptive Gradient Modulation for Hierarchical Fine-Grained Classification
Zilu Zhou, Dongliang Chang, Junhan Chen +1
Many fine-grained recognition tasks contain hierarchical labels such as order, family and species. Although this supervision should be beneficial, jointly optimising all levels oft…
ConFusion: Continuous Fusion Space Learning for Fine-Grained Controllable Infrared and Visible Image Fusion
Guo Yurong, He Yufei, Li Yonghao +3
Controllable infrared-visible image fusion aims to integrate complementary thermal and structural information with flexible region-aware modulation, producing fused images that ada…
Reversing the Flow: Generation-to-Understanding Synergy in Large Multimodal Models
Yujun Tong, Dongliang Chang, Zijin Yin +3
The long-standing goal of multimodal AI is to build unified models in which visual understanding and visual generation mutually enhance one another. Despite recent works such as BA…
IncreFA: Breaking the Static Wall of Generative Model Attribution
Haotian Qin, Dongliang Chang, Yueying Gao +3
As AI generative models evolve at unprecedented speed, image attribution has become a moving target. New diffusion, adversarial and autoregressive generators appear almost monthly,…
Recolour What Matters: Region-Aware Colour Editing via Token-Level Diffusion
Yuqi Yang, Dongliang Chang, Yijia Ling +2
Colour is one of the most perceptually salient yet least controllable attributes in image generation. Although recent diffusion models can modify object colours from user instructi…
Seeing as Experts Do: A Knowledge-Augmented Agent for Open-Set Fine-Grained Visual Understanding
Junhan Chen, Zilu Zhou, Yujun Tong +3
Fine-grained visual understanding is shifting from static classification to knowledge-augmented reasoning, where models must justify as well as recognise. Existing approaches remai…
Controllable-Continuous Color Editing in Diffusion Model via Color Mapping
Yuqi Yang, Dongliang Chang, Yuanchen Fang +3
In recent years, text-driven image editing has made significant progress. However, due to the inherent ambiguity and discreteness of natural language, color editing still faces cha…
Towards Privacy-Preserving Fine-Grained Visual Classification via Hierarchical Learning from Label Proportions
Jinyi Chang, Dongliang Chang, Lei Chen +2
In recent years, Fine-Grained Visual Classification (FGVC) has achieved impressive recognition accuracy, despite minimal inter-class variations. However, existing methods heavily r…
Multimodal Conditional Information Bottleneck for Generalizable AI-Generated Image Detection
Haotian Qin, Dongliang Chang, Yueying Gao +3
Although existing CLIP-based methods for detecting AI-generated images have achieved promising results, they are still limited by severe feature redundancy, which hinders their gen…
Toward Generalizable Forgery Detection and Reasoning
Yueying Gao, Dongliang Chang, Bingyao Yu +5
Accurate and interpretable detection of AI-generated images is essential for mitigating risks associated with AI misuse. However, the substantial domain gap among generative models…
M4Fog: A Global Multi-Regional, Multi-Modal, and Multi-Stage Dataset for Marine Fog Detection and Forecasting to Bridge Ocean and Atmosphere
Mengqiu Xu, Ming Wu, Kaixin Chen +11
Marine fog poses a significant hazard to global shipping, necessitating effective detection and forecasting to reduce economic losses. In recent years, several machine learning (ML…
DemoFusion: Democratising High-Resolution Image Generation With No $$$
Ruoyi Du, Dongliang Chang, Timothy Hospedales +2
High-resolution image generation with Generative Artificial Intelligence (GenAI) has immense potential but, due to the enormous capital investment required for training, it is incr…
Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image Classification
Jijie Wu, Dongliang Chang, Aneeshan Sain +5
The main challenge for fine-grained few-shot image classification is to learn feature representations with higher inter-class and lower intra-class variations, with a mere few labe…
Multi-View Active Fine-Grained Recognition
Ruoyi Du, Wenqing Yu, Heqing Wang +4
As fine-grained visual classification (FGVC) being developed for decades, great works related have exposed a key direction -- finding discriminative local regions and revealing sub…
Domain Generalization via Frequency-domain-based Feature Disentanglement and Interaction
Jingye Wang, Ruoyi Du, Dongliang Chang +2
Adaptation to out-of-distribution data is a meta-challenge for all statistical learning algorithms that strongly rely on the i.i.d. assumption. It leads to unavoidable labor costs…
Clue Me In: Semi-Supervised FGVC with Out-of-Distribution Data
Ruoyi Du, Dongliang Chang, Zhanyu Ma +2
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 calle…
Making a Bird AI Expert Work for You and Me
Dongliang Chang, Kaiyue Pang, Ruoyi Du +3
As powerful as fine-grained visual classification (FGVC) is, responding your query with a bird name of "Whip-poor-will" or "Mallard" probably does not make much sense. This however…
Fine-Grained Visual Classification via Simultaneously Learning of Multi-regional Multi-grained Features
Dongliang Chang, Yixiao Zheng, Zhanyu Ma +2
Fine-grained visual classification is a challenging task that recognizes the sub-classes belonging to the same meta-class. Large inter-class similarity and intra-class variance is…
Grad-CAM guided channel-spatial attention module for fine-grained visual classification
Shuai Xu, Dongliang Chang, Jiyang Xie +1
Fine-grained visual classification (FGVC) is becoming an important research field, due to its wide applications and the rapid development of computer vision technologies. The curre…
Progressive Co-Attention Network for Fine-grained Visual Classification
Tian Zhang, Dongliang Chang, Zhanyu Ma +1
Fine-grained visual classification aims to recognize images belonging to multiple sub-categories within a same category. It is a challenging task due to the inherently subtle varia…
Knowledge Transfer Based Fine-grained Visual Classification
Siqing Zhang, Ruoyi Du, Dongliang Chang +2
Fine-grained visual classification (FGVC) aims to distinguish the sub-classes of the same category and its essential solution is to mine the subtle and discriminative regions. Conv…
Your "Flamingo" is My "Bird": Fine-Grained, or Not
Dongliang Chang, Kaiyue Pang, Yixiao Zheng +3
Whether what you see in Figure 1 is a "flamingo" or a "bird", is the question we ask in this paper. While fine-grained visual classification (FGVC) strives to arrive at the former,…
CC-Loss: Channel Correlation Loss For Image Classification
Zeyu Song, Dongliang Chang, Zhanyu Ma +2
The loss function is a key component in deep learning models. A commonly used loss function for classification is the cross entropy loss, which is a simple yet effective applicatio…
OSLNet: Deep Small-Sample Classification with an Orthogonal Softmax Layer
Xiaoxu Li, Dongliang Chang, Zhanyu Ma +5
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. To mitigate overfitting…