415 citations · 795 across the 30 of their papers we have counts for
6 papers · 1 filter
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…