1 citations · 1 across the 3 of their papers we have counts for
4 papers
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…
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…
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…
Channel DropBlock: An Improved Regularization Method for Fine-Grained Visual Classification
Yifeng Ding, Shuwei Dong, Yujun Tong +3
Classifying the sub-categories of an object from the same super-category (e.g., bird) in a fine-grained visual classification (FGVC) task highly relies on mining multiple discrimin…