16 citations · 40 across the 12 of their papers we have counts for
13 papers
SemVideo: Reconstructs What You Watch from Brain Activity via Hierarchical Semantic Guidance
Minghan Yang, Lan Yang, Ke Li +3
Reconstructing dynamic visual experiences from brain activity provides a compelling avenue for exploring the neural mechanisms of human visual perception. While recent progress in…
SynMind: Reducing Semantic Hallucination in fMRI-Based Image Reconstruction
Lan Yang, Minghan Yang, Ke Li +3
Recent advances in fMRI-based image reconstruction have achieved remarkable photo-realistic fidelity. Yet, a persistent limitation remains: while reconstructed images often appear…
4-Doodle: Text to 3D Sketches that Move!
Hao Chen, Jiaqi Wang, Yonggang Qi +3
We present a novel task: text-to-3D sketch animation, which aims to bring freeform sketches to life in dynamic 3D space. Unlike prior works focused on photorealistic content genera…
Annotation-Free Human Sketch Quality Assessment
Lan Yang, Kaiyue Pang, Honggang Zhang +1
As lovely as bunnies are, your sketched version would probably not do them justice (Fig.~\ref{fig:intro}). This paper recognises this very problem and studies sketch quality assess…
VersaGen: Unleashing Versatile Visual Control for Text-to-Image Synthesis
Zhipeng Chen, Lan Yang, Yonggang Qi +4
Despite the rapid advancements in text-to-image (T2I) synthesis, enabling precise visual control remains a significant challenge. Existing works attempted to incorporate multi-face…
Wired Perspectives: Multi-View Wire Art Embraces Generative AI
Zhiyu Qu, Lan Yang, Honggang Zhang +3
Creating multi-view wire art (MVWA), a static 3D sculpture with diverse interpretations from different viewpoints, is a complex task even for skilled artists. In response, we prese…
SketchXAI: A First Look at Explainability for Human Sketches
Zhiyu Qu, Yulia Gryaditskaya, Ke Li +3
This paper, for the very first time, introduces human sketches to the landscape of XAI (Explainable Artificial Intelligence). We argue that sketch as a ``human-centred'' data form,…
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…
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,…
Deep Factorised Inverse-Sketching
Kaiyue Pang, Da Li, Jifei Song +3
Modelling human free-hand sketches has become topical recently, driven by practical applications such as fine-grained sketch based image retrieval (FG-SBIR). Sketches are clearly r…
Universal Perceptual Grouping
Ke Li, Kaiyue Pang, Jifei Song +4
In this work we aim to develop a universal sketch grouper. That is, a grouper that can be applied to sketches of any category in any domain to group constituent strokes/segments in…
Learning to Sketch with Shortcut Cycle Consistency
Jifei Song, Kaiyue Pang, Yi-Zhe Song +2
To see is to sketch -- free-hand sketching naturally builds ties between human and machine vision. In this paper, we present a novel approach for translating an object photo to a s…
SketchMate: Deep Hashing for Million-Scale Human Sketch Retrieval
Peng Xu, Yongye Huang, Tongtong Yuan +6
We propose a deep hashing framework for sketch retrieval that, for the first time, works on a multi-million scale human sketch dataset. Leveraging on this large dataset, we explore…