4 citations · 10 across the 8 of their papers we have counts for
8 papers
Mise-en-Scène: Implicit Layout Emergence in Diffusion Transformers for Human-AI Design Co-Creation
Zipeng Xu, Ryan Murdock, Umberto Michieli
Automating graphic design synthesis from user-provided elements requires both a coherent overall composition and the exact preservation of each asset. Existing methods predict a la…
SpectralCLIP: Preventing Artifacts in Text-Guided Style Transfer from a Spectral Perspective
Zipeng Xu, Songlong Xing, Enver Sangineto +1
Owing to the power of vision-language foundation models, e.g., CLIP, the area of image synthesis has seen recent important advances. Particularly, for style transfer, CLIP enables…
StylerDALLE: Language-Guided Style Transfer Using a Vector-Quantized Tokenizer of a Large-Scale Generative Model
Zipeng Xu, Enver Sangineto, Nicu Sebe
Despite the progress made in the style transfer task, most previous work focus on transferring only relatively simple features like color or texture, while missing more abstract co…
Spot the Difference: A Cooperative Object-Referring Game in Non-Perfectly Co-Observable Scene
Duo Zheng, Fandong Meng, Qingyi Si +5
Visual dialog has witnessed great progress after introducing various vision-oriented goals into the conversation, especially such as GuessWhich and GuessWhat, where the only image…
Predict, Prevent, and Evaluate: Disentangled Text-Driven Image Manipulation Empowered by Pre-Trained Vision-Language Model
Zipeng Xu, Tianwei Lin, Hao Tang +6
To achieve disentangled image manipulation, previous works depend heavily on manual annotation. Meanwhile, the available manipulations are limited to a pre-defined set the models w…
Enhancing Visual Dialog Questioner with Entity-based Strategy Learning and Augmented Guesser
Duo Zheng, Zipeng Xu, Fandong Meng +3
Considering the importance of building a good Visual Dialog (VD) Questioner, many researchers study the topic under a Q-Bot-A-Bot image-guessing game setting, where the Questioner…
Modeling Explicit Concerning States for Reinforcement Learning in Visual Dialogue
Zipeng Xu, Fandong Meng, Xiaojie Wang +3
To encourage AI agents to conduct meaningful Visual Dialogue (VD), the use of Reinforcement Learning has been proven potential. In Reinforcement Learning, it is crucial to represen…
Answer-Driven Visual State Estimator for Goal-Oriented Visual Dialogue
Zipeng Xu, Fangxiang Feng, Xiaojie Wang +3
A goal-oriented visual dialogue involves multi-turn interactions between two agents, Questioner and Oracle. During which, the answer given by Oracle is of great significance, as it…