2 citations · 3 across the 5 of their papers we have counts for
6 papers
CAR-Flow: Condition-Aware Reparameterization Aligns Source and Target for Better Flow Matching
Chen Chen, Pengsheng Guo, Liangchen Song +7
Conditional generative modeling aims to learn a conditional data distribution from samples containing data-condition pairs. For this, diffusion and flow-based methods have attained…
GIE-Bench: Towards Grounded Evaluation for Text-Guided Image Editing
Yusu Qian, Jiasen Lu, Tsu-Jui Fu +5
Editing images using natural language instructions has become a natural and expressive way to modify visual content; yet, evaluating the performance of such models remains challeng…
DiT-Air: Revisiting the Efficiency of Diffusion Model Architecture Design in Text to Image Generation
Chen Chen, Rui Qian, Wenze Hu +8
In this work, we empirically study Diffusion Transformers (DiTs) for text-to-image generation, focusing on architectural choices, text-conditioning strategies, and training protoco…
CLIP-UP: A Simple and Efficient Mixture-of-Experts CLIP Training Recipe with Sparse Upcycling
Xinze Wang, Chen Chen, Yinfei Yang +5
Mixture-of-Experts (MoE) models are crucial for scaling model capacity while controlling inference costs. While integrating MoE into multimodal models like CLIP improves performanc…
Contrastive Localized Language-Image Pre-Training
Hong-You Chen, Zhengfeng Lai, Haotian Zhang +7
Contrastive Language-Image Pre-training (CLIP) has been a celebrated method for training vision encoders to generate image/text representations facilitating various applications. R…
Video Content Swapping Using GAN
Tingfung Lau, Sailun Xu, Xinze Wang
Video generation is an interesting problem in computer vision. It is quite popular for data augmentation, special effect in move, AR/VR and so on. With the advances of deep learnin…