7 citations · 18 across the 13 of their papers we have counts for
7 papers · 1 filter
Less is More: Removing Text-regions Improves CLIP Training Efficiency and Robustness
Liangliang Cao, Bowen Zhang, Chen Chen +5
The CLIP (Contrastive Language-Image Pre-training) model and its variants are becoming the de facto backbone in many applications. However, training a CLIP model from hundreds of m…
Edit Everything: A Text-Guided Generative System for Images Editing
Defeng Xie, Ruichen Wang, Jian Ma +5
We introduce a new generative system called Edit Everything, which can take image and text inputs and produce image outputs. Edit Everything allows users to edit images using simpl…
GlyphDraw: Seamlessly Rendering Text with Intricate Spatial Structures in Text-to-Image Generation
Jian Ma, Mingjun Zhao, Chen Chen +4
Recent breakthroughs in the field of language-guided image generation have yielded impressive achievements, enabling the creation of high-quality and diverse images based on user i…
STAIR: Learning Sparse Text and Image Representation in Grounded Tokens
Chen Chen, Bowen Zhang, Liangliang Cao +7
Image and text retrieval is one of the foundational tasks in the vision and language domain with multiple real-world applications. State-of-the-art approaches, e.g. CLIP, ALIGN, re…
Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition
Yue He, Chen Chen, Jing Zhang +4
Existing Scene Text Recognition (STR) methods typically use a language model to optimize the joint probability of the 1D character sequence predicted by a visual recognition (VR) m…
Rain Removal By Image Quasi-Sparsity Priors
Yinglong Wang, Shuaicheng Liu, Chen Chen +2
Rain streaks will inevitably be captured by some outdoor vision systems, which lowers the image visual quality and also interferes various computer vision applications. We present…