18 citations · 26 across the 11 of their papers we have counts for
13 papers
Flow-CLIP: Image-Conditioned Residual Scoring for CompositionalImage-Text Matching
Minghong Wei, Enguang Zhu, Pu Cao +2
Vision--language pre-training has enabled substantial progress in image--text retrieval and zero-shot recognition by learning transferable cross-modal representations. However, sen…
Query, Align, and Distill: Navigation-Aware Cross-Modal Interaction for Efficient Vision-and-Language Navigation
Zhihao Chen, Yiyuan Ge, Ziyang Wang +2
Recent large-scale Vision-and-Language Navigation (VLN) models deliver strong accuracy but remain costly to deploy due to their large parameter counts and computational requirement…
PointRL: Learning Point-Level Vision-Language Grounding from Verifiable Annotation Evidence
Jingyang Su, Pu Cao, Xiuze Jin +3
Vision-language models (VLMs) increasingly rely on point coordinates as a compact and executable interface for visual grounding in GUI interaction, robotic manipulation, and intera…
DrawAI: Agentic Benchmark and Workflow for Making Raster Images Editable
Pu Cao, Qingye Kong, Xuedan Yin +5
Recent image-generation models and multimodal agents can produce high-quality visuals for increasingly complex visual communication tasks. Yet their raster outputs remain difficult…
A Tilted Seesaw: Revisiting Autoencoder Trade-off for Controllable Diffusion
Pu Cao, Yiyang Ma, Feng Zhou +3
In latent diffusion models, the autoencoder (AE) is typically expected to balance two capabilities: faithful reconstruction and a generation-friendly latent space (e.g., low gFID).…
Preliminary Explorations with GPT-4o(mni) Native Image Generation
Pu Cao, Feng Zhou, Junyi Ji +8
Recently, the visual generation ability by GPT-4o(mni) has been unlocked by OpenAI. It demonstrates a very remarkable generation capability with excellent multimodal condition unde…
Exploring Position Encoding in Diffusion U-Net for Training-free High-resolution Image Generation
Feng Zhou, Pu Cao, Yiyang Ma +2
Denoising higher-resolution latents via a pre-trained U-Net leads to repetitive and disordered image patterns. Although recent studies make efforts to improve generative quality by…
Frequency-based Matcher for Long-tailed Semantic Segmentation
Shan Li, Lu Yang, Pu Cao +2
The successful application of semantic segmentation technology in the real world has been among the most exciting achievements in the computer vision community over the past decade…
E4C: Enhance Editability for Text-Based Image Editing by Harnessing Efficient CLIP Guidance
Tianrui Huang, Pu Cao, Lu Yang +4
Diffusion-based image editing is a composite process of preserving the source image content and generating new content or applying modifications. While current editing approaches h…
Controllable Generation with Text-to-Image Diffusion Models: A Survey
Pu Cao, Feng Zhou, Qing Song +1
In the rapidly advancing realm of visual generation, diffusion models have revolutionized the landscape, marking a significant shift in capabilities with their impressive text-guid…
Image is All You Need to Empower Large-scale Diffusion Models for In-Domain Generation
Pu Cao, Feng Zhou, Lu Yang +2
In-domain generation aims to perform a variety of tasks within a specific domain, such as unconditional generation, text-to-image, image editing, 3D generation, and more. Early res…
What Decreases Editing Capability? Domain-Specific Hybrid Refinement for Improved GAN Inversion
Pu Cao, Lu Yang, Dongxv Liu +3
Recently, inversion methods have focused on additional high-rate information in the generator (e.g., weights or intermediate features) to refine inversion and editing results from…
LSAP: Rethinking Inversion Fidelity, Perception and Editability in GAN Latent Space
Xuekun Zhao, Pu Cao, Xiaoya Yang +3
As research on image inversion advances, the process is generally divided into two stages. The first step is Image Embedding, involves using an encoder or optimization procedure to…