348 citations · 1.9k across the 91 of their papers we have counts for
91 papers
OpenBias: Open-set Bias Detection in Text-to-Image Generative Models
Moreno D'Incà, Elia Peruzzo, Massimiliano Mancini +6
Text-to-image generative models are becoming increasingly popular and accessible to the general public. As these models see large-scale deployments, it is necessary to deeply inves…
Learning Trimaps via Clicks for Image Matting
Chenyi Zhang, Yihan Hu, Henghui Ding +3
Despite significant advancements in image matting, existing models heavily depend on manually-drawn trimaps for accurate results in natural image scenarios. However, the process of…
StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Text
Roberto Henschel, Levon Khachatryan, Hayk Poghosyan +5
Text-to-video diffusion models enable the generation of high-quality videos that follow text instructions, making it easy to create diverse and individual content. However, existin…
Social Reward: Evaluating and Enhancing Generative AI through Million-User Feedback from an Online Creative Community
Arman Isajanyan, Artur Shatveryan, David Kocharyan +2
Social reward as a form of community recognition provides a strong source of motivation for users of online platforms to engage and contribute with content. The recent progress of…
VASE: Object-Centric Appearance and Shape Manipulation of Real Videos
Elia Peruzzo, Vidit Goel, Dejia Xu +5
Recently, several works tackled the video editing task fostered by the success of large-scale text-to-image generative models. However, most of these methods holistically edit the…
HD-Painter: High-Resolution and Prompt-Faithful Text-Guided Image Inpainting with Diffusion Models
Hayk Manukyan, Andranik Sargsyan, Barsegh Atanyan +3
Recent progress in text-guided image inpainting, based on the unprecedented success of text-to-image diffusion models, has led to exceptionally realistic and visually plausible res…
Diffusion for Natural Image Matting
Yihan Hu, Yiheng Lin, Wei Wang +3
We aim to leverage diffusion to address the challenging image matting task. However, the presence of high computational overhead and the inconsistency of noise sampling between the…
Multi-Concept T2I-Zero: Tweaking Only The Text Embeddings and Nothing Else
Hazarapet Tunanyan, Dejia Xu, Shant Navasardyan +2
Recent advances in text-to-image diffusion models have enabled the photorealistic generation of images from text prompts. Despite the great progress, existing models still struggle…
Learning Mask-aware CLIP Representations for Zero-Shot Segmentation
Siyu Jiao, Yunchao Wei, Yaowei Wang +2
Recently, pre-trained vision-language models have been increasingly used to tackle the challenging zero-shot segmentation task. Typical solutions follow the paradigm of first gener…
Interactive Neural Painting
Elia Peruzzo, Willi Menapace, Vidit Goel +9
In the last few years, Neural Painting (NP) techniques became capable of producing extremely realistic artworks. This paper advances the state of the art in this emerging research…
Reference-based Painterly Inpainting via Diffusion: Crossing the Wild Reference Domain Gap
Dejia Xu, Xingqian Xu, Wenyan Cong +2
Have you ever imagined how it would look if we placed new objects into paintings? For example, what would it look like if we placed a basketball into Claude Monet's ``Water Lilies,…
FarSight: A Physics-Driven Whole-Body Biometric System at Large Distance and Altitude
Feng Liu, Ryan Ashbaugh, Nicholas Chimitt +17
Whole-body biometric recognition is an important area of research due to its vast applications in law enforcement, border security, and surveillance. This paper presents the end-to…
Matting Anything
Jiachen Li, Jitesh Jain, Humphrey Shi
In this paper, we propose the Matting Anything Model (MAM), an efficient and versatile framework for estimating the alpha matte of any instance in an image with flexible and intera…
Prompt-Free Diffusion: Taking "Text" out of Text-to-Image Diffusion Models
Xingqian Xu, Jiayi Guo, Zhangyang Wang +3
Text-to-image (T2I) research has grown explosively in the past year, owing to the large-scale pre-trained diffusion models and many emerging personalization and editing approaches.…
Zero-shot Generative Model Adaptation via Image-specific Prompt Learning
Jiayi Guo, Chaofei Wang, You Wu +6
Recently, CLIP-guided image synthesis has shown appealing performance on adapting a pre-trained source-domain generator to an unseen target domain. It does not require any target-d…
Automatic High Resolution Wire Segmentation and Removal
Mang Tik Chiu, Xuaner Zhang, Zijun Wei +7
Wires and powerlines are common visual distractions that often undermine the aesthetics of photographs. The manual process of precisely segmenting and removing them is extremely te…
Forget-Me-Not: Learning to Forget in Text-to-Image Diffusion Models
Eric Zhang, Kai Wang, Xingqian Xu +2
The unlearning problem of deep learning models, once primarily an academic concern, has become a prevalent issue in the industry. The significant advances in text-to-image generati…
Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators
Levon Khachatryan, Andranik Movsisyan, Vahram Tadevosyan +4
Recent text-to-video generation approaches rely on computationally heavy training and require large-scale video datasets. In this paper, we introduce a new task of zero-shot text-t…
Graph Transformer GANs for Graph-Constrained House Generation
Hao Tang, Zhenyu Zhang, Humphrey Shi +5
We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations in an end-to-end fashion for the challenging graph-constrained h…
OneFormer: One Transformer to Rule Universal Image Segmentation
Jitesh Jain, Jiachen Li, MangTik Chiu +3
Universal Image Segmentation is not a new concept. Past attempts to unify image segmentation in the last decades include scene parsing, panoptic segmentation, and, more recently, n…
Mask Matching Transformer for Few-Shot Segmentation
Siyu Jiao, Gengwei Zhang, Shant Navasardyan +4
In this paper, we aim to tackle the challenging few-shot segmentation task from a new perspective. Typical methods follow the paradigm to firstly learn prototypical features from s…
Boosted Dynamic Neural Networks
Haichao Yu, Haoxiang Li, Gang Hua +2
Early-exiting dynamic neural networks (EDNN), as one type of dynamic neural networks, has been widely studied recently. A typical EDNN has multiple prediction heads at different la…
Image Completion with Heterogeneously Filtered Spectral Hints
Xingqian Xu, Shant Navasardyan, Vahram Tadevosyan +3
Image completion with large-scale free-form missing regions is one of the most challenging tasks for the computer vision community. While researchers pursue better solutions, drawb…
Versatile Diffusion: Text, Images and Variations All in One Diffusion Model
Xingqian Xu, Zhangyang Wang, Eric Zhang +2
Recent advances in diffusion models have set an impressive milestone in many generation tasks, and trending works such as DALL-E2, Imagen, and Stable Diffusion have attracted great…