4 citations · 8 across the 11 of their papers we have counts for
15 papers
Frequency-Aware Flow Matching for High-Quality Image Generation
Sucheng Ren, Qihang Yu, Ju He +3
Flow matching models have emerged as a powerful framework for realistic image generation by learning to reverse a corruption process that progressively adds Gaussian noise. However…
Beyond Next-Token: Next-X Prediction for Autoregressive Visual Generation
Sucheng Ren, Qihang Yu, Ju He +3
Autoregressive (AR) modeling, known for its next-token prediction paradigm, underpins state-of-the-art language and visual generative models. Traditionally, a ``token'' is treated…
COCONut-PanCap: Joint Panoptic Segmentation and Grounded Captions for Fine-Grained Understanding and Generation
Xueqing Deng, Qihang Yu, Ali Athar +5
This paper introduces the COCONut-PanCap dataset, created to enhance panoptic segmentation and grounded image captioning. Building upon the COCO dataset with advanced COCONut panop…
Democratizing Text-to-Image Masked Generative Models with Compact Text-Aware One-Dimensional Tokens
Dongwon Kim, Ju He, Qihang Yu +4
Image tokenizers form the foundation of modern text-to-image generative models but are notoriously difficult to train. Furthermore, most existing text-to-image models rely on large…
1.58-bit FLUX
Chenglin Yang, Celong Liu, Xueqing Deng +4
We present 1.58-bit FLUX, the first successful approach to quantizing the state-of-the-art text-to-image generation model, FLUX.1-dev, using 1.58-bit weights (i.e., values in {-1,…
FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching
Sucheng Ren, Qihang Yu, Ju He +3
Autoregressive (AR) modeling has achieved remarkable success in natural language processing by enabling models to generate text with coherence and contextual understanding through…
Randomized Autoregressive Visual Generation
Qihang Yu, Ju He, Xueqing Deng +2
This paper presents Randomized AutoRegressive modeling (RAR) for visual generation, which sets a new state-of-the-art performance on the image generation task while maintaining ful…
MaskBit: Embedding-free Image Generation via Bit Tokens
Mark Weber, Lijun Yu, Qihang Yu +4
Masked transformer models for class-conditional image generation have become a compelling alternative to diffusion models. Typically comprising two stages - an initial VQGAN model…
An Image is Worth 32 Tokens for Reconstruction and Generation
Qihang Yu, Mark Weber, Xueqing Deng +3
Recent advancements in generative models have highlighted the crucial role of image tokenization in the efficient synthesis of high-resolution images. Tokenization, which transform…
Alleviating Distortion in Image Generation via Multi-Resolution Diffusion Models and Time-Dependent Layer Normalization
Qihao Liu, Zhanpeng Zeng, Ju He +3
This paper presents innovative enhancements to diffusion models by integrating a novel multi-resolution network and time-dependent layer normalization. Diffusion models have gained…
Enhancing Temporal Consistency in Video Editing by Reconstructing Videos with 3D Gaussian Splatting
Inkyu Shin, Qihang Yu, Xiaohui Shen +3
Recent advancements in zero-shot video diffusion models have shown promise for text-driven video editing, but challenges remain in achieving high temporal consistency. To address t…
COCONut: Modernizing COCO Segmentation
Xueqing Deng, Qihang Yu, Peng Wang +2
In recent decades, the vision community has witnessed remarkable progress in visual recognition, partially owing to advancements in dataset benchmarks. Notably, the established COC…
ViTamin: Designing Scalable Vision Models in the Vision-Language Era
Jieneng Chen, Qihang Yu, Xiaohui Shen +2
Recent breakthroughs in vision-language models (VLMs) start a new page in the vision community. The VLMs provide stronger and more generalizable feature embeddings compared to thos…
Towards Open-Ended Visual Recognition with Large Language Model
Qihang Yu, Xiaohui Shen, Liang-Chieh Chen
Localizing and recognizing objects in the open-ended physical world poses a long-standing challenge within the domain of machine perception. Recent methods have endeavored to addre…
A Simple Video Segmenter by Tracking Objects Along Axial Trajectories
Ju He, Qihang Yu, Inkyu Shin +4
Video segmentation requires consistently segmenting and tracking objects over time. Due to the quadratic dependency on input size, directly applying self-attention to video segment…