3 citations · 10 across the 12 of their papers we have counts for
12 papers · 1 filter
Let RGB Be the Language of Vision
Timing Yang, Jinrui Yang, Xinlong Li +11
This work introduces a unified formulation for vision models, where diverse forms of visual information beyond natural images, such as masks, depth maps, and other structured visua…
RATS! Patches Talk Through Registers: Emergent Parts in Register Attention Transformers
Timing Yang, Predrag Neskovic, Jansen Seheult +4
When humans see a bird, they recognize far more than just "bird" -- they see a head, wings, and talons, a structured assembly of reusable parts that can be identified across every…
ViT-5: Vision Transformers for The Mid-2020s
Feng Wang, Sucheng Ren, Tiezheng Zhang +4
This work presents a systematic investigation into modernizing Vision Transformer backbones by leveraging architectural advancements from the past five years. While preserving the…
WorldEdit: Towards Open-World Image Editing with a Knowledge-Informed Benchmark
Wang Lin, Feng Wang, Majun Zhang +7
Recent advances in image editing models have demonstrated remarkable capabilities in executing explicit instructions, such as attribute manipulation, style transfer, and pose synth…
ViMix-14M: A Curated Multi-Source Video-Text Dataset with Long-Form, High-Quality Captions and Crawl-Free Access
Timing Yang, Sucheng Ren, Alan Yuille +1
Text-to-video generation has surged in interest since Sora, yet open-source models still face a data bottleneck: there is no large, high-quality, easily obtainable video-text corpu…
RNN as Linear Transformer: A Closer Investigation into Representational Potentials of Visual Mamba Models
Timing Yang, Guoyizhe Wei, Alan Yuille +1
Mamba has recently garnered attention as an effective backbone for vision tasks. However, its underlying mechanism in visual domains remains poorly understood. In this work, we sys…
Scaling Laws in Patchification: An Image Is Worth 50,176 Tokens And More
Feng Wang, Yaodong Yu, Guoyizhe Wei +4
Since the introduction of Vision Transformer (ViT), patchification has long been regarded as a de facto image tokenization approach for plain visual architectures. By compressing t…
M-VAR: Decoupled Scale-wise Autoregressive Modeling for High-Quality Image Generation
Sucheng Ren, Yaodong Yu, Nataniel Ruiz +3
There exists recent work in computer vision, named VAR, that proposes a new autoregressive paradigm for image generation. Diverging from the vanilla next-token prediction, VAR stru…
Adventurer: Optimizing Vision Mamba Architecture Designs for Efficiency
Feng Wang, Timing Yang, Yaodong Yu +7
In this work, we introduce the Adventurer series models where we treat images as sequences of patch tokens and employ uni-directional language models to learn visual representation…
Autoregressive Pretraining with Mamba in Vision
Sucheng Ren, Xianhang Li, Haoqin Tu +9
The vision community has started to build with the recently developed state space model, Mamba, as the new backbone for a range of tasks. This paper shows that Mamba's visual capab…
Mamba-R: Vision Mamba ALSO Needs Registers
Feng Wang, Jiahao Wang, Sucheng Ren +6
Similar to Vision Transformers, this paper identifies artifacts also present within the feature maps of Vision Mamba. These artifacts, corresponding to high-norm tokens emerging in…
SCLIP: Rethinking Self-Attention for Dense Vision-Language Inference
Feng Wang, Jieru Mei, Alan Yuille
Recent advances in contrastive language-image pretraining (CLIP) have demonstrated strong capabilities in zero-shot classification by aligning visual representations with target te…