[Submitted on 20 Oct 2026]
Title:When Does Sharing Work? Scaling a Shared SigLIP2 Across Image, Video, Audio, and Audio-Visual Representations
View a PDF of the paper titled When Does Sharing Work? Scaling a Shared SigLIP2 Across Image, Video, Audio, and Audio-Visual Representations, by Xiaofei Sun and 6 other authors
View PDF HTML (experimental)
Abstract:Contrastive pretraining has advanced image-text understanding, yet image, video, and audio encoders are still trained independently, with cross-modal alignment realized mainly through post-hoc binding to a frozen anchor space. We present Omni-Encoder, a general multimodal encoder that processes image, video, audio, and audio-visual inputs within a single SigLIP-2 Vision Transformer with fully shared parameters. Unlike anchor-centric binding or per-modality adapters, all modalities are optimized jointly end-to-end, and the framework is organized from input to training. A modality-native input stage first maps each signal into the shared embedding space, including a 1D-convolutional audio tokenizer that converts mel-spectrograms into tokens aligned with visual patches. A multimodal rotary position embedding parameterized by absolute timestamps then places visual frames and audio segments in a unified spatiotemporal coordinate system. Finally, alternating-modality training interleaves image, video, audio, and temporally aligned audio-visual batches and accumulates their gradients into one update, which we find central to stable joint optimization. Omni-Encoder achieves competitive results on 16 benchmarks across all four modalities with one shared parameter set, and joint training improves each modality over its separately trained counterpart. Code and models will be publicly released.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2611.05406 [cs.CV]
(or arXiv:2611.05406v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2611.05406
arXiv-issued DOI via DataCite
Submission history
From: Xiaofei Sun [view email] [v1] Tue, 20 Oct 2026 11:19:25 UTC (59 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled When Does Sharing Work? Scaling a Shared SigLIP2 Across Image, Video, Audio, and Audio-Visual Representations, by Xiaofei Sun and 6 other authors
View PDF
HTML (experimental)
TeX Source
view license
Additional Features
Audio Summary
Current browse context:
cs.CV
new | recent | 2026-11
Change to browse by:
cs cs.AI
References & Citations
SciX
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)