·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Nemotron 3.5 Content Safety: Customizable Multimodal Safety for Global Enterprise AI23m◆Kevin O’Leary agrees to downsize massive Utah data center42m◆Meta rolls out a new AI creator assistant on Facebook2h◆What to expect from WWDC 2026: Siri’s highly anticipated revamp and Apple Intelligence updates2h◆Is Silicon Valley ready to put robots in people’s homes? Hello Robot is.4h◆TSMC struggles to keep up with AI demand: ‘We can only support so much’5h◆Apple touts $1.4 trillion in App Store billings and sales, 90% without a commission5h◆Elon Musk is steamrolling Wall Street to become a trillionaire5h◆How to Fine-Tune Nemotron 3.5 ASR for Your Language, Domain, or Accent6h◆Let us filter AI slop, you cowards6h◆EVA-Bench Data 2.0: 3 Domains, 121 Tools, 213 Scenarios6h◆AI leaders call for tougher protections against AI-aided bioweapons7h◆How Endava is redesigning software delivery around AI agents7h◆Task-Seeded Synthetic Q&A Generation for Nemotron Pretraining7h◆How courts are coping with a flood of AI-generated lawsuits8h◆Amazon develops a warehouse robot that workers can speak to9h◆Dreaming: Better memory for a more helpful ChatGPT10h◆SFMambaNet: Spectral-Frequency Enhanced Selective State Space Model for Correspondence Pruning15h◆Optical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning15h◆Dynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models15h◆Nemotron 3.5 Content Safety: Customizable Multimodal Safety for Global Enterprise AI23m◆Kevin O’Leary agrees to downsize massive Utah data center42m◆Meta rolls out a new AI creator assistant on Facebook2h◆What to expect from WWDC 2026: Siri’s highly anticipated revamp and Apple Intelligence updates2h◆Is Silicon Valley ready to put robots in people’s homes? Hello Robot is.4h◆TSMC struggles to keep up with AI demand: ‘We can only support so much’5h◆Apple touts $1.4 trillion in App Store billings and sales, 90% without a commission5h◆Elon Musk is steamrolling Wall Street to become a trillionaire5h◆How to Fine-Tune Nemotron 3.5 ASR for Your Language, Domain, or Accent6h◆Let us filter AI slop, you cowards6h◆EVA-Bench Data 2.0: 3 Domains, 121 Tools, 213 Scenarios6h◆AI leaders call for tougher protections against AI-aided bioweapons7h◆How Endava is redesigning software delivery around AI agents7h◆Task-Seeded Synthetic Q&A Generation for Nemotron Pretraining7h◆How courts are coping with a flood of AI-generated lawsuits8h◆Amazon develops a warehouse robot that workers can speak to9h◆Dreaming: Better memory for a more helpful ChatGPT10h◆SFMambaNet: Spectral-Frequency Enhanced Selective State Space Model for Correspondence Pruning15h◆Optical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning15h◆Dynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models15h◆
News/CLIP Tricks You: Training-free Token Pruning for Efficient Pixel Grounding in Large VIsion-Language Models
arxiv
PublishedJune 2, 2026 at 4:00 AM

CLIP Tricks You: Training-free Token Pruning for Efficient Pixel Grounding in Large VIsion-Language Models

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2605.13178v2 Announce Type: replace-cross Abstract: In large vision-language models, visual tokens typically constitute the majority of input tokens, leading to substantial computational overhead. To address this, recent studies have explored pruning redundant or less informative visual tokens

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivSFMambaNet: Spectral-Frequency Enhanced Selective State Space Model for Correspondence Pruning15harxivOptical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning15harxivDynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models15h
The Bubble Brief
WEEKLY

Read AI insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews