u/j_root_

Hey everyone,

hope this is okay to post here.

My co-author and I are currently between institutional affiliations, which means we don't have the academic email arXiv needs for an endorsement. We're hoping to find someone in cs.CV willing to take a quick look at our paper and endorse it if it meets your bar.

The project: Locate-SAM2

We built a training-free pipeline connecting NVIDIA's LocateAnything-3B to Meta's SAM 2.1 through a lightweight adapter. The question we wanted to answer was simple: in a modular text-to-mask pipeline where everything is frozen, does the choice of grounder actually matter for the final mask?

A few specifics, since the details are what tell you we're not just generating noise:

On RefCOCO val, our system reaches 0.772 mIoU versus 0.717 for Grounding DINO Base, using the same SAM 2.1 backend throughout.

RefCOCO appears in LocateAnything's training data, so we frame this honestly as in-domain benchmarking, not zero-shot transfer. We're not pretending otherwise.

The paper has controlled comparisons across RefCOCO/+/g, adapter ablations, a ground-truth box oracle, a failure taxonomy, and a nonsense-prompt probe showing the pipeline needs abstention logic.

Code is on GitHub and the paper is close to submission-ready.

What we're hoping for

Mainly an endorsement: someone to read the draft and, if they think it holds up, endorse us on arXiv. We'd acknowledge it and that's the whole ask.

If anyone wants to get more involved, we're open to expanding the experiments or pointing the paper at a specific venue, and we'd talk co-authorship based on real contribution. We also have separate work in progress in physically-constrained DL, geospatial AI, and AI governance, in case any of that overlaps with what you do.

We're not looking for a blind voucher. Drop a comment or a DM and we'll share the PDF and the repo.

Happy to answer questions, and thanks for reading.

Hey everyone,

hope this is okay to post here.

My co-author and I are currently between institutional affiliations, which means we don't have the academic email arXiv needs for an endorsement. We're hoping to find someone in cs.CV willing to take a quick look at our paper and endorse it if it meets your bar.

The project: Locate-SAM2

We built a training-free pipeline connecting NVIDIA's LocateAnything-3B to Meta's SAM 2.1 through a lightweight adapter. The question we wanted to answer was simple: in a modular text-to-mask pipeline where everything is frozen, does the choice of grounder actually matter for the final mask?

A few specifics, since the details are what tell you we're not just generating noise:

On RefCOCO val, our system reaches 0.772 mIoU versus 0.717 for Grounding DINO Base, using the same SAM 2.1 backend throughout.

RefCOCO appears in LocateAnything's training data, so we frame this honestly as in-domain benchmarking, not zero-shot transfer. We're not pretending otherwise.

The paper has controlled comparisons across RefCOCO/+/g, adapter ablations, a ground-truth box oracle, a failure taxonomy, and a nonsense-prompt probe showing the pipeline needs abstention logic.

Code is on GitHub and the paper is close to submission-ready.

What we're hoping for

Mainly an endorsement: someone to read the draft and, if they think it holds up, endorse us on arXiv. We'd acknowledge it and that's the whole ask.

If anyone wants to get more involved, we're open to expanding the experiments or pointing the paper at a specific venue, and we'd talk co-authorship based on real contribution. We also have separate work in progress in physically-constrained DL, geospatial AI, and AI governance, in case any of that overlaps with what you do.

We're not looking for a blind voucher. Drop a comment or a DM and we'll share the PDF and the repo.

Happy to answer questions, and thanks for reading.

Hey everyone,

Germany is struggling with a huge IT talent shortage right now, with over 100k open tech roles. But if you look at the market for AI and advanced tech, so many jobs still list B2 or C1 German as a hard requirement.

Almost all AI research papers and everything else is done in English anyway. But even some top research labs and companies in Germany are not trying to evolve.

Is it because the existing people at these companies lack basic English skills? Are they just protecting themselves and their positions rather than thinking about the long term company goals?

If Germany wants to be a leader from Europe, it needs global talent. By putting the German language as an important criteria for tech jobs, companies are just cutting off the top talented people.

Question for the hiring managers here, what is the business logic behind this? Why hold onto the local language when it actively stops you from hiring the best AI minds? It really feels like evolving backwards.

Would love to hear some thoughts on this.