← Back to all news
arXiv Computer Vision September 2, 2026 By Ruslan Rozumnyi, Mat\v{e}j Such\'anek, Tom\'a\v{s} Voj\'i\v{r}, Kl\'ara Janou\v{s}kov\'a, Ji\v{r}\'i Matas

Fi-ImageNet-1k: An OOD Benchmark From the Inside of the ImageNet-1k Validation Set

Read the original on arXiv Computer Vision →

The Flow has not summarised this story yet — read it at arXiv Computer Vision.

  • benchmarks

One email a morning, machine-written

One email a day, machine-written, one click to leave. We never share your address.

Related stories

arXiv Machine Learning
Jun 2

A Closer Look at In-Distribution vs. Out-of-Distribution Accuracy for Open-Set Test-time Adaptation

arXiv:2606. 01973v1 Announce Type: new Abstract: Open-set test-time adaptation (TTA) updates models on new data in the presence of input shifts and unknown output classes.

By Zefeng Li, Evan Shelhamer
benchmarks
More like this →
arXiv AI
Jul 21

GenSyn10: A Multi-Generative AI Dataset For Benchmarking Image Classification

arXiv:2607. 16283v1 Announce Type: cross Abstract: The rapid advancement of generative AI has outpaced our ability to reliably detect its outputs, particularly when detectors encounter generators they have not seen before.

By Md Faraz Kabir Khan, Saeed Anwar, Ghulam Mubashar Hassan
llmsdiffusioncomputer-visionfine-tuningmultimodalbenchmarks
More like this →
arXiv AI
Jul 8

VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection

arXiv:2607. 06254v1 Announce Type: cross Abstract: Deepfake image detection is currently served by three fundamentally different paradigms: commercial APIs, zero-shot vision-language models (LLMs), and open-source detectors.

By Sharayu N. Deshmukh, Md Rashidunnabi, Nelton Tiago Gemo, Kurundkar G. D., Mahamune M. R., Nilesh K. Deshmukh
llmsdiffusionmultimodalbenchmarkssafety
More like this →
arXiv Machine Learning
Aug 18

PWLR: Pairwise Witness Local Rejection for Boundary-Aware Out-of-Distribution Detection

arXiv:2608. 15802v1 Announce Type: cross Abstract: Out-of-distribution (OOD) detection remains challenging for image classifiers, especially when near-OOD samples lie close to in-distribution (ID) class boundaries.

By Chengyao Jia, Ruixuan Wang
llmsmultimodalbenchmarks
More like this →
Hugging Face Trending Papers
Aug 16

PWLR: Pairwise Witness Local Rejection for Boundary-Aware Out-of-Distribution Detection

Out-of-distribution (OOD) detection remains challenging for image classifiers, especially when near-OOD samples lie close to in-distribution (ID) class boundaries. Recent vision-language detectors imp...

llmsmultimodalbenchmarks
More like this →
arXiv AI
Jun 24

Zero-Shot Test-Time Canonicalization using Out-of-Distribution Scoring

arXiv:2606. 24178v1 Announce Type: cross Abstract: Pretrained vision models often misclassify inputs that are rotated, scaled, or sheared, even though these affine transformations leave the object class unchanged.

By Dominik Lindner, Johann Schmidt, Tom Siegl, Martin Becker, Sebastian Stober
benchmarks
More like this →
About Pricing API Newsletter Sources Privacy Terms Refunds Accessibility Provider info Contact RSS

The Flow links to publishers and never republishes their articles. Summaries are machine-generated.

v1.1.0 · 5f852ea