15 citations · 19 across the 16 of their papers we have counts for
5 papers · 1 filter
BOP-ASK: Object-Interaction Reasoning for Vision-Language Models
Vineet Bhat, Sungsu Kim, Valts Blukis +6
Vision Language Models (VLMs) have achieved impressive performance on spatial reasoning benchmarks, yet these evaluations mask critical weaknesses in understanding object interacti…
Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail
NVIDIA, :, Yan Wang +41
End-to-end architectures trained via imitation learning have advanced autonomous driving by scaling model size and data, yet performance remains brittle in safety-critical long-tai…
Small Language Models are the Future of Agentic AI
Peter Belcak, Greg Heinrich, Shizhe Diao +5
Large language models (LLMs) are often praised for exhibiting near-human performance on a wide range of tasks and valued for their ability to hold a general conversation. The rise…
Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation
Peter Belcak, Greg Heinrich, Jan Kautz +1
Finetuning language models for a new domain inevitably leads to the deterioration of their general performance. This becomes more pronounced the more limited the finetuning data re…
FeatSharp: Your Vision Model Features, Sharper
Mike Ranzinger, Greg Heinrich, Pavlo Molchanov +3
The feature maps of vision encoders are fundamental to myriad modern AI tasks, ranging from core perception algorithms (e.g. semantic segmentation, object detection, depth percepti…