Cordelia Schmid

Learning with Neighbor Consistency for Noisy Labels

2022 · arXiv (Cornell University)

Recent advances in deep learning have relied on large, labelled datasets to train high-capacity models. However, collecting large datasets in a time- and cost-efficient manner often results in label noise. We present a method for …

Robust Visual Sim-to-Real Transfer for Robotic Manipulation

2023

Learning visuomotor policies in simulation is much safer and cheaper than in the real world. However, due to discrepancies between the simulated and real data, simulator-trained policies often fail when transferred to real robots. One …

Learning Correlation Structures for Vision Transformers

2024 · arXiv (Cornell University)

We introduce a new attention mechanism, dubbed structural self-attention (StructSA), that leverages rich correlation patterns naturally emerging in key-query interactions of attention. StructSA generates attention maps by recognizing space-time structures of key-query correlations via convolution …

InteractVLM: 3D Interaction Reasoning from 2D Foundational Models

2025

We introduce InteractVLM, a novel method to estimate 3D contact points on human bodies and objects from single in- the-wild images, enabling accurate human-object joint reconstruction in 3D. This is challenging due to occlusions, depth …