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Found 10 papers, 8 papers with code
Co-Supervised Learning: Improving Weak-to-Strong Generalization with Hierarchical Mixture of Experts
Steering the behavior of a strong model pre-trained on internet-scale data can be difficult due to the scarcity of competent supervisors.
Sim-to-Real Causal Transfer: A Metric Learning Approach to Causally-Aware Interaction Representations
Modeling spatial-temporal interactions among neighboring agents is at the heart of multi-agent problems such as motion forecasting and crowd navigation.
On Pitfalls of Test-Time Adaptation
Test-Time Adaptation (TTA) has recently emerged as a promising approach for tackling the robustness challenge under distribution shifts.
Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning
Recent years have seen a surge of interest in learning high-level causal representations from low-level image pairs under interventions.
Motion Style Transfer: Modular Low-Rank Adaptation for Deep Motion Forecasting
To this end, we introduce two components that exploit our prior knowledge of motion style shifts: (i) a low-rank motion style adapter that projects and adjusts the style features at a low-dimensional bottleneck; and (ii) a modular adapter strategy that disentangles the features of scene context and motion history to facilitate a fine-grained choice of adaptation layers.
TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive?
In this work, we first provide an in-depth look at its limitations and show that TTT can possibly deteriorate, instead of improving, the test-time performance in the presence of severe distribution shifts.
Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective
Learning behavioral patterns from observational data has been a de-facto approach to motion forecasting.
Social NCE: Contrastive Learning of Socially-aware Motion Representations
Learning socially-aware motion representations is at the core of recent advances in multi-agent problems, such as human motion forecasting and robot navigation in crowds.
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Collaborative Sampling in Generative Adversarial Networks
The standard practice in Generative Adversarial Networks (GANs) discards the discriminator during sampling.
Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning
We propose to (i) rethink pairwise interactions with a self-attention mechanism, and (ii) jointly model Human-Robot as well as Human-Human interactions in the deep reinforcement learning framework.
