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Domain Prompt Learning for Efficiently Adapting CLIP to Unseen Domains
Xin Zhang, Shixiang Shane Gu, Yutaka Matsuo +1
Domain generalization (DG) is a difficult transfer learning problem aiming to learn a generalizable model for unseen domains. Recent foundation models (FMs) are robust to many dist…
Improving the Robustness to Variations of Objects and Instructions with a Neuro-Symbolic Approach for Interactive Instruction Following
Kazutoshi Shinoda, Yuki Takezawa, Masahiro Suzuki +2
An interactive instruction following task has been proposed as a benchmark for learning to map natural language instructions and first-person vision into sequences of actions to in…
Estimating Disentangled Belief about Hidden State and Hidden Task for Meta-RL
Kei Akuzawa, Yusuke Iwasawa, Yutaka Matsuo
There is considerable interest in designing meta-reinforcement learning (meta-RL) algorithms, which enable autonomous agents to adapt new tasks from small amount of experience. In…
Group Equivariant Conditional Neural Processes
Makoto Kawano, Wataru Kumagai, Akiyoshi Sannai +2
We present the group equivariant conditional neural process (EquivCNP), a meta-learning method with permutation invariance in a data set as in conventional conditional neural proce…
Wheelchair Behavior Recognition for Visualizing Sidewalk Accessibility by Deep Neural Networks
Takumi Watanabe, Hiroki Takahashi, Goh Sato +3
This paper introduces our methodology to estimate sidewalk accessibilities from wheelchair behavior via a triaxial accelerometer in a smartphone installed under a wheelchair seat.…