200 research outputs found
GraspGPT: Leveraging Semantic Knowledge from a Large Language Model for Task-Oriented Grasping
Task-oriented grasping (TOG) refers to the problem of predicting grasps on an
object that enable subsequent manipulation tasks. To model the complex
relationships between objects, tasks, and grasps, existing methods incorporate
semantic knowledge as priors into TOG pipelines. However, the existing semantic
knowledge is typically constructed based on closed-world concept sets,
restraining the generalization to novel concepts out of the pre-defined sets.
To address this issue, we propose GraspGPT, a large language model (LLM) based
TOG framework that leverages the open-end semantic knowledge from an LLM to
achieve zero-shot generalization to novel concepts. We conduct experiments on
Language Augmented TaskGrasp (LA-TaskGrasp) dataset and demonstrate that
GraspGPT outperforms existing TOG methods on different held-out settings when
generalizing to novel concepts out of the training set. The effectiveness of
GraspGPT is further validated in real-robot experiments. Our code, data,
appendix, and video are publicly available at
https://sites.google.com/view/graspgpt/.Comment: 15 pages, 8 figure
Hard-aware Instance Adaptive Self-training for Unsupervised Cross-domain Semantic Segmentation
The divergence between labeled training data and unlabeled testing data is a
significant challenge for recent deep learning models. Unsupervised domain
adaptation (UDA) attempts to solve such problem. Recent works show that
self-training is a powerful approach to UDA. However, existing methods have
difficulty in balancing the scalability and performance. In this paper, we
propose a hard-aware instance adaptive self-training framework for UDA on the
task of semantic segmentation. To effectively improve the quality and diversity
of pseudo-labels, we develop a novel pseudo-label generation strategy with an
instance adaptive selector. We further enrich the hard class pseudo-labels with
inter-image information through a skillfully designed hard-aware pseudo-label
augmentation. Besides, we propose the region-adaptive regularization to smooth
the pseudo-label region and sharpen the non-pseudo-label region. For the
non-pseudo-label region, consistency constraint is also constructed to
introduce stronger supervision signals during model optimization. Our method is
so concise and efficient that it is easy to be generalized to other UDA
methods. Experiments on GTA5 to Cityscapes, SYNTHIA to Cityscapes, and
Cityscapes to Oxford RobotCar demonstrate the superior performance of our
approach compared with the state-of-the-art methods.Comment: arXiv admin note: text overlap with arXiv:2008.1219
Automated Machine Learning for Deep Recommender Systems: A Survey
Deep recommender systems (DRS) are critical for current commercial online
service providers, which address the issue of information overload by
recommending items that are tailored to the user's interests and preferences.
They have unprecedented feature representations effectiveness and the capacity
of modeling the non-linear relationships between users and items. Despite their
advancements, DRS models, like other deep learning models, employ sophisticated
neural network architectures and other vital components that are typically
designed and tuned by human experts. This article will give a comprehensive
summary of automated machine learning (AutoML) for developing DRS models. We
first provide an overview of AutoML for DRS models and the related techniques.
Then we discuss the state-of-the-art AutoML approaches that automate the
feature selection, feature embeddings, feature interactions, and system design
in DRS. Finally, we discuss appealing research directions and summarize the
survey
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