Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Vision Transformer (ViT) faces obstacles in wide application due to its huge computational cost.

Figure 1: A simple subsplit Bayesian network for a leaf set that contains 4 species A, B, C and D. This figure is adapted from Zhang and Matsen IV (2019). SBN (the one with a full and complete binary tree structure as shown in Figure 1) is good enough. The SBN framework also generalizes to unrooted trees, which are the most common type of phylogenetic trees. (Zhang and Matsen IV, 2018). Sampling from SBNs is also straightforward via ancestral sampling.

Task-oriented grasping (TOG) is an essential preliminary step for robotic task execution, which involves predicting grasps on regions of target objects that facilitate intended tasks. Existing literature reveals there is a limited availability of TOG datasets for training and benchmarking despite large demand, which are often synthetic or have artifacts in mask annotations that hinder model performance. Moreover, TOG solutions often require affordance masks, grasps, and object masks for training, however, existing datasets typically provide only a subset of these annotations. To address these limitations, we introduce the Top-down Task-oriented Grasping (TD-TOG) dataset, designed to train and evaluate TOG solutions. TD-TOG comprises 1,449 real-world RGB-D scenes including 30 object categories and 120 subcategories, with hand-annotated object masks, affordances, and planar rectangular grasps. It also features a test set for a novel challenge that assesses a TOG solution's ability to distinguish between object subcategories. To contribute to the demand for TOG solutions that can adapt and manipulate previously unseen objects without re-training, we propose a novel TOG framework, Binary-TOG. Binary-TOG uses zero-shot for object recognition, and one-shot learning for affordance recognition. Zero-shot learning enables Binary-TOG to identify objects in multi-object scenes through textual prompts, eliminating the need for visual references. In multi-object settings, Binary-TOG achieves an average task-oriented grasp accuracy of 68.9%. Lastly, this paper contributes a comparative analysis between one-shot and zero-shot learning for object generalization in TOG to be used in the development of future TOG solutions.

In this paper the authors propose an efficient method for tree probability estimation (given a collection of trees) that relies on the description of trees as subsplit Bayesian networks. Through this representation, the authors relax the classic conditional clade distribution - which assumes that given their parent, sister clades are independent - and assume instead that given their parent subsplit, sister subsplits are independent, thus allowing more dependence structure on sister clades. The authors first present a simple maximum likelihood estimation algorithm for rooted trees, and then propose two alternatives to generalize their work to unrooted trees. They finally illustrate their method on both simulated and real-data experiments. I think this paper is very well written, in particular I have greatly appreciated the Background and SBN description sections that make use of a simple though not trivial example to introduce new notions and provide useful insights on the assumptions.

Fields such as phylogenetics often work with a sort of abstracted family tree, called a phylogenetic tree, frequently abbreviated here as tree. These trees have different members of a population as their tips, and their branching points describe the relations between the tips and how recently they had a common ancestor. If some of the tips are censored, the tree topology simplifies in a process we refer to as restriction. If one has multiple trees restricted from the same original, uncensored tree, one may wish to reconstruct the original supertree. Suppose instead one has multiple probability distributions of restricted trees, then one may be interested in reconstructing the supertree probability distribution.

Probability estimation is one of the fundamental tasks in statistics and machine learning. However, standard methods for probability estimation on discrete objects do not handle object structure in a satisfactory manner. In this paper, we derive a general Bayesian network formulation for probability estimation on leaf-labeled trees that enables flexible approximations which can generalize beyond observations. We show that efficient algorithms for learning Bayesian networks can be easily extended to probability estimation on this challenging structured space. Experiments on both synthetic and real data show that our methods greatly outperform the current practice of using the empirical distribution, as well as a previous effort for probability estimation on trees.