Acoustic scene classification (ASC) aims to classify an audio clip based on
the characteristic of the recording environment. In this regard, deep learning
based approaches have emerged as a useful tool for ASC problems. Conventional
approaches to improving the classification accuracy include integrating
auxiliary methods such as attention mechanism, pre-trained models and ensemble
multiple sub-networks. However, due to the complexity of audio clips captured
from different environments, it is difficult to distinguish their categories
without using any auxiliary methods for existing deep learning models using
only a single classifier. In this paper, we propose a novel approach for ASC
using deep neural decision forest (DNDF). DNDF combines a fixed number of
convolutional layers and a decision forest as the final classifier. The
decision forest consists of a fixed number of decision tree classifiers, which
have been shown to offer better classification performance than a single
classifier in some datasets. In particular, the decision forest differs
substantially from traditional random forests as it is stochastic,
differentiable, and capable of using the back-propagation to update and learn
feature representations in neural network. Experimental results on the
DCASE2019 and ESC-50 datasets demonstrate that our proposed DNDF method
improves the ASC performance in terms of classification accuracy and shows
competitive performance as compared with state-of-the-art baselines.

本文提出了一种基于深度神经决策树的声场分类方法（DNDF），它将卷积层与决策森林作为最终分类器的固定数量结合起来，相较于传统的单一分类器，此模型在 DCASE2019 和 ESC-50 数据集上展现出更好的声场分类性能，并且具有竞争性能。

基于深度神经决策森林的声场分类

Deep Neural Decision Forest for Acoustic Scene Classification

We address Unsupervised Video Object Segmentation (UVOS), the task of
automatically generating accurate pixel masks for salient objects in a video
sequence and of tracking these objects consistently through time, without any
input about which objects should be tracked. Towards solving this task, we
present UnOVOST (Unsupervised Offline Video Object Segmentation and Tracking)
as a simple and generic algorithm which is able to track and segment a large
variety of objects. This algorithm builds up tracks in a number stages, first
grouping segments into short tracklets that are spatio-temporally consistent,
before merging these tracklets into long-term consistent object tracks based on
their visual similarity. In order to achieve this we introduce a novel
tracklet-based Forest Path Cutting data association algorithm which builds up a
decision forest of track hypotheses before cutting this forest into paths that
form long-term consistent object tracks. When evaluating our approach on the
DAVIS 2017 Unsupervised dataset we obtain state-of-the-art performance with a
mean J &F score of 67.9% on the val, 58% on the test-dev and 56.4% on the
test-challenge benchmarks, obtaining first place in the DAVIS 2019 Unsupervised
Video Object Segmentation Challenge. UnOVOST even performs competitively with
many semi-supervised video object segmentation algorithms even though it is not
given any input as to which objects should be tracked and segmented.

本研究提出了一种基于轨迹分组和决策森林的算法，名为 UnOVOST，用于解决无监督的视频对象分割和跟踪问题，并在 DAVIS 2017 Unsupervised 数据集上达到了最先进性能。