4,580 research outputs found
Indoor Semantic Segmentation using depth information
This work addresses multi-class segmentation of indoor scenes with RGB-D
inputs. While this area of research has gained much attention recently, most
works still rely on hand-crafted features. In contrast, we apply a multiscale
convolutional network to learn features directly from the images and the depth
information. We obtain state-of-the-art on the NYU-v2 depth dataset with an
accuracy of 64.5%. We illustrate the labeling of indoor scenes in videos
sequences that could be processed in real-time using appropriate hardware such
as an FPGA.Comment: 8 pages, 3 figure
Temporal Relational Reasoning in Videos
Temporal relational reasoning, the ability to link meaningful transformations
of objects or entities over time, is a fundamental property of intelligent
species. In this paper, we introduce an effective and interpretable network
module, the Temporal Relation Network (TRN), designed to learn and reason about
temporal dependencies between video frames at multiple time scales. We evaluate
TRN-equipped networks on activity recognition tasks using three recent video
datasets - Something-Something, Jester, and Charades - which fundamentally
depend on temporal relational reasoning. Our results demonstrate that the
proposed TRN gives convolutional neural networks a remarkable capacity to
discover temporal relations in videos. Through only sparsely sampled video
frames, TRN-equipped networks can accurately predict human-object interactions
in the Something-Something dataset and identify various human gestures on the
Jester dataset with very competitive performance. TRN-equipped networks also
outperform two-stream networks and 3D convolution networks in recognizing daily
activities in the Charades dataset. Further analyses show that the models learn
intuitive and interpretable visual common sense knowledge in videos.Comment: camera-ready version for ECCV'1
A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D images
Semantic segmentation is the pixel-wise labelling of an image. Since the
problem is defined at the pixel level, determining image class labels only is
not acceptable, but localising them at the original image pixel resolution is
necessary. Boosted by the extraordinary ability of convolutional neural
networks (CNN) in creating semantic, high level and hierarchical image
features; excessive numbers of deep learning-based 2D semantic segmentation
approaches have been proposed within the last decade. In this survey, we mainly
focus on the recent scientific developments in semantic segmentation,
specifically on deep learning-based methods using 2D images. We started with an
analysis of the public image sets and leaderboards for 2D semantic
segmantation, with an overview of the techniques employed in performance
evaluation. In examining the evolution of the field, we chronologically
categorised the approaches into three main periods, namely pre-and early deep
learning era, the fully convolutional era, and the post-FCN era. We technically
analysed the solutions put forward in terms of solving the fundamental problems
of the field, such as fine-grained localisation and scale invariance. Before
drawing our conclusions, we present a table of methods from all mentioned eras,
with a brief summary of each approach that explains their contribution to the
field. We conclude the survey by discussing the current challenges of the field
and to what extent they have been solved.Comment: Updated with new studie
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