2,575 research outputs found
OVSNet : Towards One-Pass Real-Time Video Object Segmentation
Video object segmentation aims at accurately segmenting the target object
regions across consecutive frames. It is technically challenging for coping
with complicated factors (e.g., shape deformations, occlusion and out of the
lens). Recent approaches have largely solved them by using backforth
re-identification and bi-directional mask propagation. However, their methods
are extremely slow and only support offline inference, which in principle
cannot be applied in real time. Motivated by this observation, we propose a
efficient detection-based paradigm for video object segmentation. We propose an
unified One-Pass Video Segmentation framework (OVS-Net) for modeling
spatial-temporal representation in a unified pipeline, which seamlessly
integrates object detection, object segmentation, and object re-identification.
The proposed framework lends itself to one-pass inference that effectively and
efficiently performs video object segmentation. Moreover, we propose a
maskguided attention module for modeling the multi-scale object boundary and
multi-level feature fusion. Experiments on the challenging DAVIS 2017
demonstrate the effectiveness of the proposed framework with comparable
performance to the state-of-the-art, and the great efficiency about 11.5 FPS
towards pioneering real-time work to our knowledge, more than 5 times faster
than other state-of-the-art methods.Comment: 10 pages, 6 figure
Going Deeper into First-Person Activity Recognition
We bring together ideas from recent work on feature design for egocentric
action recognition under one framework by exploring the use of deep
convolutional neural networks (CNN). Recent work has shown that features such
as hand appearance, object attributes, local hand motion and camera ego-motion
are important for characterizing first-person actions. To integrate these ideas
under one framework, we propose a twin stream network architecture, where one
stream analyzes appearance information and the other stream analyzes motion
information. Our appearance stream encodes prior knowledge of the egocentric
paradigm by explicitly training the network to segment hands and localize
objects. By visualizing certain neuron activation of our network, we show that
our proposed architecture naturally learns features that capture object
attributes and hand-object configurations. Our extensive experiments on
benchmark egocentric action datasets show that our deep architecture enables
recognition rates that significantly outperform state-of-the-art techniques --
an average increase in accuracy over all datasets. Furthermore, by
learning to recognize objects, actions and activities jointly, the performance
of individual recognition tasks also increase by (actions) and
(objects). We also include the results of extensive ablative analysis to
highlight the importance of network design decisions.
CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos
Temporal action localization is an important yet challenging problem. Given a
long, untrimmed video consisting of multiple action instances and complex
background contents, we need not only to recognize their action categories, but
also to localize the start time and end time of each instance. Many
state-of-the-art systems use segment-level classifiers to select and rank
proposal segments of pre-determined boundaries. However, a desirable model
should move beyond segment-level and make dense predictions at a fine
granularity in time to determine precise temporal boundaries. To this end, we
design a novel Convolutional-De-Convolutional (CDC) network that places CDC
filters on top of 3D ConvNets, which have been shown to be effective for
abstracting action semantics but reduce the temporal length of the input data.
The proposed CDC filter performs the required temporal upsampling and spatial
downsampling operations simultaneously to predict actions at the frame-level
granularity. It is unique in jointly modeling action semantics in space-time
and fine-grained temporal dynamics. We train the CDC network in an end-to-end
manner efficiently. Our model not only achieves superior performance in
detecting actions in every frame, but also significantly boosts the precision
of localizing temporal boundaries. Finally, the CDC network demonstrates a very
high efficiency with the ability to process 500 frames per second on a single
GPU server. We will update the camera-ready version and publish the source
codes online soon.Comment: IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
201
Embodied Visual Perception Models For Human Behavior Understanding
Many modern applications require extracting the core attributes of human behavior such as a person\u27s attention, intent, or skill level from the visual data. There are two main challenges related to this problem. First, we need models that can represent visual data in terms of object-level cues. Second, we need models that can infer the core behavioral attributes from the visual data. We refer to these two challenges as ``learning to see\u27\u27, and ``seeing to learn\u27\u27 respectively. In this PhD thesis, we have made progress towards addressing both challenges.
We tackle the problem of ``learning to see\u27\u27 by developing methods that extract object-level information directly from raw visual data. This includes, two top-down contour detectors, DeepEdge and HfL, which can be used to aid high-level vision tasks such as object detection. Furthermore, we also present two semantic object segmentation methods, Boundary Neural Fields (BNFs), and Convolutional Random Walk Networks (RWNs), which integrate low-level affinity cues into an object segmentation process. We then shift our focus to video-level understanding, and present a Spatiotemporal Sampling Network (STSN), which can be used for video object detection, and discriminative motion feature learning.
Afterwards, we transition into the second subproblem of ``seeing to learn\u27\u27, for which we leverage first-person GoPro cameras that record what people see during a particular activity. We aim to infer the core behavior attributes such as a person\u27s attention, intention, and his skill level from such first-person data. To do so, we first propose a concept of action-objects--the objects that capture person\u27s conscious visual (watching a TV) or tactile (taking a cup) interactions. We then introduce two models, EgoNet and Visual-Spatial Network (VSN), which detect action-objects in supervised and unsupervised settings respectively. Afterwards, we focus on a behavior understanding task in a complex basketball activity. We present a method for evaluating players\u27 skill level from their first-person basketball videos, and also a model that predicts a player\u27s future motion trajectory from a single first-person image
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