22,359 research outputs found
Video Object Detection with an Aligned Spatial-Temporal Memory
We introduce Spatial-Temporal Memory Networks for video object detection. At
its core, a novel Spatial-Temporal Memory module (STMM) serves as the recurrent
computation unit to model long-term temporal appearance and motion dynamics.
The STMM's design enables full integration of pretrained backbone CNN weights,
which we find to be critical for accurate detection. Furthermore, in order to
tackle object motion in videos, we propose a novel MatchTrans module to align
the spatial-temporal memory from frame to frame. Our method produces
state-of-the-art results on the benchmark ImageNet VID dataset, and our
ablative studies clearly demonstrate the contribution of our different design
choices. We release our code and models at
http://fanyix.cs.ucdavis.edu/project/stmn/project.html
Flow-Guided Feature Aggregation for Video Object Detection
Extending state-of-the-art object detectors from image to video is
challenging. The accuracy of detection suffers from degenerated object
appearances in videos, e.g., motion blur, video defocus, rare poses, etc.
Existing work attempts to exploit temporal information on box level, but such
methods are not trained end-to-end. We present flow-guided feature aggregation,
an accurate and end-to-end learning framework for video object detection. It
leverages temporal coherence on feature level instead. It improves the
per-frame features by aggregation of nearby features along the motion paths,
and thus improves the video recognition accuracy. Our method significantly
improves upon strong single-frame baselines in ImageNet VID, especially for
more challenging fast moving objects. Our framework is principled, and on par
with the best engineered systems winning the ImageNet VID challenges 2016,
without additional bells-and-whistles. The proposed method, together with Deep
Feature Flow, powered the winning entry of ImageNet VID challenges 2017. The
code is available at
https://github.com/msracver/Flow-Guided-Feature-Aggregation
Object Detection in Videos with Tubelet Proposal Networks
Object detection in videos has drawn increasing attention recently with the
introduction of the large-scale ImageNet VID dataset. Different from object
detection in static images, temporal information in videos is vital for object
detection. To fully utilize temporal information, state-of-the-art methods are
based on spatiotemporal tubelets, which are essentially sequences of associated
bounding boxes across time. However, the existing methods have major
limitations in generating tubelets in terms of quality and efficiency.
Motion-based methods are able to obtain dense tubelets efficiently, but the
lengths are generally only several frames, which is not optimal for
incorporating long-term temporal information. Appearance-based methods, usually
involving generic object tracking, could generate long tubelets, but are
usually computationally expensive. In this work, we propose a framework for
object detection in videos, which consists of a novel tubelet proposal network
to efficiently generate spatiotemporal proposals, and a Long Short-term Memory
(LSTM) network that incorporates temporal information from tubelet proposals
for achieving high object detection accuracy in videos. Experiments on the
large-scale ImageNet VID dataset demonstrate the effectiveness of the proposed
framework for object detection in videos.Comment: CVPR 201
CED: Color Event Camera Dataset
Event cameras are novel, bio-inspired visual sensors, whose pixels output
asynchronous and independent timestamped spikes at local intensity changes,
called 'events'. Event cameras offer advantages over conventional frame-based
cameras in terms of latency, high dynamic range (HDR) and temporal resolution.
Until recently, event cameras have been limited to outputting events in the
intensity channel, however, recent advances have resulted in the development of
color event cameras, such as the Color-DAVIS346. In this work, we present and
release the first Color Event Camera Dataset (CED), containing 50 minutes of
footage with both color frames and events. CED features a wide variety of
indoor and outdoor scenes, which we hope will help drive forward event-based
vision research. We also present an extension of the event camera simulator
ESIM that enables simulation of color events. Finally, we present an evaluation
of three state-of-the-art image reconstruction methods that can be used to
convert the Color-DAVIS346 into a continuous-time, HDR, color video camera to
visualise the event stream, and for use in downstream vision applications.Comment: Conference on Computer Vision and Pattern Recognition Workshop
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Explainable and Advisable Learning for Self-driving Vehicles
Deep neural perception and control networks are likely to be a key component of self-driving vehicles. These models need to be explainable - they should provide easy-to-interpret rationales for their behavior - so that passengers, insurance companies, law enforcement, developers, etc., can understand what triggered a particular behavior. Explanations may be triggered by the neural controller, namely introspective explanations, or informed by the neural controller's output, namely rationalizations. Our work has focused on the challenge of generating introspective explanations of deep models for self-driving vehicles. In Chapter 3, we begin by exploring the use of visual explanations. These explanations take the form of real-time highlighted regions of an image that causally influence the network's output (steering control). In the first stage, we use a visual attention model to train a convolution network end-to-end from images to steering angle. The attention model highlights image regions that potentially influence the network's output. Some of these are true influences, but some are spurious. We then apply a causal filtering step to determine which input regions actually influence the output. This produces more succinct visual explanations and more accurately exposes the network's behavior. In Chapter 4, we add an attention-based video-to-text model to produce textual explanations of model actions, e.g. "the car slows down because the road is wet". The attention maps of controller and explanation model are aligned so that explanations are grounded in the parts of the scene that mattered to the controller. We explore two approaches to attention alignment, strong- and weak-alignment. These explainable systems represent an externalization of tacit knowledge. The network's opaque reasoning is simplified to a situation-specific dependence on a visible object in the image. This makes them brittle and potentially unsafe in situations that do not match training data. In Chapter 5, we propose to address this issue by augmenting training data with natural language advice from a human. Advice includes guidance about what to do and where to attend. We present the first step toward advice-giving, where we train an end-to-end vehicle controller that accepts advice. The controller adapts the way it attends to the scene (visual attention) and the control (steering and speed). Further, in Chapter 6, we propose a new approach that learns vehicle control with the help of long-term (global) human advice. Specifically, our system learns to summarize its visual observations in natural language, predict an appropriate action response (e.g. "I see a pedestrian crossing, so I stop"), and predict the controls, accordingly
Multichannel Attention Network for Analyzing Visual Behavior in Public Speaking
Public speaking is an important aspect of human communication and
interaction. The majority of computational work on public speaking concentrates
on analyzing the spoken content, and the verbal behavior of the speakers. While
the success of public speaking largely depends on the content of the talk, and
the verbal behavior, non-verbal (visual) cues, such as gestures and physical
appearance also play a significant role. This paper investigates the importance
of visual cues by estimating their contribution towards predicting the
popularity of a public lecture. For this purpose, we constructed a large
database of more than TED talk videos. As a measure of popularity of the
TED talks, we leverage the corresponding (online) viewers' ratings from
YouTube. Visual cues related to facial and physical appearance, facial
expressions, and pose variations are extracted from the video frames using
convolutional neural network (CNN) models. Thereafter, an attention-based long
short-term memory (LSTM) network is proposed to predict the video popularity
from the sequence of visual features. The proposed network achieves
state-of-the-art prediction accuracy indicating that visual cues alone contain
highly predictive information about the popularity of a talk. Furthermore, our
network learns a human-like attention mechanism, which is particularly useful
for interpretability, i.e. how attention varies with time, and across different
visual cues by indicating their relative importance
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