8 research outputs found
Unified Embedding and Metric Learning for Zero-Exemplar Event Detection
Event detection in unconstrained videos is conceived as a content-based video
retrieval with two modalities: textual and visual. Given a text describing a
novel event, the goal is to rank related videos accordingly. This task is
zero-exemplar, no video examples are given to the novel event.
Related works train a bank of concept detectors on external data sources.
These detectors predict confidence scores for test videos, which are ranked and
retrieved accordingly. In contrast, we learn a joint space in which the visual
and textual representations are embedded. The space casts a novel event as a
probability of pre-defined events. Also, it learns to measure the distance
between an event and its related videos.
Our model is trained end-to-end on publicly available EventNet. When applied
to TRECVID Multimedia Event Detection dataset, it outperforms the
state-of-the-art by a considerable margin.Comment: IEEE CVPR 201
VideoGraph: Recognizing Minutes-Long Human Activities in Videos
Many human activities take minutes to unfold. To represent them, related
works opt for statistical pooling, which neglects the temporal structure.
Others opt for convolutional methods, as CNN and Non-Local. While successful in
learning temporal concepts, they are short of modeling minutes-long temporal
dependencies. We propose VideoGraph, a method to achieve the best of two
worlds: represent minutes-long human activities and learn their underlying
temporal structure. VideoGraph learns a graph-based representation for human
activities. The graph, its nodes and edges are learned entirely from video
datasets, making VideoGraph applicable to problems without node-level
annotation. The result is improvements over related works on benchmarks:
Epic-Kitchen and Breakfast. Besides, we demonstrate that VideoGraph is able to
learn the temporal structure of human activities in minutes-long videos
Coarse Temporal Attention Network (CTA-Net) for Driver’s Activity Recognition
There is significant progress in recognizing traditional human activities
from videos focusing on highly distinctive actions involving discriminative
body movements, body-object and/or human-human interactions. Driver's
activities are different since they are executed by the same subject with
similar body parts movements, resulting in subtle changes. To address this, we
propose a novel framework by exploiting the spatiotemporal attention to model
the subtle changes. Our model is named Coarse Temporal Attention Network
(CTA-Net), in which coarse temporal branches are introduced in a trainable
glimpse network. The goal is to allow the glimpse to capture high-level
temporal relationships, such as 'during', 'before' and 'after' by focusing on a
specific part of a video. These branches also respect the topology of the
temporal dynamics in the video, ensuring that different branches learn
meaningful spatial and temporal changes. The model then uses an innovative
attention mechanism to generate high-level action specific contextual
information for activity recognition by exploring the hidden states of an LSTM.
The attention mechanism helps in learning to decide the importance of each
hidden state for the recognition task by weighing them when constructing the
representation of the video. Our approach is evaluated on four publicly
accessible datasets and significantly outperforms the state-of-the-art by a
considerable margin with only RGB video as input.Comment: Extended version of the accepted WACV 202
Timeception for Complex Action Recognition
This paper focuses on the temporal aspect for recognizing human activities in
videos; an important visual cue that has long been undervalued. We revisit the
conventional definition of activity and restrict it to Complex Action: a set of
one-actions with a weak temporal pattern that serves a specific purpose.
Related works use spatiotemporal 3D convolutions with fixed kernel size, too
rigid to capture the varieties in temporal extents of complex actions, and too
short for long-range temporal modeling. In contrast, we use multi-scale
temporal convolutions, and we reduce the complexity of 3D convolutions. The
outcome is Timeception convolution layers, which reasons about minute-long
temporal patterns, a factor of 8 longer than best related works. As a result,
Timeception achieves impressive accuracy in recognizing the human activities of
Charades, Breakfast Actions, and MultiTHUMOS. Further, we demonstrate that
Timeception learns long-range temporal dependencies and tolerate temporal
extents of complex actions.Comment: IEEE CVPR 2019 (Oral