2,472 research outputs found
Discriminatively Trained Latent Ordinal Model for Video Classification
We study the problem of video classification for facial analysis and human
action recognition. We propose a novel weakly supervised learning method that
models the video as a sequence of automatically mined, discriminative
sub-events (eg. onset and offset phase for "smile", running and jumping for
"highjump"). The proposed model is inspired by the recent works on Multiple
Instance Learning and latent SVM/HCRF -- it extends such frameworks to model
the ordinal aspect in the videos, approximately. We obtain consistent
improvements over relevant competitive baselines on four challenging and
publicly available video based facial analysis datasets for prediction of
expression, clinical pain and intent in dyadic conversations and on three
challenging human action datasets. We also validate the method with qualitative
results and show that they largely support the intuitions behind the method.Comment: Paper accepted in IEEE TPAMI. arXiv admin note: substantial text
overlap with arXiv:1604.0150
Shape Representations Using Nested Descriptors
The problem of shape representation is a core problem in computer vision. It can be argued that shape representation is the most central representational problem for computer vision, since unlike texture or color, shape alone can be used for perceptual tasks such as image matching, object detection and object categorization.
This dissertation introduces a new shape representation called the nested descriptor. A nested descriptor represents shape both globally and locally by pooling salient scaled and oriented complex gradients in a large nested support set. We show that this nesting property introduces a nested correlation structure that enables a new local distance function called the nesting distance, which provides a provably robust similarity function for image matching. Furthermore, the nesting property suggests an elegant flower like normalization strategy called a log-spiral difference. We show that this normalization enables a compact binary representation and is equivalent to a form a bottom up saliency. This suggests that the nested descriptor representational power is due to representing salient edges, which makes a fundamental connection between the saliency and local feature descriptor literature. In this dissertation, we introduce three examples of shape representation using nested descriptors: nested shape descriptors for imagery, nested motion descriptors for video and nested pooling for activities. We show evaluation results for these representations that demonstrate state-of-the-art performance for image matching, wide baseline stereo and activity recognition tasks
EGO-TOPO: Environment Affordances from Egocentric Video
First-person video naturally brings the use of a physical environment to the
forefront, since it shows the camera wearer interacting fluidly in a space
based on his intentions. However, current methods largely separate the observed
actions from the persistent space itself. We introduce a model for environment
affordances that is learned directly from egocentric video. The main idea is to
gain a human-centric model of a physical space (such as a kitchen) that
captures (1) the primary spatial zones of interaction and (2) the likely
activities they support. Our approach decomposes a space into a topological map
derived from first-person activity, organizing an ego-video into a series of
visits to the different zones. Further, we show how to link zones across
multiple related environments (e.g., from videos of multiple kitchens) to
obtain a consolidated representation of environment functionality. On
EPIC-Kitchens and EGTEA+, we demonstrate our approach for learning scene
affordances and anticipating future actions in long-form video.Comment: Published in CVPR 2020, project page:
http://vision.cs.utexas.edu/projects/ego-topo
Is an Object-Centric Video Representation Beneficial for Transfer?
The objective of this work is to learn an object-centric video
representation, with the aim of improving transferability to novel tasks, i.e.,
tasks different from the pre-training task of action classification. To this
end, we introduce a new object-centric video recognition model based on a
transformer architecture. The model learns a set of object-centric summary
vectors for the video, and uses these vectors to fuse the visual and
spatio-temporal trajectory 'modalities' of the video clip. We also introduce a
novel trajectory contrast loss to further enhance objectness in these summary
vectors. With experiments on four datasets -- SomethingSomething-V2,
SomethingElse, Action Genome and EpicKitchens -- we show that the
object-centric model outperforms prior video representations (both
object-agnostic and object-aware), when: (1) classifying actions on unseen
objects and unseen environments; (2) low-shot learning of novel classes; (3)
linear probe to other downstream tasks; as well as (4) for standard action
classification.Comment: Accepted to ACCV 202
The THUMOS Challenge on Action Recognition for Videos "in the Wild"
Automatically recognizing and localizing wide ranges of human actions has
crucial importance for video understanding. Towards this goal, the THUMOS
challenge was introduced in 2013 to serve as a benchmark for action
recognition. Until then, video action recognition, including THUMOS challenge,
had focused primarily on the classification of pre-segmented (i.e., trimmed)
videos, which is an artificial task. In THUMOS 2014, we elevated action
recognition to a more practical level by introducing temporally untrimmed
videos. These also include `background videos' which share similar scenes and
backgrounds as action videos, but are devoid of the specific actions. The three
editions of the challenge organized in 2013--2015 have made THUMOS a common
benchmark for action classification and detection and the annual challenge is
widely attended by teams from around the world.
In this paper we describe the THUMOS benchmark in detail and give an overview
of data collection and annotation procedures. We present the evaluation
protocols used to quantify results in the two THUMOS tasks of action
classification and temporal detection. We also present results of submissions
to the THUMOS 2015 challenge and review the participating approaches.
Additionally, we include a comprehensive empirical study evaluating the
differences in action recognition between trimmed and untrimmed videos, and how
well methods trained on trimmed videos generalize to untrimmed videos. We
conclude by proposing several directions and improvements for future THUMOS
challenges.Comment: Preprint submitted to Computer Vision and Image Understandin
Modeling geometric-temporal context with directional pyramid co-occurrence for action recognition
In this paper, we present a new geometric-temporal representation for visual action recognition based on local spatio-temporal features. First, we propose a modified covariance descriptor under the log-Euclidean Riemannian metric to represent the spatio-temporal cuboids detected in the video sequences. Compared with previously proposed covariance descriptors, our descriptor can be measured and clustered in Euclidian space. Second, to capture the geometric-temporal contextual information, we construct a directional pyramid co-occurrence matrix (DPCM) to describe the spatio-temporal distribution of the vector-quantized local feature descriptors extracted from a video. DPCM characterizes the co-occurrence statistics of local features as well as the spatio-temporal positional relationships among the concurrent features. These statistics provide strong descriptive power for action recognition. To use DPCM for action recognition, we propose a directional pyramid co-occurrence matching kernel to measure the similarity of videos. The proposed method achieves the state-of-the-art performance and improves on the recognition performance of the bag-of-visual-words (BOVWs) models by a large margin on six public data sets. For example, on the KTH data set, it achieves 98.78% accuracy while the BOVW approach only achieves 88.06%. On both Weizmann and UCF CIL data sets, the highest possible accuracy of 100% is achieved
- …