301 research outputs found
Linking factual and procedural knowledge in solving science problems: A case study in a thermodynamics course
Well-specified problems of the type presented boxed in the introduction to this article are extremely common in science courses. Unfortunately, this does not mean that students find them easy to solve, even when a teacher provides model answers to problems which differ only marginally (in the teacher's eyes) from those put before the students. The central difficulty with such courses is that they do not embody instructional principles that reflect students' need for “direction” in problem solving. In this article, we describe how the necessary heuristics and strategic knowledge were built into the remake of a conventional thermodynamics course. In contrast to mainstream American work on learning problem solving we chose to direct our curriculum reconstruction using the Gal'perin theory of stage-by-stage formation of mental actions and Landa's description of the “through” systematization of knowledge. As indicated by both, we first developed an integrated system of instructional objectives: a programme of actions and methods (PAM) to solve problems in thermodynamics. Then the plan of instruction was designed. This plan indicates which instructional procedures and materials should be used to realize the instructional functions, derived from the learning theory. The evaluation design contained two control and three experimental courses. In discussing our main findings, we consider the generalizability of the procedures we followed in constructing the PAM and the instructional plan
Spatial-Aware Object Embeddings for Zero-Shot Localization and Classification of Actions
We aim for zero-shot localization and classification of human actions in
video. Where traditional approaches rely on global attribute or object
classification scores for their zero-shot knowledge transfer, our main
contribution is a spatial-aware object embedding. To arrive at spatial
awareness, we build our embedding on top of freely available actor and object
detectors. Relevance of objects is determined in a word embedding space and
further enforced with estimated spatial preferences. Besides local object
awareness, we also embed global object awareness into our embedding to maximize
actor and object interaction. Finally, we exploit the object positions and
sizes in the spatial-aware embedding to demonstrate a new spatio-temporal
action retrieval scenario with composite queries. Action localization and
classification experiments on four contemporary action video datasets support
our proposal. Apart from state-of-the-art results in the zero-shot localization
and classification settings, our spatial-aware embedding is even competitive
with recent supervised action localization alternatives.Comment: ICC
Action Search: Spotting Actions in Videos and Its Application to Temporal Action Localization
State-of-the-art temporal action detectors inefficiently search the entire
video for specific actions. Despite the encouraging progress these methods
achieve, it is crucial to design automated approaches that only explore parts
of the video which are the most relevant to the actions being searched for. To
address this need, we propose the new problem of action spotting in video,
which we define as finding a specific action in a video while observing a small
portion of that video. Inspired by the observation that humans are extremely
efficient and accurate in spotting and finding action instances in video, we
propose Action Search, a novel Recurrent Neural Network approach that mimics
the way humans spot actions. Moreover, to address the absence of data recording
the behavior of human annotators, we put forward the Human Searches dataset,
which compiles the search sequences employed by human annotators spotting
actions in the AVA and THUMOS14 datasets. We consider temporal action
localization as an application of the action spotting problem. Experiments on
the THUMOS14 dataset reveal that our model is not only able to explore the
video efficiently (observing on average 17.3% of the video) but it also
accurately finds human activities with 30.8% mAP.Comment: Accepted to ECCV 201
Universal Prototype Transport for Zero-Shot Action Recognition and Localization
This work addresses the problem of recognizing action categories in videos when no training examples are available. The current state-of-the-art enables such a zero-shot recognition by learning universal mappings from videos to a semantic space, either trained on large-scale seen actions or on objects. While effective, we find that universal action and object mappings are biased to specific regions in the semantic space. These biases lead to a fundamental problem: many unseen action categories are simply never inferred during testing. For example on UCF-101, a quarter of the unseen actions are out of reach with a state-of-the-art universal action model. To that end, this paper introduces universal prototype transport for zero-shot action recognition. The main idea is to re-position the semantic prototypes of unseen actions by matching them to the distribution of all test videos. For universal action models, we propose to match distributions through a hyperspherical optimal transport from unseen action prototypes to the set of all projected test videos. The resulting transport couplings in turn determine the target prototype for each unseen action. Rather than directly using the target prototype as final result, we re-position unseen action prototypes along the geodesic spanned by the original and target prototypes as a form of semantic regularization. For universal object models, we outline a variant that defines target prototypes based on an optimal transport between unseen action prototypes and object prototypes. Empirically, we show that universal prototype transport diminishes the biased selection of unseen action prototypes and boosts both universal action and object models for zero-shot classification and spatio-temporal localization
Counting with Focus for Free
This paper aims to count arbitrary objects in images. The leading counting
approaches start from point annotations per object from which they construct
density maps. Then, their training objective transforms input images to density
maps through deep convolutional networks. We posit that the point annotations
serve more supervision purposes than just constructing density maps. We
introduce ways to repurpose the points for free. First, we propose supervised
focus from segmentation, where points are converted into binary maps. The
binary maps are combined with a network branch and accompanying loss function
to focus on areas of interest. Second, we propose supervised focus from global
density, where the ratio of point annotations to image pixels is used in
another branch to regularize the overall density estimation. To assist both the
density estimation and the focus from segmentation, we also introduce an
improved kernel size estimator for the point annotations. Experiments on six
datasets show that all our contributions reduce the counting error, regardless
of the base network, resulting in state-of-the-art accuracy using only a single
network. Finally, we are the first to count on WIDER FACE, allowing us to show
the benefits of our approach in handling varying object scales and crowding
levels. Code is available at
https://github.com/shizenglin/Counting-with-Focus-for-FreeComment: ICCV, 201
Localizing Actions from Video Labels and Pseudo-Annotations
The goal of this paper is to determine the spatio-temporal location of
actions in video. Where training from hard to obtain box annotations is the
norm, we propose an intuitive and effective algorithm that localizes actions
from their class label only. We are inspired by recent work showing that
unsupervised action proposals selected with human point-supervision perform as
well as using expensive box annotations. Rather than asking users to provide
point supervision, we propose fully automatic visual cues that replace manual
point annotations. We call the cues pseudo-annotations, introduce five of them,
and propose a correlation metric for automatically selecting and combining
them. Thorough evaluation on challenging action localization datasets shows
that we reach results comparable to results with full box supervision. We also
show that pseudo-annotations can be leveraged during testing to improve weakly-
and strongly-supervised localizers.Comment: BMV
- …