Design and Method in Architectural Research: From Objective Quantification to Material Speculation

This issue of SPOOL introduces a new thread: ‘Method and Design’, titled “Design and Method in Architectural Research: From Objective Quantification to Material Speculation”. The issue explores the conventional understanding of method through both theoretical contributions and visual essays. The theoretical contributions discuss methodology, material practice, studio approaches, or design principles. The visual essays are more experimental, allowing for design proposals or artistic expressions that explore specific methods, depict scenarios, or articulate a material logic

A call for cautious interpretation of meta-analytic reviews

Meta-analytic reviews collect available empirical studies on a specified domain and calculate the average effect of a factor. Educators as well as researchers exploring a new domain of inquiry may rely on the conclusions from meta-analytic reviews rather than reading multiple primary studies. This article calls for caution in this regard, because the outcome of a meta-analysis is determined by how effect sizes are calculated, how factors are defined, and how studies are selected for inclusion. Three recently published meta-analyses are re-examined to illustrate these issues. One illustrates the risk of conflating effect sizes from studies with different design features, another illustrates problems with delineating the variable of interest, with implications for cause-effect relations, and the third illustrates the challenge of determining the eligibility of candidate studies. Replication attempts yield outcomes that differ from the three original meta-analyses, suggesting that also conclusions drawn from meta-analyses need to be interpreted cautiously

Learning Action Changes by Measuring Verb-Adverb Textual Relationships

The goal of this work is to understand the way actions are performed in videos. That is, given a video, we aim to predict an adverb indicating a modification applied to the action (e.g. cut "finely"). We cast this problem as a regression task. We measure textual relationships between verbs and adverbs to generate a regression target representing the action change we aim to learn. We test our approach on a range of datasets and achieve state-of-the-art results on both adverb prediction and antonym classification. Furthermore, we outperform previous work when we lift two commonly assumed conditions: the availability of action labels during testing and the pairing of adverbs as antonyms. Existing datasets for adverb recognition are either noisy, which makes learning difficult, or contain actions whose appearance is not influenced by adverbs, which makes evaluation less reliable. To address this, we collect a new high quality dataset: Adverbs in Recipes (AIR). We focus on instructional recipes videos, curating a set of actions that exhibit meaningful visual changes when performed differently. Videos in AIR are more tightly trimmed and were manually reviewed by multiple annotators to ensure high labelling quality. Results show that models learn better from AIR given its cleaner videos. At the same time, adverb prediction on AIR is challenging, demonstrating that there is considerable room for improvement.Comment: CVPR 23. Code and dataset available at https://github.com/dmoltisanti/air-cvpr2

Reflecting on How Social Impacts are Considered in Transport Infrastructure Project Planning:Looking beyond the Claimed Success of Sydney’s South West Rail Link

Urban rail transport megaprojects are promoted as generating positive social change at a metropolitan scale, yet they produce complex unplanned negative impacts at local scales. Environmental and Social Impact Assessment (ESIA) and its follow-up help decision-makers assess and manage the social and environmental impacts of major projects. Using Western Sydney’s politically-successful South West Rail Link as an example, we identified the practice challenges and governance barriers to applying ESIA and EIA follow-up across spatial scales. These challenges and barriers influence the planning and management of the impacts of integrated urban development and transport infrastructure development

A Closer Look at Temporal Ordering in the Segmentation of Instructional Videos

Understanding the steps required to perform a task is an important skill for AI systems. Learning these steps from instructional videos involves two subproblems: (i) identifying the temporal boundary of sequentially occurring segments and (ii) summarizing these steps in natural language. We refer to this task as Procedure Segmentation and Summarization (PSS). In this paper, we take a closer look at PSS and propose three fundamental improvements over current methods. The segmentation task is critical, as generating a correct summary requires each step of the procedure to be correctly identified. However, current segmentation metrics often overestimate the segmentation quality because they do not consider the temporal order of segments. In our first contribution, we propose a new segmentation metric that takes into account the order of segments, giving a more reliable measure of the accuracy of a given predicted segmentation. Current PSS methods are typically trained by proposing segments, matching them with the ground truth and computing a loss. However, much like segmentation metrics, existing matching algorithms do not consider the temporal order of the mapping between candidate segments and the ground truth. In our second contribution, we propose a matching algorithm that constrains the temporal order of segment mapping, and is also differentiable. Lastly, we introduce multi-modal feature training for PSS, which further improves segmentation. We evaluate our approach on two instructional video datasets (YouCook2 and Tasty) and observe an improvement over the state-of-the-art of $\sim7\%$ and $\sim2.5\%$ for procedure segmentation and summarization, respectively.Comment: Accepted at BMVC 202

Learn2Augment: Learning to Composite Videos for Data Augmentation in Action Recognition

We address the problem of data augmentation for video action recognition. Standard augmentation strategies in video are hand-designed and sample the space of possible augmented data points either at random, without knowing which augmented points will be better, or through heuristics. We propose to learn what makes a good video for action recognition and select only high-quality samples for augmentation. In particular, we choose video compositing of a foreground and a background video as the data augmentation process, which results in diverse and realistic new samples. We learn which pairs of videos to augment without having to actually composite them. This reduces the space of possible augmentations, which has two advantages: it saves computational cost and increases the accuracy of the final trained classifier, as the augmented pairs are of higher quality than average. We present experimental results on the entire spectrum of training settings: few-shot, semi-supervised and fully supervised. We observe consistent improvements across all of them over prior work and baselines on Kinetics, UCF101, HMDB51, and achieve a new state-of-the-art on settings with limited data. We see improvements of up to 8.6% in the semi-supervised setting.Comment: Accepted to ECCV-202

Metro infrastructure planning in Amsterdam:how are social issues managed in the absence of environmental and social impact assessment?

Amsterdam's North-South Metro Line (NZL) megaproject has had a long eventful history. From the initial proposal in the 1990s, through construction in the 2000s to 2010s, to its opening in 2018, the NZL overcame many challenges. Several geotechnical incidents in the Vijzelgracht neighbourhood in 2008 cost the City of Amsterdam and the Dutch government millions of Euros. These incidents required complex recovery management actions, and there was a complete re-evaluation of the project, resulting in extensive reformulation of the project's communications and impact management strategies, and in more-transparent public participation. Despite NZL's significance, it never underwent any formal Environmental and Social Impact Assessment (ESIA), thus it provides an interesting case to consider how social impacts are addressed when there is no formal ESIA. Drawing on document review, semi-structured interviews, and a focus group, we considered the experiences of key decision-makers and project team members to learn how social impacts were assessed and managed over time in the absence of ESIA. We conclude that, when combined with appropriate urban governance frameworks, applying ESIA in urban and transport planning would improve the assessment and management of the social impacts of future megaproject infrastructure developments

CLASTER: Clustering with Reinforcement Learning for Zero-Shot Action Recognition

Zero-shot action recognition is the task of recognizing action classes without visual examples, only with a semantic embedding which relates unseen to seen classes. The problem can be seen as learning a function which generalizes well to instances of unseen classes without losing discrimination between classes. Neural networks can model the complex boundaries between visual classes, which explains their success as supervised models. However, in zero-shot learning, these highly specialized class boundaries may not transfer well from seen to unseen classes. In this paper, we propose a clustering-based model, which considers all training samples at once, instead of optimizing for each instance individually. We optimize the clustering using Reinforcement Learning which we show is critical for our approach to work. We call the proposed method CLASTER and observe that it consistently improves over the state-of-the-art in all standard datasets, UCF101, HMDB51, and Olympic Sports; both in the standard zero-shot evaluation and the generalized zero-shot learning