Recognition in Terra Incognita

It is desirable for detection and classification algorithms to generalize to unfamiliar environments, but suitable benchmarks for quantitatively studying this phenomenon are not yet available. We present a dataset designed to measure recognition generalization to novel environments. The images in our dataset are harvested from twenty camera traps deployed to monitor animal populations. Camera traps are fixed at one location, hence the background changes little across images; capture is triggered automatically, hence there is no human bias. The challenge is learning recognition in a handful of locations, and generalizing animal detection and classification to new locations where no training data is available. In our experiments state-of-the-art algorithms show excellent performance when tested at the same location where they were trained. However, we find that generalization to new locations is poor, especially for classification systems. (The dataset is available at https://beerys.github.io/CaltechCameraTraps/

Recognition in Terra Incognita

It is desirable for detection and classification algorithms to generalize to unfamiliar environments, but suitable benchmarks for quantitatively studying this phenomenon are not yet available. We present a dataset designed to measure recognition generalization to novel environments. The images in our dataset are harvested from twenty camera traps deployed to monitor animal populations. Camera traps are fixed at one location, hence the background changes little across images; capture is triggered automatically, hence there is no human bias. The challenge is learning recognition in a handful of locations, and generalizing animal detection and classification to new locations where no training data is available. In our experiments state-of-the-art algorithms show excellent performance when tested at the same location where they were trained. However, we find that generalization to new locations is poor, especially for classification systems. (The dataset is available at https://beerys.github.io/CaltechCameraTraps/

Recognition in Terra Incognita

It is desirable for detection and classification algorithms to generalize to unfamiliar environments, but suitable benchmarks for quantitatively studying this phenomenon are not yet available. We present a dataset designed to measure recognition generalization to novel environments. The images in our dataset are harvested from twenty camera traps deployed to monitor animal populations. Camera traps are fixed at one location, hence the background changes little across images; capture is triggered automatically, hence there is no human bias. The challenge is learning recognition in a handful of locations, and generalizing animal detection and classification to new locations where no training data is available. In our experiments state-of-the-art algorithms show excellent performance when tested at the same location where they were trained. However, we find that generalization to new locations is poor, especially for classification systems. (The dataset is available at https://beerys.github.io/CaltechCameraTraps/

Into the Unknown: Navigating Spaces, Terra Incognita and the Art Archive

This paper is a navigation across time and space – travelling from 16th century colonial world maps which marked unknown territories as Terra Incognita, via 18th century cabinets of curiosities; to the unknown spaces of the Anthropocene Age, in which for the first time we humans are making a permanent geological record on the earth’s ecosystems. This includes climate change. The recurring theme is loss and becoming lost. I investigate what happens when someone who is lost attempts to navigate and find parallels between Terra Incognita and the art archive, and explore the points where mapping, archiving and collecting intersect. Once something is perceived to be at risk, the fear of loss and the impulse to preserve emerges. I investigate why in the Anthropocene Age we have a stronger impulse to the archive and look to the past, rather than face the unknowable effects of climate change. This is counterpointed by artists, whose hybrids practices engage with re-imaging and re-imagining today’s world, thereby moving us forward into the unknown. ‘Becoming’ is therefore another central theme. The art archive is explored from multiple perspectives – as an artist, an art archive user and an archivist – noting that the subject, the consumer and the archivist all have very differing agendas. I question who uses physical archives today and how we can retain our sense of curiosity. I conclude with a link to an interactive artwork, which visualises, synthesises and expands this research

DISPEL: Domain Generalization via Domain-Specific Liberating

Domain generalization aims to learn a generalization model that can perform well on unseen test domains by only training on limited source domains. However, existing domain generalization approaches often bring in prediction-irrelevant noise or require the collection of domain labels. To address these challenges, we consider the domain generalization problem from a different perspective by categorizing underlying feature groups into domain-shared and domain-specific features. Nevertheless, the domain-specific features are difficult to be identified and distinguished from the input data. In this work, we propose DomaIn-SPEcific Liberating (DISPEL), a post-processing fine-grained masking approach that can filter out undefined and indistinguishable domain-specific features in the embedding space. Specifically, DISPEL utilizes a mask generator that produces a unique mask for each input data to filter domain-specific features. The DISPEL framework is highly flexible to be applied to any fine-tuned models. We derive a generalization error bound to guarantee the generalization performance by optimizing a designed objective loss. The experimental results on five benchmarks demonstrate DISPEL outperforms existing methods and can further generalize various algorithms

Are Data-driven Explanations Robust against Out-of-distribution Data?

As black-box models increasingly power high-stakes applications, a variety of data-driven explanation methods have been introduced. Meanwhile, machine learning models are constantly challenged by distributional shifts. A question naturally arises: Are data-driven explanations robust against out-of-distribution data? Our empirical results show that even though predict correctly, the model might still yield unreliable explanations under distributional shifts. How to develop robust explanations against out-of-distribution data? To address this problem, we propose an end-to-end model-agnostic learning framework Distributionally Robust Explanations (DRE). The key idea is, inspired by self-supervised learning, to fully utilizes the inter-distribution information to provide supervisory signals for the learning of explanations without human annotation. Can robust explanations benefit the model's generalization capability? We conduct extensive experiments on a wide range of tasks and data types, including classification and regression on image and scientific tabular data. Our results demonstrate that the proposed method significantly improves the model's performance in terms of explanation and prediction robustness against distributional shifts.Comment: In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 202

PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization

In a joint vision-language space, a text feature (e.g., from "a photo of a dog") could effectively represent its relevant image features (e.g., from dog photos). Inspired by this, we propose PromptStyler which simulates various distribution shifts in the joint space by synthesizing diverse styles via prompts without using any images to deal with source-free domain generalization. Our method learns to generate a variety of style features (from "a S* style of a") via learnable style word vectors for pseudo-words S*. To ensure that learned styles do not distort content information, we force style-content features (from "a S* style of a [class]") to be located nearby their corresponding content features (from "[class]") in the joint vision-language space. After learning style word vectors, we train a linear classifier using synthesized style-content features. PromptStyler achieves the state of the art on PACS, VLCS, OfficeHome and DomainNet, although it does not require any images and takes just ~30 minutes for training using a single GPU.Comment: Accepted to ICCV 2023, Project Page: https://promptstyler.github.io

THE EUROPEAN PROJECT "VERSUS+ / HERITAGE FOR PEOPLE". OBJECTIVES AND METHODOLOGY

Abstract. The project "VerSus+ / Heritage for PEOPLE", founded by the European Commission as part of the Creative Europe Culture Programme (Ref. 607593-CREA-1-2019-1-ES-CULT-COOP1) during the period 2019–2023, focuses on the transmission of knowledge to all branches of society and the general public. Its aim is to raise awareness on what constitutes the basis for the conservation of the tangible and intangible heritage as well as for a more sustainable contemporary architecture. This in-depth transmission of the lessons from vernacular heritage to future society is to be carried out in specific defined contexts, such as islands and archipelagos (geographically limited territories that are accessible to collaborators and administrative, technical and social agents), where vernacular heritage is under pressure, subjected to the transformations of contemporary life, particularly mass tourism. These pilot experiences should serve as a real testing ground for the implementation of actions for social participation, dissemination, education, communication, and promotion in different contexts and through different media. This project aims to reach out to society in order to showcase the sustainable qualities of the examples identified, through the establishment of an operative approach that can be adjusted to different contexts. The experiences on each island are expected to have repercussions throughout the region and, in turn, throughout the country in question, improving the perspectives and opportunities starting from best practices, and promoting the development of local skills. In addition, promotion and support from partners and associate partners will allow these experiences to be applied in other similar European and international contexts

Grafting culture:On the development and diffusion of the strathspey in Scottish music

The strathspey is typically understood to be an eighteenth-century variety of fiddle music instigated by two well-known musical families native to the Spey valley region

The importance of respect as a discursive resource in making identity claims: insights from the experiences of becoming a circus director

Though often invoked in the leadership and identity literatures, respect has been poorly articulated. This paper conceptualizes respect as a discursive resource for making identity claims and provides empirical illustration from circus directors’ accounts of becoming managers. Identity claims draw on particular discursive resources and enact recurrent social practices in “specific local historical circumstances” that cohere with “the local moral order”. To claim and to offer respect based on recognition, appraisal, identification, status and other discourses is to participate in such an order, and to make identity claims which are understood as positioning self and others. We provide “transparently observable” illustrations of respect as a discursive resource for forming, maintaining, strengthening, repairing or revising identity claims. An extreme case purposive sample of circus directors provides an organizational site in which identity dynamics are “highly visible”. Within the local moral order of travelling circuses respect is both desired from and conferred upon those whose artistic merit is recognized in both single acts and whole shows. We show that the distinction between appraisal and status as respect discourses evident in the wide social order breaks down in the case of circus. We theorize from this to the importance of respect as a discursive resource in identity claims and to its dependence upon particular accounts of merit