How Does Individual Recognition Evolve? Comparing Responses to Identity Information in P olistes Species with and Without Individual Recognition

A wide range of complex social behaviors are facilitated by the recognition of individual conspecifics. Individual recognition requires sufficient phenotypic variation to provide identity information as well as receivers that process and respond to identity information. Understanding how a complex trait such as individual recognition evolves requires that we consider how each component has evolved. Previous comparative studies have examined phenotypic variability in senders and receiver learning abilities, although little work has compared receiver responses to identity information among related species with and without individual recognition. Here, we compare responses to identity information in two Polistes paper wasps: P. fuscatus, which visually recognizes individuals, and P. metricus , which does not normally show evidence of individual recognition. Although the species differ in individual recognition, the results of this study show that receiver responses to experimentally manipulated identity information are surprisingly similar in both species. Receivers direct less aggression toward identifiable individuals than unidentifiable individuals. Therefore, the responses necessary for individual recognition may pre‐date its evolution in the P. fuscatus lineage. Additionally, our data demonstrate the apparent binary differences in a complex behavior between the two species, such as individual recognition, likely involve incremental differences along a number of axes.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/102093/1/eth12191.pd

Memory Based Online Learning of Deep Representations from Video Streams

We present a novel online unsupervised method for face identity learning from video streams. The method exploits deep face descriptors together with a memory based learning mechanism that takes advantage of the temporal coherence of visual data. Specifically, we introduce a discriminative feature matching solution based on Reverse Nearest Neighbour and a feature forgetting strategy that detect redundant features and discard them appropriately while time progresses. It is shown that the proposed learning procedure is asymptotically stable and can be effectively used in relevant applications like multiple face identification and tracking from unconstrained video streams. Experimental results show that the proposed method achieves comparable results in the task of multiple face tracking and better performance in face identification with offline approaches exploiting future information. Code will be publicly available.Comment: arXiv admin note: text overlap with arXiv:1708.0361

Highly Efficient Regression for Scalable Person Re-Identification

Existing person re-identification models are poor for scaling up to large data required in real-world applications due to: (1) Complexity: They employ complex models for optimal performance resulting in high computational cost for training at a large scale; (2) Inadaptability: Once trained, they are unsuitable for incremental update to incorporate any new data available. This work proposes a truly scalable solution to re-id by addressing both problems. Specifically, a Highly Efficient Regression (HER) model is formulated by embedding the Fisher's criterion to a ridge regression model for very fast re-id model learning with scalable memory/storage usage. Importantly, this new HER model supports faster than real-time incremental model updates therefore making real-time active learning feasible in re-id with human-in-the-loop. Extensive experiments show that such a simple and fast model not only outperforms notably the state-of-the-art re-id methods, but also is more scalable to large data with additional benefits to active learning for reducing human labelling effort in re-id deployment

Familiarization through Ambient Images Alone

The term “ambient images” has begun to show up in much of the current literature on facial recognition. Ambient images refer to naturally occurring views of a face that captures the idiosyncratic ways in which a target face may vary (Ritchie & Burton, 2017). Much of the literature on ambient images have concluded that exposing people to ambient images of a target face can lead to improved facial recognition for that target face. Some studies have even suggested that familiarity is the result of increased exposure to ambient images of a target face (Burton, Kramer, Ritchie, & Jenkins, 2016). The current study extended the literature on ambient images. Using the face sorting paradigm from Jenkins, White, Van Montfort, and Burton (2011), the current study served three purposes. First, this study captured whether there was an incremental benefit in showing ambient images. Particularly, we observed whether performance improved as participants were shown a low, medium, or high number of ambient images. Next, this study attempted to provide a strong enough manipulation that participant would be able to perform the face sorting task perfectly, after being exposed to a high number (45 total) of ambient images. Lastly, this study introduced time data as a measure of face familiarity. The results found support for one aim of this study and partial support for another aim of this study. Time data were found to be an effective quantitative measure of familiarity. Also, there was some evidence of an incremental benefit of ambient images, but that benefit disappeared after viewing around 15 unique exemplar presentations of a novel identity’s face. Lastly, exposing participants to 45 ambient images alone did not cause them to reach perfect performance. The paper concludes with a discussion on the need to extend past ambient images to understand how to best mimic natural familiarity in a lab setting

The management of context-sensitive features: A review of strategies

In this paper, we review five heuristic strategies for handling context- sensitive features in supervised machine learning from examples. We discuss two methods for recovering lost (implicit) contextual information. We mention some evidence that hybrid strategies can have a synergetic effect. We then show how the work of several machine learning researchers fits into this framework. While we do not claim that these strategies exhaust the possibilities, it appears that the framework includes all of the techniques that can be found in the published literature on context-sensitive learning

Reinstated episodic context guides sampling-based decisions for reward.

How does experience inform decisions? In episodic sampling, decisions are guided by a few episodic memories of past choices. This process can yield choice patterns similar to model-free reinforcement learning; however, samples can vary from trial to trial, causing decisions to vary. Here we show that context retrieved during episodic sampling can cause choice behavior to deviate sharply from the predictions of reinforcement learning. Specifically, we show that, when a given memory is sampled, choices (in the present) are influenced by the properties of other decisions made in the same context as the sampled event. This effect is mediated by fMRI measures of context retrieval on each trial, suggesting a mechanism whereby cues trigger retrieval of context, which then triggers retrieval of other decisions from that context. This result establishes a new avenue by which experience can guide choice and, as such, has broad implications for the study of decisions