Multistage Multiscale Inference Network with Visibility Attention for Occluded Person Re-Identification

For occluded person re-identification this thesis presents the Multistage Multiscale Inference Network (MMI-Net) that leverages an inference framework based on multiscale representations with visibility guidance. MMI-Net consists of three sub-networks, i) global, ii) part-based and iii) integrated, to infer person re-identification. The global inference sub-network provides an overall holistic analysis of input images. The part-based sub-network captures more localized information. Both the global and part-based models make use of multiscale representation across multiple processing stages to capture a variety of complementary discriminative image structure. The integrated sub-network aggregates the global and part-based representations to obtain the final fusion of all extracted information. Pose guided attentional processing is used to provide robustness to occlusion. MMI-Net is unique in its integrated multistage inference architecture that accounts for local and global appearance with attentional processing. In empirical evaluation, MMI-Net outperforms current existing methods on multiple occluded person re-identification datasets

Testing a frequency of exposure hypothesis in attentional bias for alcohol-related stimuli amongst social drinkers

Aims To examine whether a group of social drinkers showed longer response latencies to alcohol-related stimuli than neutral stimuli and to test whether exposure to 1) an alcohol-related environment and 2) consumption related cues influenced the interference from alcohol-related stimuli. Methods A 2 × 2 × 2 × 5 factorial design with Exposure Group (high, low) and Consumption Group (high, low) as between-participant factors and Word Type (alcohol, neutral) and Block (1–5) as within-participant factors was used. Forty-three undergraduate university students, 21 assigned to a high exposure group and 22 to a low exposure group, took part in the experiment. Exposure Group was defined according to whether or not participants currently worked in a bar or pub. Consumption Group was defined according to a median split on a quantity–frequency measure derived from two questions of the Alcohol Use Disorders Identification Test (AUDIT) questionnaire. A modified computerised Stroop colour naming test was used to measure response latencies. Results Exposure and consumption factors interacted to produce greater interference from alcohol-related stimuli. In particular, the low consumption group showed interference from alcohol-related stimuli only in the high exposure condition. Exposure did not affect the magnitude of interference in the high consumption group. Conclusions Attentional bias is dependent upon exposure to distinct types of alcohol-related cues

Comparator Networks

The objective of this work is set-based verification, e.g. to decide if two sets of images of a face are of the same person or not. The traditional approach to this problem is to learn to generate a feature vector per image, aggregate them into one vector to represent the set, and then compute the cosine similarity between sets. Instead, we design a neural network architecture that can directly learn set-wise verification. Our contributions are: (i) We propose a Deep Comparator Network (DCN) that can ingest a pair of sets (each may contain a variable number of images) as inputs, and compute a similarity between the pair--this involves attending to multiple discriminative local regions (landmarks), and comparing local descriptors between pairs of faces; (ii) To encourage high-quality representations for each set, internal competition is introduced for recalibration based on the landmark score; (iii) Inspired by image retrieval, a novel hard sample mining regime is proposed to control the sampling process, such that the DCN is complementary to the standard image classification models. Evaluations on the IARPA Janus face recognition benchmarks show that the comparator networks outperform the previous state-of-the-art results by a large margin.Comment: To appear in ECCV 201

The Problem of Mental Action

In mental action there is no motor output to be controlled and no sensory input vector that could be manipulated by bodily movement. It is therefore unclear whether this specific target phenomenon can be accommodated under the predictive processing framework at all, or if the concept of “active inference” can be adapted to this highly relevant explanatory domain. This contribution puts the phenomenon of mental action into explicit focus by introducing a set of novel conceptual instruments and developing a first positive model, concentrating on epistemic mental actions and epistemic self-control. Action initiation is a functionally adequate form of self-deception; mental actions are a specific form of predictive control of effective connectivity, accompanied and possibly even functionally mediated by a conscious “epistemic agent model”. The overall process is aimed at increasing the epistemic value of pre-existing states in the conscious self-model, without causally looping through sensory sheets or using the non-neural body as an instrument for active inference