Interactive Class-Agnostic Object Counting

We propose a novel framework for interactive class-agnostic object counting, where a human user can interactively provide feedback to improve the accuracy of a counter. Our framework consists of two main components: a user-friendly visualizer to gather feedback and an efficient mechanism to incorporate it. In each iteration, we produce a density map to show the current prediction result, and we segment it into non-overlapping regions with an easily verifiable number of objects. The user can provide feedback by selecting a region with obvious counting errors and specifying the range for the estimated number of objects within it. To improve the counting result, we develop a novel adaptation loss to force the visual counter to output the predicted count within the user-specified range. For effective and efficient adaptation, we propose a refinement module that can be used with any density-based visual counter, and only the parameters in the refinement module will be updated during adaptation. Our experiments on two challenging class-agnostic object counting benchmarks, FSCD-LVIS and FSC-147, show that our method can reduce the mean absolute error of multiple state-of-the-art visual counters by roughly 30% to 40% with minimal user input. Our project can be found at https://yifehuang97.github.io/ICACountProjectPage/

Online tracking of ants based on deep association metrics: method, dataset and evaluation

Tracking movement of insects in a social group (such as ants) is challenging, because the individuals are not only similar in appearance but also likely to perform intensive body contact and sudden movement adjustment (start/stop, direction changes). To address this challenge, we introduce an online multi-object tracking framework that combines both the motion and appearance information of ants. We obtain the appearance descriptors by using the ResNet model for offline training on a small (N=50) sample dataset. For online association, a cosine similarity metric computes the matching degree between historical appearance sequences of the trajectory and the current detection. We validate our method in both indoor (lab setup) and outdoor video sequences. The results show that our model obtains 99.3% ± 0.5% MOTA and 91.9% ± 2.1% MOTP across 24,050 testing samples in five indoor sequences, with real-time tracking performance. In an outdoor sequence, we achieve 99.3% MOTA and 92.9% MOTP across 22,041 testing samples. The datasets and code are made publicly available for future research in relevant domains

The Temporal Organization of Operant Behavior: A Response Bout Analysis

abstract: Many behaviors are organized into bouts – brief periods of responding punctuated by pauses. This dissertation examines the operant bouts of the lever pressing rat. Chapter 1 provides a brief history of operant response bout analyses. Chapters 2, 3, 5, and 6 develop new probabilistic models to identify changes in response bout parameters. The parameters of those models are demonstrated to be uniquely sensitive to different experimental manipulations, such as food deprivation (Chapters 2 and 4), response requirements (Chapters 2, 4, and 5), and reinforcer availability (Chapters 2 and 3). Chapter 6 reveals the response bout parameters that underlie the operant hyperactivity of a common rodent model of attention deficit hyperactivity disorder (ADHD), the spontaneously hypertensive rat (SHR). Chapter 6 then ameliorates the SHR’s operant hyperactivity using training procedures developed from findings in Chapters 2 and 4. Collectively, this dissertation provides new tools for the assessment of response bouts and demonstrates their utility for discerning differences between experimental preparations and animal strains that may be otherwise indistinguishable with more primitive methods.Dissertation/ThesisDoctoral Dissertation Neuroscience 201

Interactive tracking of insect posture

In this paper, we present an association based tracking approach to track multiple insect body parts in a set of low frame-rate videos. The association is formulated as a MAP problem and solved by the Hungarian algorithm. Different from a traditional track-and-then-rectification scheme, this framework refines the tracking hypotheses in an interactive fashion: it integrates a key frame selection approach to minimize the number of frames for user correction while optimizing the final hypotheses. Given user correction, it takes user inputs to rectify the incorrect hypotheses on the other frames. Thus, the framework improves the tracking accuracy by introducing active key frame selection and interactive components, enabling a flexible strategy to achieve a trade-off between human effort and tracking precision. Given the refined tracks at a bounding box (BB) level, the tip of each body part is estimated, and multiple body parts in a BB are further differentiated. The efficiency and the effectiveness of the framework are verified on challenging video datasets for insect behavioral experiments