In this paper, we propose fine-grained crowd counting, which differentiates a crowd into categories based on the low-level behavior attributes of the individuals (e.g. standing/sitting or violent behavior) and then counts the number of people in each category. To enable research in this area, we construct a new dataset of four real-world fine-grained counting tasks: traveling direction on a sidewalk, standing or sitting, waiting in line or not, and exhibiting violent behavior or not.

We propose a new multiple-object tracking (MOT) paradigm, tracking-by-counting, tailored for crowded scenes. Using crowd density maps, we jointly model detection, counting, and tracking of multiple targets as a network flow program, which simultaneously finds the global optimal detections and
trajectories of multiple targets over the whole video.

We model the annotation noise using a random variable with Gaussian distribution and derive the pdf of the crowd density value for each spatial location in the image. We then approximate the joint distribution of the density values (i.e., the distribution of density maps) with a full covariance multivariate Gaussian density, and derive a low-rank approximate for tractable implementation.

We propose a generic framework to approximate the output probability distribution induced by a Bayesian NN model posterior with a parameterized model and in an amortized fashion. The aim is to approximate the predictive uncertainty of a specific Bayesian model, meanwhile alleviating the heavy workload of MC integration at testing time.

To improve the distinctiveness of image captions, we first propose a metric, between-set CIDEr (CIDErBtw), to evaluate the distinctiveness of a caption with respect to those of similar images, and then propose several new training strategies for image captioning based on the new distinctiveness measure.