Recent progress in image recognition has stimulated the deployment of vision systems (e.g. image search engines) at an unprecedented scale. As a result, visual data are now often consumed not only by humans but also by machines. Meanwhile, existing image processing methods only optimize for better human perception, whereas the resulting images may not be accurately recognized by machines. This can be undesirable, e.g., the images can be improperly handled by search engines or recommendation systems. In this work, we propose simple approaches to improve machine interpretability of processed images: optimizing the recognition loss directly on the image processing network or through an intermediate transforming model, a process which we show can also be done in an unsupervised manner. Interestingly, the processing model's ability to enhance the recognition performance can transfer when evaluated on different recognition models, even if they are of different architectures, trained on different object categories or even different recognition tasks. This makes the solutions applicable even when we do not have the knowledge about future downstream recognition models, e.g., if we are to upload the processed images to the Internet. We conduct comprehensive experiments on three image processing tasks with two downstream recognition tasks, and confirm our method brings substantial accuracy improvement on both the same recognition model and when transferring to a different one, with minimal or no loss in the image processing quality.

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Anytime inference requires a model to make a progression of predictions which might be halted at any time. Prior research on anytime visual recognition has mostly focused on image classification. We propose the first unified and end-to-end model approach for anytime pixel-level recognition. A cascade of "exits" is attached to the model to make multiple predictions and direct further computation. We redesign the exits to account for the depth and spatial resolution of the features for each exit. To reduce total computation, and make full use of prior predictions, we develop a novel spatially adaptive approach to avoid further computation on regions where early predictions are already sufficiently confident. Our full model with redesigned exit architecture and spatial adaptivity enables anytime inference, achieves the same level of final accuracy, and even significantly reduces total computation. We evaluate our approach on semantic segmentation and human pose estimation. On Cityscapes semantic segmentation and MPII human pose estimation, our approach enables anytime inference while also reducing the total FLOPs of its base models by 44.4% and 59.1% without sacrificing accuracy. As a new anytime baseline, we measure the anytime capability of deep equilibrium networks, a recent class of model that is intrinsically iterative, and we show that the accuracycomputation curve of our architecture strictly dominates it.

One of the most popular tools for quantifying protein expression is Immunofluorescence (IF). Although IF is widely applied in drug discovery research and assessing disease mechanisms, it has great room for improvement on the task of analyzing human postmortem brain samples. IF analysis of postmortem human tissue relies mostly on manual interaction, which is often error-prone and leading to low inter and intraobserver reproducibility. The high level of autofluorescence caused by accumulation of lipofuscin pigment during aging impedes systematic analyses of human postmortem brain samples. A method for automating cell counting and classification in IF microscopy of human postmortem brains was proposed before, which speeds up the quantification task while improving reproducibility. To correct for misclassified cells by the algorithm, we created HBFMTool, a software package that ease the process of editing the result produced by cell detection/classification algorithm.

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