We tackle one-shot visual search by example for arbitrary object categories: Given an example image of a novel reference object, find and segment all object instances of the same category within a scene. To address this problem, we propose Siamese Mask R-CNN. It extends Mask R-CNN by a Siamese backbone encoding both reference image and scene, allowing it to target detection and segmentation towards the reference category. We use Siamese Mask R-CNN to perform one-shot instance segmentation on MS-COCO, demonstrating that it can detect and segment objects of novel categories it was not trained on, and without using mask annotations at test time. Our results highlight challenges of the one-shot setting: while transferring knowledge about instance segmentation to novel object categories not used during training works very well, targeting the detection and segmentation networks towards the reference category appears to be more difficult. Our work provides a first strong baseline for one-shot instance segmentation and will hopefully inspire further research in this relatively unexplored field.
Visual search has a long history in perceptual psychology (reviewed, , by @cite_37 ), although typically with simple visual patterns, while search for arbitrary objects in real scenes has been addressed only recently @cite_45 @cite_29 , and often using a natural language cue @cite_29 .
@cite_29 @cite_37 @cite_45
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A number of psychophysical studies concerning the detection, localization and recognition of objects in the visual field have suggested a two-stage theory of human visual perception. The first stage is the “preattentive” mode, in which simple features are processed rapidly and in parallel over the entire visual field. In the second, “attentive” mode, a specialized processing focus, usually called the focus of attention, is directed to particular locations in the visual field. The analysis of complex forms and the recognition of objects are associated with this second stage.1 The computational justification for such a hypothesis comes from the realization that while it is possible to imagine specific algorithms performing tasks such as shape analysis and recognition at specific locations, it is difficult to imagine these algorithms operating in parallel over the whole visual scene, since such an approach will quickly lead to a combinatorial explosion in terms of required computational resources.2 This is essentially the major critique of Minsky and Papert to a universal application of perceptrons in visual perception.3 Taken together, these empirical and theoretical studies suggest that beyond a certain preprocessing stage, the analysis of visual information proceeds in a sequence of operations, each one applied to a selected location (or locations). An important component of routine visual behavior is the ability to find one item in a visual world filled with other, distracting items. This ability to performvisual search has been the subject of a large body of research in the past 15 years. This paper reviews the visual search literature and presents a model of human search behavior. Built upon the work of Neisser, Treisman, Julesz, and others, the model distinguishes between a preattentive, massively parallel stage that processes information about basic visual features (color, motion, various depth cues, etc.) across large portions of the visual field and a subsequent limited-capacity stage that performs other, more complex operations (e.g., face recognition, reading, object identification) over a limited portion of the visual field. The spatial deployment of the limited-capacity process is under attentional control. The heart of the guided search model is the idea that attentional deployment of limited resources isguided by the output of the earlier parallel processes. Guided Search 2.0 (GS2) is a revision of the model in which virtually all aspects of the model have been made more explicit and or revised in light of new data. The paper is organized into four parts: Part 1 presents the model and the details of its computer simulation. Part 2 reviews the visual search literature on preattentive processing of basic features and shows how the GS2 simulation reproduces those results. Part 3 reviews the literature on the attentional deployment of limited-capacity processes in conjunction and serial searches and shows how the simulation handles those conditions. Finally, Part 4 deals with shortcomings of the model and unresolved issues. By providing substantial amounts of data and standardized evaluation protocols, datasets in computer vision have helped fuel advances across all areas of visual recognition. But even in light of breakthrough results on recent benchmarks, it is still fair to ask if our recognition algorithms are doing as well as we think they are. The vision sciences at large make use of a very different evaluation regime known as Visual Psychophysics to study visual perception. Psychophysics is the quantitative examination of the relationships between controlled stimuli and the behavioral responses they elicit in experimental test subjects. Instead of using summary statistics to gauge performance, psychophysics directs us to construct item-response curves made up of individual stimulus responses to find perceptual thresholds, thus allowing one to identify the exact point at which a subject can no longer reliably recognize the stimulus class. In this article, we introduce a comprehensive evaluation framework for visual recognition models that is underpinned by this methodology. Over millions of procedurally rendered 3D scenes and 2D images, we compare the performance of well-known convolutional neural networks. Our results bring into question recent claims of human-like performance, and provide a path forward for correcting newly surfaced algorithmic deficiencies. How efficient is visual search in real scenes? In searches for targets among arrays of randomly placed distractors, efficiency is often indexed by the slope of the reaction time (RT) × Set Size function. However, it may be impossible to define set size for real scenes. As an approximation, we hand-labeled 100 indoor scenes and used the number of labeled regions as a surrogate for set size. In Experiment 1, observers searched for named objects (a chair, bowl, etc.). With set size defined as the number of labeled regions, search was very efficient ( 5 ms item). When we controlled for a possible guessing strategy in Experiment 2, slopes increased somewhat ( 15 ms item), but they were much shallower than search for a random object among other distinctive objects outside of a scene setting (Exp. 3: 40 ms item). In Experiments 4–6, observers searched repeatedly through the same scene for different objects. Increased familiarity with scenes had modest effects on RTs, while repetition of target items had large effects (>500 ms). We propose that visual search in scenes is efficient because scene-specific forms of attentional guidance can eliminate most regions from the “functional set size” of items that could possibly be the target.