Many tasks in video analysis and understanding boil down to the need for frame-based feature learning, aiming to encapsulate the relevant visual content so as to enable simpler and easier subsequent processing. While supervised strategies for this learning task can be envisioned, self and weakly-supervised alternatives are preferred due to the difficulties in getting labeled data. This paper introduces LRProp – a novel weakly-supervised representation learning approach, with an emphasis on the application of temporal alignment between pairs of videos of the same action category. The proposed approach uses a transformer encoder for extracting frame-level features, and employs the DTW algorithm within the training iterations in order to identify the alignment path between video pairs. Through a process referred to as "pair-wise position propagation", the probability distributions of these correspondences per location are matched with the similarity of the frame-level features via KL-divergence minimization. The proposed algorithm uses also a regularized SoftDTW loss for better tuning the learned features. Our novel representation learning paradigm consistently outperforms the state of the art on temporal alignment tasks, establishing a new performance bar over several downstream video analysis applications.

Detection and segmentation of surgical instruments is an important problem for laparoscopic surgery. Accurate pixel-wise instrument segmentation is a useful intermediate task for the development of computer-assisted surgery systems, such as pose estimation, surgical phase estimation, enhanced image fusion, video retrieval and others. In this paper we describe a deep learning-based approach to instrument segmentation, which addresses the binary segmentation problem in which every pixel in an image is labeled as instrument or background. The key novelty of our approach relates to the use of training data which is inexpensive and fast to acquire. First, our approach relies on weak annotations provided as bounding boxes of the instruments, which are much faster and cheaper to obtain than a dense pixel-level annotations. Second, to further improve the system’s accuracy we propose a novel approach to generate synthetic training images. Our approach achieves state-of-the-art results, outperforming previously proposed methods for automatic instrument segmentation, based only on weak annotations.

We revisit existing ensemble diversification approaches and present two novel diversification methods tailored for open-set scenarios. The first method uses a new loss, designed to encourage models disagreement on outliers only, thus alleviating the intrinsic accuracy-diversity trade-off. The second method achieves diversity via automated feature engineering, by training each model to disregard input features learned by previously trained ensemble models. We conduct an extensive evaluation and analysis of the proposed techniques on seven datasets that cover image classification, re-identification and recognition domains. We compare to and demonstrate accuracy improvements over the existing state-of-the-art ensemble diversification methods.

In this paper we introduce a novel Depth-Aware Video Saliency approach to predict human focus of attention when viewing videos that contain a depth map (RGBD) on a 2D screen. Saliency estimation in this scenario is highly important since in the near future 3D video content will be easily acquired yet hard to display. Despite considerable progress in 3D display technologies, most are still expensive and require special glasses for viewing, so RGBD content is primarily viewed on 2D screens, removing the depth channel from the final viewing experience. We train a generative convolutional neural network that predicts the 2D viewing saliency map for a given frame using the RGBD pixel values and previous fixation estimates in the video. To evaluate the performance of our approach, we present a new comprehensive database of 2D viewing eye-fixation ground-truth for RGBD videos. Our experiments indicate that it is beneficial to integrate depth into video saliency estimates for content that is viewed on a 2D display. We demonstrate that our approach outperforms state-of-the-art methods for video saliency, achieving 15% relative improvement.

While the detection of the interesting regions in images has been extensively studied, relatively few papers have addressed surfaces. This paper proposes an algorithm for detecting the regions of interest of surfaces. It looks for regions that are distinct both locally and globally and accounts for the distance to the foci of attention. It is also shown how this algorithm can be adopted to saliency detection in point clouds. Many applications can utilize these regions. In this paper we explore one such application-viewpoint selection. The most informative views are those that collectively provide the most descriptive presentation of the surface. We show that our results compete favorably with the state-of-the-art results.

Colorization refers to the process of adding color to black and white images or videos. This paper extends the term to handle surfaces in three dimensions. This is important for applications in which the colors of an object need to be restored and no relevant image exists for texturing it. We focus on surfaces with patterns and propose a novel algorithm for adding colors to these surfaces. The user needs only to scribble a few color strokes on one instance of each pattern, and the system proceeds to automatically colorize the whole surface. For this scheme to work, we address not only the problem of colorization, but also the problem of pattern detection on surfaces.

While saliency in images has been extensively studied in recent years, there is very little work on saliency of point sets. This is despite the fact that point sets and range data are becoming ever more widespread and have myriad applications. In this paper we present an algorithm for detecting the salient points in unorganized 3D point sets. Our algorithm is designed to cope with extremely large sets, which may contain tens of millions of points. Such data is typical of urban scenes, which have recently become commonly available on the web. No previous work has handled such data. For general data sets, we show that our results are competitive with those of saliency detection of surfaces, although we do not have any connectivity information. We demonstrate the utility of our algorithm in two applications: producing a set of the most informative viewpoints and suggesting an informative city tour given a city scan.

While the detection of the interesting regions in images has been extensively studied, relatively few papers have addressed surfaces. This paper proposes an algorithm for detecting the regions of interest of surfaces. It looks for regions that are distinct both locally and globally and accounts for the distance to the foci of attention. Many applications can utilize these regions. In this paper we explore one such application - viewpoint selection. The most informative views are those that collectively provide the most descriptive presentation of the surface. We show that our results compete favorably with the state-of-the-art results.

Abstract This paper proposes a novel algorithm for colorization of meshes. This is important for applications in which the model needs to be colored by just a handful of colors or when no relevant image exists for texturing the model. For instance, archaeologists argue that the great Roman or Greek statues were full of color in the days of their creation, and traces of the original colors can be found. In this case, our system lets the user scribble some desired colors in various regions of the mesh. Colorization is then formulated as a constrained quadratic optimization problem, which can be readily solved. Special care is taken to avoid color bleeding between regions, through the definition of a new direction field on meshes.

This paper addresses the problem of automatic reconstruction of a 3D relief from a line drawing on top of a given base object. Reconstruction is challenging due to four reasons-the sparsity of the strokes, their ambiguity, their large number, and their inter-relations. Our approach is able to reconstruct a model from a complex drawing that consists of many inter-related strokes. Rather than viewing the inter-dependencies as a problem, we show how they can be exploited to automatically generate a good initial interpretation of the line drawing. Then, given a base and an interpretation, we propose an algorithm for reconstructing a consistent surface. The strength of our approach is demonstrated in the reconstruction of archaeological artifacts from drawings. These drawings are highly challenging, since artists created very complex and detailed descriptions of artifacts regardless of any considerations concerning their future use for shape reconstruction.

Constructing new, complex models is often done by reusing parts of existing models, typically by applying a sequence of segmentation, alignment and composition operations. Segmentation, either manual or automatic, is rarely adequate for this task, since it is applied to each model independently, leaving it to the user to trim the models and determine where to connect them. In this paper we propose a new composition tool. Our tool obtains as input two models, aligned either manually or automatically, and a small set of constraints indicating which portions of the two models should be preserved in the final output. It then automatically negotiates the best location to connect the models, trimming and stitching them as required to produce a seamless result. We offer a method based on the graph theoretic minimal cut as a means of implementing this new tool. We describe a system intended for both expert and novice users, allowing easy and flexible control over the composition result. In addition, we show our method to be well suited for a variety of model processing applications such as model repair, hole filling, and piecewise rigid deformations.