Abstract-A combination of query-by-visual-example (QBVE) and semantic retrieval (SR), denoted as query-by-semantic-example (QBSE), is proposed. Images are labeled with respect to a vocabulary of visual concepts, as is usual in SR. Each image is then represented by a vector, referred to as a semantic multinomial, of posterior concept probabilities. Retrieval is based on the query-by-example paradigm: the user provides a query image, for which 1) a semantic multinomial is computed and 2) matched to those in the database. QBSE is shown to have two main properties of interest, one mostly practical and the other philosophical. From a practical standpoint, because it inherits the generalization ability of SR inside the space of known visual concepts (referred to as the semantic space) but performs much better outside of it, QBSE produces retrieval systems that are more accurate than what was previously possible. Philosophically, because it allows a direct comparison of visual and semantic representations under a common query paradigm, QBSE enables the design of experiments that explicitly test the value of semantic representations for image retrieval. An implementation of QBSE under the minimum probability of error (MPE) retrieval framework, previously applied with success to both QBVE and SR, is proposed, and used to demonstrate the two properties. In particular, an extensive objective comparison of QBSE with QBVE is presented, showing that the former significantly outperforms the latter both inside and outside the semantic space. By carefully controlling the structure of the semantic space, it is also shown that this improvement can only be attributed to the semantic nature of the representation on which QBSE is based.Index Terms-Content-based image retrieval, Gaussian mixtures, image similarity, multiple instance learning, query by example, semantic retrieval, semantic space.

We develop an inverse graphics approach to the problem of scene understanding, obtaining a rich representation that includes descriptions of the objects in the scene and their spatial layout, as well as global latent variables like the camera parameters and lighting. The framework's stages include object detection, the prediction of the camera and lighting variables, and prediction of object-specific variables (shape, appearance and pose). This acts like the encoder of an autoencoder, with graphics rendering as the decoder. Importantly the scene representation is interpretable and is of variable dimension to match the detected number of objects plus the global variables. For the prediction of the camera latent variables we introduce a novel architecture termed Probabilistic HoughNets (PHNs), which provides a principled approach to combining information from multiple detections. We demonstrate the quality of the reconstructions obtained quantitatively on synthetic data, and qualitatively on real scenes.

The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis. More recently, AI techniques have been applied to football, due to a huge increase in data collection by professional teams, increased computational power, and advances in machine learning, with the goal of better addressing new scientific challenges involved in the analysis of both individual players’ and coordinated teams’ behaviors. The research challenges associated with predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision. In this paper, we provide an overarching perspective highlighting how the combination of these fields, in particular, forms a unique microcosm for AI research, while offering mutual benefits for professional teams, spectators, and broadcasters in the years to come. We illustrate that this duality makes football analytics a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for the field of AI. We review the state-of-the-art and exemplify the types of analysis enabled by combining the aforementioned fields, including illustrative examples of counterfactual analysis using predictive models, and the combination of game-theoretic analysis of penalty kicks with statistical learning of player attributes. We conclude by highlighting envisioned downstream impacts, including possibilities for extensions to other sports (real and virtual).

Scene understanding tasks such as the prediction of object pose, shape, appearance and illumination are hampered by the occlusions often found in images. We propose a vision-as-inverse-graphics approach to handle these occlusions by making use of a graphics renderer in combination with a robust generative model (GM). Since searching over scene factors to obtain the best match for an image is very inefficient, we make use of a recognition model (RM) trained on synthetic data to initialize the search. This paper addresses two issues: (i) We study how the inferences are affected by the degree of occlusion of the foreground object, and show that a robust GM which includes an outlier model to account for occlusions works significantly better than a non-robust model. (ii) We characterize the performance of the RM and the gains that can be made by refining the search using the GM, using a new dataset that includes background clutter and occlusions. We find that pose and shape are predicted very well by the RM, but appearance and especially illumination less so. However, accuracy on these latter two factors can be clearly improved with the generative model.

Automatic acquisition of raw source material is of great aid for the compilation of dictionaries, and, in particular, of specialized dictionaries such as collocation dictionaries. The extraction of collocations from corpora has been actively worked on since the late eighties. The quality of the state-of-the-art extraction algorithms allows the lexicographers to obtain lists of collocations they can work with. However, mere lists of collocations are not sufficient. In collocation dictionaries, collocations are grouped semantically, which also presupposes a semantic classification of collocations. In this article, a distributional semantics-based model is proposed that classifies collocations with respect to broad semantic categories as encountered in dictionaries. In experiments with Spanish verb-noun and noun-adjective collocations from the lexicographic field of emotion nouns, it is shown that the use of features extracted from the context of collocations is decisive for retrieval of draft entries for collocation dictionaries.

In multiagent worlds, several decision-making individuals interact while adhering to the dynamics constraints imposed by the environment. These interactions, combined with the potential stochasticity of the agents’ dynamic behaviors, make such systems complex and interesting to study from a decision-making perspective. Significant research has been conducted on learning models for forward-direction estimation of agent behaviors, for example, pedestrian predictions used for collision-avoidance in self-driving cars. In many settings, only sporadic observations of agents may be available in a given trajectory sequence. In football, subsets of players may come in and out of view of broadcast video footage, while unobserved players continue to interact off-screen. In this paper, we study the problem of multiagent time-series imputation in the context of human football play, where available past and future observations of subsets of agents are used to estimate missing observations for other agents. Our approach, called the Graph Imputer, uses past and future information in combination with graph networks and variational autoencoders to enable learning of a distribution of imputed trajectories. We demonstrate our approach on multiagent settings involving players that are partially-observable, using the Graph Imputer to predict the behaviors of off-screen players. To quantitatively evaluate the approach, we conduct experiments on football matches with ground truth trajectory data, using a camera module to simulate the off-screen player state estimation setting. We subsequently use our approach for downstream football analytics under partial observability using the well-established framework of pitch control, which traditionally relies on fully observed data. We illustrate that our method outperforms several state-of-the-art approaches, including those hand-crafted for football, across all considered metrics.

The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis. More recently, AI techniques have been applied to football, due to a huge increase in data collection by professional teams, increased computational power, and advances in machine learning, with the goal of better addressing new scientific challenges involved in the analysis of both individual players' and coordinated teams' behaviors. The research challenges associated with predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision. In this paper, we provide an overarching perspective highlighting how the combination of these fields, in particular, forms a unique microcosm for AI research, while offering mutual benefits for professional teams, spectators, and broadcasters in the years to come. We illustrate that this duality makes football analytics a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for the field of AI. We review the state-of-theart and exemplify the types of analysis enabled by combining the aforementioned fields, including illustrative examples of counterfactual analysis using predictive models, and the combination of game-theoretic analysis of penalty kicks with statistical learning of player attributes. We conclude by highlighting envisioned downstream impacts, including possibilities for extensions to other sports (real and virtual).

The heat equation can be used to model the diffusion process shown in a defocused (blurry) region of a picture taken with conventional camera lens. The original focused image can be recovered by reverting the heat equation, that is, by reverse diffusion. However, the main difficulty with this technique is that it becomes unstable very quickly due to the finite precision of pixel values and the image values blow up. For that reason, detecting the exact time when the reverse diffusion process should stop is crucial. The goal of this work it to evaluate the behavior of different non-reference state-of-the-art sharpness measures (that is, when a perfectly focused image is not available) for the forward and inverse diffusion processes and to propose a robust stop criterion to reliably detect the moment before each region becomes unstable. To find out a good stop criterion, we carry out a set of experiments with test and real images. The results in this paper can be valuable not only to estimate monocular depth from blur cues, but also to any other image processing fields that require image deblurring.