In the last few years we have seen a growing interest in machine learning approaches to computer vision and, especially, to semantic labeling. Nowadays state of the art systems use deep learning on millions of labeled images with very successful results on benchmarks, though it is unlikely to expect similar results in unrestricted visual environments. Most learning schemes essentially ignore the inherent sequential structure of videos: this might be a critical issue, since any visual recognition process is remarkably more complex when shuffling video frames. Based on this remark, we propose a re-foundation of the communication protocol between visual agents and the environment, which is referred to as learning to see like children. Like for human interaction, visual concepts are acquired by the agents solely by processing their own visual stream along with human supervisions on selected pixels. We give a proof of concept that remarkable semantic labeling can emerge within this protocol by using only a few supervised examples. This is made possible by exploiting a constraint of motion coherent labeling that virtually offers tons of supervisions. Additional visual constraints, including those associated with object supervisions, are used within the context of learning from constraints. The framework is extended in the direction of lifelong learning, so as our visual agents live in their own visual environment without distinguishing learning and test set. Learning takes place in deep architectures under a progressive developmental scheme. In order to evaluate our Developmental Visual Agents (DVAs), in addition to classic benchmarks, we open the doors of our lab, allowing people to evaluate DVAs by crowd-sourcing. Such assessment mechanism might result in a paradigm shift in methodologies and algorithms for computer vision, encouraging truly novel solutions within the proposed framework.
@cite_14: 
@cite_29: In this paper we present a novel approach to multi---view object recognition based on kernel methods with constraints. Differently from many previous approaches, we describe a system that is able to exploit a set of views of an input object to recognize it. Views are acquired by cameras located around the object and each view is modeled by a specific classifier. The relationships among different views are formulated as constraints that are exploited by a sort of collaborative learning process. The proposed approach applies the constraints on unlabeled data in a semi---supervised framework. The results collected on the COIL benchmark show that constraint based learning can improve the quality of the recognition system and of each single classifier, both on the original and noisy data, and it can increase the invariance with respect to object orientation.
@cite_9: In this paper, we address the problem of estimating the optical flow in long-term video sequences. We devise a computational scheme that exploits the idea of receptive fields, in which the pixel flow does not only depends on the brightness level of the pixel itself, but also on neighborhood-related information. Our approach relies on the definition of receptive units that are invariant to affine transformations of the input data. This distinguishing characteristic allows us to build a video-receptive-inputs database with arbitrary detail level, that can be used to match local features and to determine their motion. We propose a parallel computational scheme, well suited for nowadays parallel architectures, to exploit motion information and invariant features from real-time video streams, for deep feature extraction, object detection, tracking, and other applications.
@cite_40: The mathematical foundations of a new theory for the design of intelligent agents are presented. The proposed learning paradigm is centered around the concept of constraint, representing the interactions with the environment, and the parsimony principle. The classical regularization framework of kernel machines is naturally extended to the case in which the agents interact with a richer environment, where abstract granules of knowledge, compactly described by different linguistic formalisms, can be translated into the unified notion of constraint for defining the hypothesis set. Constrained variational calculus is exploited to derive general representation theorems that provide a description of the optimal body of the agent i.e., the functional structure of the optimal solution to the learning problem, which is the basis for devising new learning algorithms. We show that regardless of the kind of constraints, the optimal body of the agent is a support constraint machine SCM based on representer theorems that extend classical results for kernel machines and provide new representations. In a sense, the expressiveness of constraints yields a semantic-based regularization theory, which strongly restricts the hypothesis set of classical regularization. Some guidelines to unify continuous and discrete computational mechanisms are given so as to accommodate in the same framework various kinds of stimuli, for example, supervised examples and logic predicates. The proposed view of learning from constraints incorporates classical learning from examples and extends naturally to the case in which the examples are subsets of the input space, which is related to learning propositional logic clauses.
@cite_49: 
@cite_2: 
@cite_13: 
@cite_20: In the last few years, due to the growing ubiquity of unlabeled data, much effort has been spent by the machine learning community to develop better understanding and improve the quality of classifiers exploiting unlabeled data. Following the manifold regularization approach, Laplacian Support Vector Machines (LapSVMs) have shown the state of the art performance in semi-supervised classification. In this paper we present two strategies to solve the primal LapSVM problem, in order to overcome some issues of the original dual formulation. In particular, training a LapSVM in the primal can be efficiently performed with preconditioned conjugate gradient. We speed up training by using an early stopping strategy based on the prediction on unlabeled data or, if available, on labeled validation examples. This allows the algorithm to quickly compute approximate solutions with roughly the same classification accuracy as the optimal ones, considerably reducing the training time. The computational complexity of the training algorithm is reduced from O(n3) to O(kn2), where n is the combined number of labeled and unlabeled examples and k is empirically evaluated to be significantly smaller than n. Due to its simplicity, training LapSVM in the primal can be the starting point for additional enhancements of the original LapSVM formulation, such as those for dealing with large data sets. We present an extensive experimental evaluation on real world data showing the benefits of the proposed approach.
@cite_17: We propose a general framework to incorporate first-order logic (FOL) clauses, that are thought of as an abstract and partial representation of the environment, into kernel machines that learn within a semi-supervised scheme. We rely on a multi-task learning scheme where each task is associated with a unary predicate defined on the feature space, while higher level abstract representations consist of FOL clauses made of those predicates. We re-use the kernel machine mathematical apparatus to solve the problem as primal optimization of a function composed of the loss on the supervised examples, the regularization term, and a penalty term deriving from forcing real-valued constraints deriving from the predicates. Unlike for classic kernel machines, however, depending on the logic clauses, the overall function to be optimized is not convex anymore. An important contribution is to show that while tackling the optimization by classic numerical schemes is likely to be hopeless, a stage-based learning scheme, in which we start learning the supervised examples until convergence is reached, and then continue by forcing the logic clauses is a viable direction to attack the problem. Some promising experimental results are given on artificial learning tasks and on the automatic tagging of bibtex entries to emphasize the comparison with plain kernel machines.
The theory of @cite_40 was applied in several contexts and with different types of knowledge, such as First-Order Logic clauses @cite_17 @cite_14 and visual relationships in object recognition @cite_29 . In the case of manifold regularization based constraints @cite_20 , our on-line learning system was evaluated using heterogenous data, showing promising results @cite_49 . Finally, the notion of constraint is used in this paper to model motion coherence, thus resembling what is usually done in optical flow algorithms @cite_13 @cite_2 . Motion estimation in DVAs is part of the feature extraction process; some qualitative results can be found in @cite_9 .