Unsupervised Adaptive Re-identification in Open World Dynamic Camera Networks

Person re-identification is an open and challenging problem in computer vision. Existing approaches have concentrated on either designing the best feature representation or learning optimal matching metrics in a static setting where the number of cameras are fixed in a network. Most approaches have neglected the dynamic and open world nature of the re-identification problem, where a new camera may be temporarily inserted into an existing system to get additional information. To address such a novel and very practical problem, we propose an unsupervised adaptation scheme for re-identification models in a dynamic camera network. First, we formulate a domain perceptive re-identification method based on geodesic flow kernel that can effectively find the best source camera (already installed) to adapt with a newly introduced target camera, without requiring a very expensive training phase. Second, we introduce a transitive inference algorithm for re-identification that can exploit the information from best source camera to improve the accuracy across other camera pairs in a network of multiple cameras. Extensive experiments on four benchmark datasets demonstrate that the proposed approach significantly outperforms the state-of-the-art unsupervised learning based alternatives whilst being extremely efficient to compute.Comment: CVPR 2017 Spotligh

Adaptation of Person Re-identification Models for On-boarding New Camera(s)

Existing approaches for person re-identification have concentrated on either designing the best feature representation or learning optimal matching metrics in a static setting where the number of cameras are fixed in a network. Most approaches have neglected the dynamic and open world nature of the re- identification problem, where one or multiple new cameras may be temporarily on-boarded into an ex- isting system to get additional information or added to expand an existing network. To address such a very practical problem, we propose a novel approach for adapting existing multi-camera re-identification frameworks with limited supervision. First, we formulate a domain perceptive re-identification method based on geodesic flow kernel that can effectively find the best source camera (already installed) to adapt with newly introduced target camera(s), without requiring a very expensive training phase. Second, we introduce a transitive inference algorithm for re-identification that can exploit the information from best source camera to improve the accuracy across other camera pairs in a network of multiple cameras. Third, we develop a target-aware sparse prototype selection strategy for finding an informative subset of source camera data for data-efficient learning in resource constrained environments. Our approach can greatly increase the flexibility and reduce the deployment cost of new cameras in many real-world dy- namic camera networks. Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art unsupervised alternatives whilst being extremely efficient to compute

The C*-algebra of an affine map on the 3-torus

We study the C*-algebra of an affine map on a compact abelian group and give necessary and sufficient conditions for strong transitivity when the group is a torus. The structure of the C*-algebra is completely determined for all strongly transitive affine maps on a torus of dimension one, two or three

Transitivity of visual sameness

The way in which vision represents objects as being the same despite movement and qualitative changes has been extensively investigated in contemporary psychology. However, the formal properties of the visual sameness relation are still unclear, for example, whether it is an identity-like, equivalence relation. The paper concerns one aspect of this problem: the transitivity of visual sameness. Results obtained by using different experimental paradigms are analysed, in particular studies using streaming/bouncing stimuli, multiple object tracking experiments and investigations concerning object-specific preview benefit, and it is argued that the transitive interpretation of visual sameness is the most plausible given the current stage of knowledge. What is more, it is claimed that the way in which visual sameness is represented suggests that in some cases it should be characterized as a “primitive sameness”, similarly as in philosophical theories postulating “thisness”

On the density of singular hyperbolic three-dimensional vector fields: a conjecture of Palis

In this note we announce a result for vector fields on three-dimensional manifolds: those who are singular hyperbolic or exhibit a homoclinic tangency form a dense subset of the space of $C^1$-vector fields. This answers a conjecture by Palis. The argument uses an extension for local fibered flows of Ma\~n\'e and Pujals-Sambarino's theorems about the uniform contraction of one-dimensional dominated bundles. Sur la densit\'e de l'hyperbolicit\'e singuli\`ere pour les champs de vecteurs en dimension trois : une conjecture de Palis Dans cette note, nous annon\c{c}ons un r\'esultat portant sur les champs de vecteurs des vari\'et\'es de dimension $3$ : ceux qui v\'erifient l'hyperbolicit\'e singuli\`ere ou qui poss\`edent une tangence homocline forment un sous-ensemble dense de l'espace des champs de vecteurs $C^1$. Ceci r\'epond \`a une conjecture de Palis. La d\'emonstration utilise une g\'en\'eralisation pour les flots fibr\'es locaux des th\'eor\`emes de Ma\~n\'e et Pujals-Sambarino traitant de la contraction uniforme de fibr\'es unidimensionnels domin\'es

Matching Dependencies with Arbitrary Attribute Values: Semantics, Query Answering and Integrity Constraints

Matching dependencies (MDs) were introduced to specify the identification or matching of certain attribute values in pairs of database tuples when some similarity conditions are satisfied. Their enforcement can be seen as a natural generalization of entity resolution. In what we call the "pure case" of MDs, any value from the underlying data domain can be used for the value in common that does the matching. We investigate the semantics and properties of data cleaning through the enforcement of matching dependencies for the pure case. We characterize the intended clean instances and also the "clean answers" to queries as those that are invariant under the cleaning process. The complexity of computing clean instances and clean answers to queries is investigated. Tractable and intractable cases depending on the MDs and queries are identified. Finally, we establish connections with database "repairs" under integrity constraints.Comment: 13 pages, double column, 2 figure