Articulated Pose Estimation in a Learned Smooth Space of Feasible Solutions

A learning based framework is proposed for estimating human body pose from a single image. Given a differentiable function that maps from pose space to image feature space, the goal is to invert the process: estimate the pose given only image features. The inversion is an ill-posed problem as the inverse mapping is a one to many process. Hence multiple solutions exist, and it is desirable to restrict the solution space to a smaller subset of feasible solutions. For example, not all human body poses are feasible due to anthropometric constraints. Since the space of feasible solutions may not admit a closed form description, the proposed framework seeks to exploit machine learning techniques to learn an approximation that is smoothly parameterized over such a space. One such technique is Gaussian Process Latent Variable Modelling. Scaled conjugate gradient is then used find the best matching pose in the space of feasible solutions when given an input image. The formulation allows easy incorporation of various constraints, e.g. temporal consistency and anthropometric constraints. The performance of the proposed approach is evaluated in the task of upper-body pose estimation from silhouettes and compared with the Specialized Mapping Architecture. The estimation accuracy of the Specialized Mapping Architecture is at least one standard deviation worse than the proposed approach in the experiments with synthetic data. In experiments with real video of humans performing gestures, the proposed approach produces qualitatively better estimation results.

Some Considerations on a Calculus with Weak References

Weak references are references that do not prevent the object they point to from being garbage collected. Most realistic languages, including Java, SML/NJ, and OCaml to name a few, have some facility for programming with weak references. Weak references are used in implementing idioms like memoizing functions and hash-consing in order to avoid potential memory leaks. However, the semantics of weak references in many languages are not clearly specified. Without a formal semantics for weak references it becomes impossible to prove the correctness of implementations making use of this feature. Previous work by Hallett and Kfoury extends λgc, a language for modeling garbage collection, to λweak, a similar language with weak references. Using this previously formalized semantics for weak references, we consider two issues related to well-behavedness of programs. Firstly, we provide a new, simpler proof of the well-behavedness of the syntactically restricted fragment of λweak defined previously. Secondly, we give a natural semantic criterion for well-behavedness much broader than the syntactic restriction, which is useful as principle for programming with weak references. Furthermore we extend the result, proved in previously of λgc, which allows one to use type-inference to collect some reachable objects that are never used. We prove that this result holds of our language, and we extend this result to allow the collection of weakly-referenced reachable garbage without incurring the computational overhead sometimes associated with collecting weak bindings (e.g. the need to recompute a memoized function). Lastly we use extend the semantic framework to model the key/value weak references found in Haskell and we prove the Haskell is semantics equivalent to a simpler semantics due to the lack of side-effects in our language.

A Formal Semantics for Weak References

A weak reference is a reference to an object that is not followed by the pointer tracer when garbage collection is called. That is, a weak reference cannot prevent the object it references from being garbage collected. Weak references remain a troublesome programming feature largely because there is not an accepted, precise semantics that describes their behavior (in fact, we are not aware of any formalization of their semantics). The trouble is that weak references allow reachable objects to be garbage collected, therefore allowing garbage collection to influence the result of a program. Despite this difficulty, weak references continue to be used in practice for reasons related to efficient storage management, and are included in many popular programming languages (Standard ML, Haskell, OCaml, and Java). We give a formal semantics for a calculus called λweak that includes weak references and is derived from Morrisett, Felleisen, and Harper’s λgc. λgc formalizes the notion of garbage collection by means of a rewrite rule. Such a formalization is required to precisely characterize the semantics of weak references. However, the inclusion of a garbage-collection rewrite-rule in a language with weak references introduces non-deterministic evaluation, even if the parameter-passing mechanism is deterministic (call-by-value in our case). This raises the question of confluence for our rewrite system. We discuss natural restrictions under which our rewrite system is confluent, thus guaranteeing uniqueness of program result. We define conditions that allow other garbage collection algorithms to co-exist with our semantics of weak references. We also introduce a polymorphic type system to prove the absence of erroneous program behavior (i.e., the absence of “stuck evaluation”) and a corresponding type inference algorithm. We prove the type system sound and the inference algorithm sound and complete.

Towards Formalizing Java's Weak References

Weak references provide the programmer with limited control over the process of memory management. By using them, a programmer can make decisions based on previous actions that are taken by the garbage collector. Although this is often helpful, the outcome of a program using weak references is less predictable due to the nondeterminism they introduce in program evaluation. It is therefore desirable to have a framework of formal tools to reason about weak references and programs that use them. We present several calculi that formalize various aspects of weak references, inspired by their implementation in Java. We provide a calculus to model multiple levels of non-strong references, where a different garbage collection policy is applied to each level. We consider different collection policies such as eager collection and lazy collection. Similar to the way they are implemented in Java, we give the semantics of eager collection to weak references and the semantics of lazy collection to soft references. Moreover, we condition garbage collection on the availability of time and space resources. While time constraints are used in order to restrict garbage collection, space constraints are used in order to trigger it. Finalizers are a problematic feature in Java, especially when they interact with weak references. We provide a calculus to model finalizer evaluation. Since finalizers have little meaning in a language without side-effect, we introduce a limited form of side effect into the calculus. We discuss determinism and the separate notion of uniqueness of (evaluation) outcome. We show that in our calculus, finalizer evaluation does not affect uniqueness of outcome.