10 accepted papers
Weakly supervised Referring Expression Comprehension (WREC) aims to localize referred objects using only image-text pairs without box-level annotations. Existing one-stage methods predominantly rely on anchor-level alignment, which suffers from two fundamental limitations: (1) anchors represent loca
Generalizable gaze estimation methods have garnered increasing attention due to their critical importance in real-world applications and have achieved significant progress. However, they often overlook the effect of label noise, arising from the inherent difficulty of acquiring precise gaze annotati
Classical analysis of convex and non-convex optimization methods often requires the Lipschitz continuity of the gradient, which limits the analysis to functions bounded by quadratics. Recent work relaxed this requirement to a non-uniform smoothness condition with the Hessian norm bounded by an affi…
We provide a first-order oracle complexity lower bound for finding stationary points of min-max optimization problems where the objective function is smooth, nonconvex in the minimization variable, and strongly concave in the maximization variable. We establish a lower bound of $\Omega\left(\sqrt{\k…
We study online learning in unknown Markov games, a problem that arises in episodic multi-agent reinforcement learning where the actions of the opponents are unobservable. We show that in this challenging setting, achieving sublinear regret against the best response in hindsight is statistically har…
We study multi-objective reinforcement learning (RL) where an agent’s reward is represented as a vector. In settings where an agent competes against opponents, its performance is measured by the distance of its average return vector to a target set. We develop statistically and computationally effic…
We study minimax optimal reinforcement learning in episodic factored Markov decision processes (FMDPs), which are MDPs with conditionally independent transition components. Assuming the factorization is known, we propose two model-based algorithms. The first one achieves minimax optimal regret guara…
In this paper, we propose a deep progressive reinforcement learning (DPRL) method for action recognition in skeleton-based videos, which aims to distil the most informative frames and discard ambiguous frames in sequences for recognizing actions. Since the choices of selecting representative frames…
Recent works have shown that hierarchical models lead to significant improvement in human activity recognition, which can not only enhance descriptive capability, but also improve discriminative power. However, most existing methods exploit just one of the two advantages. In this paper, a new hierar…
A significant current research trend in image quality assessment is to investigate the added value of visual attention aspects. Previous approaches mainly focused on adopting a specific saliency model to improve a specific image quality metric (IQM). It is still not known yet which of the existing s…