5 accepted papers
Invariant learning can fail even when the invariant structure is statistically identifiable. We show an inherent computational barrier: under the Planted Clique hypothesis, there exist samplable linear-Gaussian multi-environment instances with a one-dimensional invariant subspace ($k=1$) that are le…
Deep learning-based methods have made significant progress in image dehazing. However, these methods often falter when applied to real-world hazy images, primarily due to the scarcity of paired real-world data and the limitations of current dehazing feature extractors. Toward these issues, we introd…
Rooting in the scarcity of most attributes, realistic pedestrian attribute datasets exhibit unduly skewed data distribution, from which two types of model failures are delivered: (1) label imbalance: model predictions lean greatly towards the side of majority labels; (2) semantics imbalance: model i…
Recent studies on pedestrian attribute recognition progress with either explicit or implicit modeling of the co-occurence among attributes. Considering that this known a prior is highly variable and unforeseeable regarding the specific scenarios, we show that current methods can actually suffer in g…
Band selection has a great impact on the spectral recovery quality. To solve this ill-posed inverse problem, most band selection methods adopt hand-crafted priors or exploit clustering or sparse regularization constraints to find most prominent bands. These methods are either very slow due to the co…