Multi-Scale Low-Discriminative Feature Reactivation for Weakly Supervised Object Localization.

For weakly supervised object localization (WSOL), how to avoid the network focusing only on some small discriminative parts is a main challenge needed to solve. The widely-used Class Activation Mapping (CAM) based paradigm usually employs Adversarial Learning (AL) strategy to search more object parts by constantly hiding discovered object features, but the adversarial process is difficult to control. In this paper, we propose a novel CAM-based framework with Multi-scale Low-Discriminative Feature Reactivation (mLDFR) for WSOL.

Multi-View Multi-Instance Learning Based on Joint Sparse Representation and Multi-View Dictionary Learning.

In multi-instance learning (MIL), the relations among instances in a bag convey important contextual information in many applications. Previous studies on MIL either ignore such relations or simply model them with a fixed graph structure so that the overall performance inevitably degrades in complex environments. To address this problem, this paper proposes a novel multi-view multi-instance learning algorithm (MIL) that combines multiple context structures in a bag into a unified framework.

Characterization and photocatalytic properties of Ni-doped Sr(10)Bi(6)O(24-y).

The compound Sr(10)Bi(6)O(24-y) doped with Ni was prepared by solid-state reaction method. The obtained powders were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), UV-vis diffuse reflectance spectra and X-ray photoemission spectra (XPS). The Ni-doped Sr(10)Bi(6)O(24-y) samples assume a cubic perovskite structure with space group Fm3m (225).