No abstract

In this paper we present the first large-scale scene attribute database. First, we perform crowdsourced human studies to find a taxonomy of 102 discriminative attributes. We discover attributes related to materials, surface properties, lighting, affordances, and spatial layout. Next, we build the "SUN attribute database" on top of the diverse SUN categorical database. We use crowdsourcing to annotate attributes for 14,340 images from 707 scene categories. We perform numerous experiments to study the interplay between scene attributes and scene categories. We train and evaluate attribute classifiers and then study the feasibility of attributes as an intermediate scene representation for scene classification, zero shot learning, automatic image captioning, semantic image search, and parsing natural images. We show that when used as features for these tasks, low dimensional scene attributes can compete with or improve on the state of the art performance. The experiments suggest that scene attributes are an effective low-dimensional feature for capturing high-level context and semantics in scenes.

Hearing loss is a major risk factor for tinnitus, hyperacusis, and central auditory processing disorder. Although recent studies indicate that hearing loss causes neuroinflammation in the auditory pathway, the mechanisms underlying hearing loss–related pathologies are still poorly understood. We examined neuroinflammation in the auditory cortex following noise-induced hearing loss (NIHL) and its role in tinnitus in rodent models. Our results indicate that NIHL is associated with elevated expression of proinflammatory cytokines and microglial activation—two defining features of neuroinflammatory responses—in the primary auditory cortex (AI). Genetic knockout of tumor necrosis factor alpha (TNF-α) or pharmacologically blocking TNF-α expression prevented neuroinflammation and ameliorated the behavioral phenotype associated with tinnitus in mice with NIHL. Conversely, infusion of TNF-α into AI resulted in behavioral signs of tinnitus in both wild-type and TNF-α knockout mice with normal hearing. Pharmacological depletion of microglia also prevented tinnitus in mice with NIHL. At the synaptic level, the frequency of miniature excitatory synaptic currents (mEPSCs) increased and that of miniature inhibitory synaptic currents (mIPSCs) decreased in AI pyramidal neurons in animals with NIHL. This excitatory-to-inhibitory synaptic imbalance was completely prevented by pharmacological blockade of TNF-α expression. These results implicate neuroinflammation as a therapeutic target for treating tinnitus and other hearing loss–related disorders.

In this paper, we discover and annotate visual attributes for the COCO dataset. With the goal of enabling deeper object understanding, we deliver the largest attribute dataset to date. Using our COCO Attributes dataset, a fine-tuned classification system can do more than recognize object categories -for example, rendering multi-label classifications such as "sleeping spotted curled-up cat" instead of simply "cat". To overcome the expense of annotating thousands of COCO object instances with hundreds of attributes, we present an Economic Labeling Algorithm (ELA) which intelligently generates crowd labeling tasks based on correlations between attributes. The ELA offers a substantial reduction in labeling cost while largely maintaining attribute density and variety. Currently, we have collected 3.5 million object-attribute pair annotations describing 180 thousand different objects. We demonstrate that our efficiently labeled training data can be used to produce classifiers of similar discriminative ability as classifiers created using exhaustively labeled ground truth. Finally, we provide baseline performance analysis for object attribute recognition.