Scene Context Impairs Perception of Semantically Congruent Objects

Visual scene context is well-known to facilitate the recognition of scene-congruent objects. Interestingly, however, according to predictive-processing accounts of brain function, scene congruency may lead to reduced (rather than enhanced) processing of congruent objects, compared with incongruent ones, because congruent objects elicit reduced prediction-error responses. We tested this counterintuitive hypothesis in two online behavioral experiments with human participants ( N = 300). We found clear evidence for impaired perception of congruent objects, both in a change-detection task measuring response times and in a bias-free object-discrimination task measuring accuracy. Congruency costs were related to independent subjective congruency ratings. Finally, we show that the reported effects cannot be explained by low-level stimulus confounds, response biases, or top-down strategy. These results provide convincing evidence for perceptual congruency costs during scene viewing, in line with predictive-processing theory.

Siamese anchor-free object tracking with multiscale spatial attentions

Recently, object trackers based on Siamese networks have attracted considerable attentions due to their remarkable tracking performance and widespread application. Especially, the anchor-based methods exploit the region proposal subnetwork to get accurate prediction of a target and make great performance improvement. However, those trackers cannot capture the spatial information very well and the pre-defined anchors will hinder robustness. To solve these problems, we propose a Siamese-based anchor-free object tracking algorithm with multiscale spatial attentions in this paper. Firstly, we take ResNet-50 as the backbone network to generate multiscale features of both template patch and search regions. Secondly, we propose the spatial attention extraction (SAE) block to capture the spatial information among all positions in the template and search region feature maps. Thirdly, we put these features into the SAE block to get the multiscale spatial attentions. Finally, an anchor-free classification and regression subnetwork is used for predicting the location of the target. Unlike anchor-based methods, our tracker directly predicts the target position without predefined parameters. Extensive experiments with state-of-the-art trackers are carried out on four challenging visual object tracking benchmarks: OTB100, UAV123, VOT2016 and GOT-10k. Those experimental results confirm the effectiveness of our proposed tracker.