English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Using image context is an effective approach for improving object detection.Previously proposed methods used contextual cues that rely on semantic orspatial information. In this work, we explore a different kind of contextualinformation: inner-scene similarity. We present the CISS (Context by InnerScene Similarity) algorithm, which is based on the observation that twovisually similar sub-image patches are likely to share semantic identities,especially when both appear in the same image. CISS uses base-scores providedby a base detector and performs as a post-detection stage. For each candidatesub-image (denoted anchor), the CISS algorithm finds a few similar sub-images(denoted supporters), and, using them, calculates a new enhanced score for theanchor. This is done by utilizing the base-scores of the supporters and apre-trained dependency model. The new scores are modeled as a linear functionof the base scores of the anchor and the supporters and is estimated using aminimum mean square error optimization. This approach results in: (a) improveddetection of partly occluded objects (when there are similar non-occludedobjects in the scene), and (b) fewer false alarms (when the base detectormistakenly classifies a background patch as an object). This work relates toDuncan and Humphreys' "similarity theory," a psychophysical study. whichsuggested that the human visual system perceptually groups similar imageregions and that the classification of one region is affected by the estimatedidentity of the other. Experimental results demonstrate the enhancement of abase detector's scores on the PASCAL VOC dataset.

Inner-Scene Similarities as a Contextual Cue for Object Detection