In this work, we propose a novel approach that predicts the relationships between various entities in an image in a weakly supervised manner by relying on image captions and object bounding box annotations as the sole source of supervision.Our proposed approach uses a top-down attention mechanism to align entities in captions to objects in the image, and then leverage the syntactic structure of the captions to align the relations.We use these alignments to train a relation classification network, thereby obtaining both grounded captions and dense relationships.We demonstrate the effectiveness of our model on the Visual Genome dataset by achieving a recall@50 of 15% and recall@100 of 25% on the relationships present in the image.We also show that the model successfully predicts relations that are not present in the corresponding captions.

In this paper, a novel image watermarking scheme is proposed which is robust to resolution scaling. In the proposed method, SIFT (Scale Invariant Feature Transform) features are used which are invariant to rotation, translation and scaling. Most of the existing watermarking algorithms that uses SIFT features, fail to retain the watermark when the image is heavily scaled down. In the proposed scheme, a context coherent object or patch is inserted in the image such that it generates strong SIFT features. These newly generated SIFT features themselves work as a watermark. Since the SIFT features are invariant to scaling, these features can be extracted from any image resolution with high probability. The experimental results support the contention that the proposed method is considerably robust against resolution scaling.