Newness and distinctiveness is claimed in the features of ornamentation as shown inside the broken line circle in the accompanying representation.

Display screen or portion thereof with graphical user interface

Image captioning is a challenging problem owing to the complexity in understanding the image content and diverse ways of describing it in natural language. Recent advances in deep neural networks have substantially improved the performance of this task. Most state-of-the-art approaches follow an encoder-decoder framework, which generates captions using a sequential recurrent prediction model. However, in this paper, we introduce a novel decision-making framework for image captioning. We utilize a policy network and a value network to collaboratively generate captions. The policy network serves as a local guidance by providing the confidence of predicting the next word according to the current state. Additionally, the value network serves as a global and lookahead guidance by evaluating all possible extensions of the current state. In essence, it adjusts the goal of predicting the correct words towards the goal of generating captions similar to the ground truth captions. We train both networks using an actor-critic reinforcement learning model, with a novel reward defined by visual-semantic embedding. Extensive experiments and analyses on the Microsoft COCO dataset show that the proposed framework outperforms state-of-the-art approaches across different evaluation metrics.

/pdf/deep-reinforcement-learning-based-image-captioning-with-eqv8nzr5gh.pdf

Deep Reinforcement Learning-Based Image Captioning with Embedding Reward

Dense captioning is a newly emerging computer vision topic for understanding images with dense language descriptions. The goal is to densely detect visual concepts (e.g., objects, object parts, and interactions between them) from images, labeling each with a short descriptive phrase. We identify two key challenges of dense captioning that need to be properly addressed when tackling the problem. First, dense visual concept annotations in each image are associated with highly overlapping target regions, making accurate localization of each visual concept challenging. Second, the large amount of visual concepts makes it hard to recognize each of them by appearance alone. We propose a new model pipeline based on two novel ideas, joint inference and context fusion, to alleviate these two challenges. We design our model architecture in a methodical manner and thoroughly evaluate the variations in architecture. Our final model, compact and efficient, achieves state-of-the-art accuracy on Visual Genome [23] for dense captioning with a relative gain of 73% compared to the previous best algorithm. Qualitative experiments also reveal the semantic capabilities of our model in dense captioning.

https://openaccess.thecvf.com/content_cvpr_2017/papers/Yang_Dense_Captioning_With_CVPR_2017_paper.pdf

Dense Captioning with Joint Inference and Visual Context

A method executed by a computer includes receiving an image from a client device. A facial recognition technique is executed against an individual face within the image to obtain a recognized face. Privacy rules are applied to the image, where the privacy rules are associated with privacy settings for a user associated with the recognized face. A privacy protected version of the image is distributed, where the privacy protected version of the image has an altered image feature.

Apparatus and method for automated privacy protection in distributed images

A system and method for geolocation-based pictographs are provided. In example embodiments, a current geolocation of a user device is determined. A pictograph is identified based on the current geolocation of the user device. The identified pictograph is presented on a user interface of the user device.

Geolocation-based pictographs

Described are various embodiments of a user content sharing system and method with automated external content integration. In one embodiment, a system and method are provided in which a graphical user interface (GUI) is rendered on each system users' personal communication device. The GUI produces a content selection function selecting, under user operation, user content for sharing, and a sharing platform selection function selecting, under user operation, one or more sharing platforms. The system further comprise a digital content integrator communicatively linked to the user interface to gain access to the selected user content in response to the content selection function, the integrator having access to stored external content distinct from user content. The integrator integrates the external content with the selected user content to output integrated content. The system further comprises a communication interface communicatively linked to the integrator and selectively operable to interface with each of the distinct content sharing platforms, wherein the integrated content is concurrently relayed to the selected sharing platforms via the communication interface on behalf of system users as originating therefrom.

User content sharing system and method with automated external content integration

User content sharing system and method with location-based external content integration

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User content sharing system and method with automated external content integration

User content sharing system and method with location-based external content integration