You can still find and save the original image by clicking on the thumbnail in the search results, then right-clicking the image and selecting "Open Image in New Tab." There you'll find the original you would have found using the recently deceased "View Image" button.

Today we're launching some changes on Google Images to help connect users and useful websites. This will include removing the View Image button. The Visit button remains, so users can see images in the context of the webpages they're on. The Search by Image button is also being removed. Reverse image search *still works* through the way most people use it, from the search bar of Google Images.

Google has introduced a change in how it presents image search results yesterday. Google has removed the convenient "view image" button from image search which allowed users to open the image alone instead of opening the website where the image was originally published. "Today we're launching some changes on Google Images to help connect users and useful websites. This will include removing the View Image button," Google said on its SearchLiaison Twitter page. "The Visit button remains, so users can see images in the context of the webpages they're on."

The media service at Grubhub ingests and manages images for every menu item currently available on the Grubhub platform. These images need to be moderated for prohibited content and quality before they are presented to our diners. Manual moderation of millions of pre-existing images on the platform along with the ones constantly being added everyday, is a tedious task. Automating this process saves time of the manual moderators allowing them to focus only on moderating images that cannot be approved by the automated process. Owing to the increase in computational power due to the advent of Graphic Processing Units (GPUs), usage of Neural Networks to identify objects in images has become feasible.

AI is a considerably massive field. In recent years, with the extensive on-going research, generation of massive data sets and availability of massive computing power, Deep Learning has become one of most exciting fields of this era. Lets have a look at one of the foremost and supreme applications of Deep Learning which at the forefront of innovation and technology. Image Recognition is at the sweet intersection b/w Deep Learning and Computer Vision. I have seen a lot of people using these two terms interchangeably.

Artificial intelligence and machine learning promise to radically transform many industries, but they also pose significant risks -- many of which are yet to be discovered, given that the technology is only now beginning to be rolled out in force. There have already been a number of public, and embarrassing, examples of AI gone bad. Microsoft's Tay went from innocent chatbot to a crazed racist in just a day, corrupted by Twitter trolls. Two years ago, Google had to censor image searches for keywords like "gorilla" and "chimp" because it returned photos of African-Americans -- and the problem still hasn't been fully fixed in its Google Photos app. As businesses increasingly embrace AI, the stakes will only get higher.

It will also pull "view image" links for pictures to reduce the number of direct downloads. Google has long had an option to filter photos by licensing rights, but that only helps if you already intend to honor image permissions. The cost of the deal isn't known. It's no surprise that a deal exists at all, mind you. Google already has its plate full with EU matters, including a shopping-related antitrust fine and tax disputes.

Instance-level retrieval has become an essential paradigm to index and retrieves images from large-scale databases. Conventional instance search requires at least an example of the query image to retrieve images that contain the same object instance. Existing semantic retrieval can only search semantically-related images, such as those sharing the same category or a set of tags, not the exact instances. Meanwhile, the unrealistic assumption is that all categories or tags are known beforehand. Training models for these semantic concepts highly rely on instance-level attributes or human captions which are expensive to acquire. Given the above challenges, this paper studies the Zero-shot Retrieval problem that aims for instance-level image search using only a few dominant attributes. The contributions are: 1) we utilise automatic word embedding to infer class-level attributes to circumvent expensive human labelling; 2) the inferred class-attributes can be extended into discriminative instance attributes through our proposed Latent Instance Attributes Discovery (LIAD) algorithm; 3) our method is not restricted to complete attribute signatures, query of dominant attributes can also be dealt with. On two benchmarks, CUB and SUN, extensive experiments demonstrate that our method can achieve promising performance for the problem. Moreover, our approach can also benefit conventional ZSL tasks.

Recognizing multiple labels of images is a fundamental but challenging task in computer vision, and remarkable progress has been attained by localizing semantic-aware image regions and predicting their labels with deep convolutional neural networks. The step of hypothesis regions (region proposals) localization in these existing multi-label image recognition pipelines, however, usually takes redundant computation cost, e.g., generating hundreds of meaningless proposals with non-discriminative information and extracting their features, and the spatial contextual dependency modeling among the localized regions are often ignored or over-simplified. To resolve these issues, this paper proposes a recurrent attention reinforcement learning framework to iteratively discover a sequence of attentional and informative regions that are related to different semantic objects and further predict label scores conditioned on these regions. Besides, our method explicitly models long-term dependencies among these attentional regions that help to capture semantic label co-occurrence and thus facilitate multi-label recognition. Extensive experiments and comparisons on two large-scale benchmarks (i.e., PASCAL VOC and MS-COCO) show that our model achieves superior performance over existing state-of-the-art methods in both performance and efficiency as well as explicitly identifying image-level semantic labels to specific object regions.

2D/3D image registration to align a 3D volume and 2D X-ray images is a challenging problem due to its ill-posed nature and various artifacts presented in 2D X-ray images. In this paper, we propose a multi-agent system with an auto attention mechanism for robust and efficient 2D/3D image registration. Specifically, an individual agent is trained with dilated Fully Convolutional Network (FCN) to perform registration in a Markov Decision Process (MDP) by observing a local region, and the final action is then taken based on the proposals from multiple agents and weighted by their corresponding confidence levels. The contributions of this paper are threefold. First, we formulate 2D/3D registration as a MDP with observations, actions, and rewards properly defined with respect to X-ray imaging systems. Second, to handle various artifacts in 2D X-ray images, multiple local agents are employed efficiently via FCN-based structures, and an auto attention mechanism is proposed to favor the proposals from regions with more reliable visual cues. Third, a dilated FCN-based training mechanism is proposed to significantly reduce the Degree of Freedom in the simulation of registration environment, and drastically improve training efficiency by an order of magnitude compared to standard CNN-based training method. We demonstrate that the proposed method achieves high robustness on both spine cone beam Computed Tomography data with a low signal-to-noise ratio and data from minimally invasive spine surgery where severe image artifacts and occlusions are presented due to metal screws and guide wires, outperforming other state-of-the-art methods (single agent-based and optimization-based) by a large margin.