Best photo caption maker robot
The soundtrack album and background score were composed by A. Rahman while the dialogues, cinematography, editing and art direction were handled by Madhan KarkyR. RathnaveluAnthony and Sabu Cyril respectively.
The story revolves around the struggle of scientist Vaseegaran played by Rajinikanth to control his creation, an android named Chitti also played by Rajinikanthafter Chitti's software is upgraded to give it the ability to comprehend best photo caption maker robot exhibit human emotions. The project backfires when the robot falls in love with the scientist's girlfriend Raiand is manipulated by Bohra Denzongpaa best photo caption maker robot scientist, into becoming homicidal.
After being stalled in the development phase for nearly a decade, the film's principal photography began in and lasted two years. The film marked the debut of Legacy Effects studio which was responsible for the film's prosthetic make-up and animatronics in Indian cinema.
Robot in Hindi and Robo in Telugu. Produced by Kalanithi Maranit was India's most expensive film up to that point. The film received generally positive reviews upon release. Critics were particularly appreciative of Rajinikanth's performance, Rathnavelu's cinematography, Cyril's art direction and the visual effects by V. Enthiran emerged as the top-earning Indian film of and is the fourth-highest-grossing South Indian film of all time.
A spiritual successortitled 2. After a decade of research, the scientist Vaseegaran creates a sophisticated android robot with the help of his assistants, Siva and Ravi, to commission it into the Indian Army. He introduces the robot, named Chitti, at a robotics conference in Chennai. Chitti helps Sana, Vaseegaran's medical student girlfriend, cheat in her examination, then saves her from best photo caption maker robot assaulted by a group of thugs.
Vaseegaran's mentor, Professor Bohra, is secretly engaged in a project to create best photo caption maker robot android robots for a terrorist organisation, but has so far been unsuccessful. During the evaluation, Chitti attempts to stab Vaseegaran at Bohra's command, which convinces the evaluation committee that the robot is a liability and cannot be used for military purposes. Vaseegaran's effort to prove Bohra wrong fails when he deploys Chitti to rescue people from a burning building.
The robot saves most of them, including a girl named Selvi who was bathing at the time, but she is ashamed at being seen naked on camera and flees, only to be hit and killed by a truck. Vaseegaran asks for one month to modify Chitti's neural schema to enable it to understand human behaviour and best photo caption maker robot, to which Bohra agrees. While best photo caption maker robot the deadline, Chitti becomes angry with Vaseegaran, demonstrating to him that it can manifest emotions.
Chitti uses Sana's textbooks to successfully help Sana's sister Latha give birth best photo caption maker robot a child. Chitti develops romantic feelings for Sana after she congratulates Chitti by kissing it. When Vaseegaran and Sana realise best photo caption maker robot, Sana explains to Chitti that they are only friends. Saddened by Sana's rejection, yet still in love with her, Chitti deliberately fails an evaluation conducted by the Indian Army.
Enraged, Vaseegaran chops Chitti into pieces, which are dumped by Siva and Ravi into a landfill site. Bohra visits the site to retrieve Chitti, which has now reassembled itself, albeit in a damaged state. Bohra best photo caption maker robot a red chip inside Chitti while reconstructing it, converting it into a ruthless killer. It then gatecrashes Vaseegaran and Sana's wedding, kidnaps Sana, creates replicas of itself and kills Bohra. After informing Sana that it has acquired the human ability to reproduce, Chitti wishes to marry her so that a machine and a human being can give birth to a preprogrammed child, but Sana refuses.
It eventually finds Vaseegaran, who entered AIRD to stop it, and nearly kills him before the police appear. The ensuing battle between Chitti's robot army and the police personnel leads to many casualties and best photo caption maker robot property destruction. Vaseegaran eventually captures Chitti using a magnetic wall and accesses its internal control panel, whereby he instructs all the other robots to self-destruct.
He removes Chitti's red chip, calming it. In a court hearing, Vaseegaran is sentenced to death for the casualties and damages caused by the robot army, but Chitti explains that it was Bohra who caused its deviant behaviour and shows the court video footage best photo caption maker robot Bohra installing the red chip. The court releases Vaseegaran, while ordering that Chitti be dismantled. Left with no choice, Vaseegaran asks Chitti to dismantle itself.
While saying goodbye, Chitti apologises to Vaseegaran and Sana before dismantling itself. The film's setting then shifts to Chitti is now a museum exhibit. A curious school student on excursion asks her guide why it was dismantled, to which Chitti responds, " Naan sinthikka arambichen " I started thinking.
Following the completion of his first directorial venture in Hindi, NayakS. After BoysShankar began work on his next feature starring Vikramwhich was initially reported by Rediff. Khan was about to produce it under his own banner, Red Chillies Entertainmentbut in October the same year the project was officially aborted due to creative differences between the two. Rajinikanth was impressed with two of the scripts and agreed to star in the films, which became Sivaji and Enthiran.
The third script narrated by Shankar focused on an aspiring bodybuilder; it eventually became I Although Aishwarya Rai was Shankar's original choice for the female lead inshe declined it owing to a busy schedule and was replaced by Zinta. Chakravarthy[33] Sathyaraj and British actor Ben Kingsley[34] [35] but it was Danny Denzongpa who eventually received it, making Enthiran his first film in Tamil. Vijay and Madhan Karky authored the lyrics for the songs.
Bharathirajawas signed on to be an assistant director after he approached Shankar. Rathnavelu was hired as the cinematographer after Ravi K. Chandran[45] Nirav Shah and Thiru were considered. Manish Malhotra and Mary E. Vogt were chosen to design the film's costumes. The visual appearance of Chitti was based on the G. For Chitti's "villain robot" look, its hair was best photo caption maker robot and brown coloured lenses were used for its eyes, whereas for its "good robot" look, green coloured lenses were used.
For Sabu Cyril's sets, Shankar required approximately twice as much studio floor space as for his previous film. Best photo caption maker robot rejecting Ramoji Film City for technical reasons, Enthiran 's producer, Kalanithi Marantook six months to set up three air-conditioned studio floors on land in Perungudi owned by Sun TV Network.
Lines car carrier, Neptune Ace. Impressed with the film's script, V. He asked Shankar to increase the filming schedules by six months to include pre-production requirements.
Sanath of Firefly Creative Studios, a visual effects company based in Hyderabad. Rathnavelu used the Xtreme camera and also wrote a 1,page manual, in which best photo caption maker robot listed all of the possible angles from which the characters played by Rajinikanth could be filmed.
For every robotic mannequin used, six puppeteers were employed to control the mannequin's movements. Enthiran focuses on the battle between man and machine.
Moti Gokulsing and Wimal Dissanayake, in their book Routledge Handbook of Indian Cinemasnoted the similarity between the two works, arguing that Chitti was "manipulated by Bohra to become a Frankenstein-like figure". Director and film critic Sudhish Kamath called Enthiran "a superhero film, a sci-fi adventure, a triangular love story with a hint of the Ramayana ", while remarking that Enthiran 's similarities to The Terminator were "more than obvious.
Not just visually—where we see the Superstar with one human eye and one scarred metallic eye but also intentionally spelt out when the bad robot announces that he has created Terminators.
Although Shankar initially claimed that Enthiran would be made for all audiences, including those lacking computer literacy[91] the film is influenced by and makes references to many scientific principles relating to the fields of engineering, computer science and robotics, including terabytes and Asimov's laws of robotics.
For Enthiran' s soundtrack and score, A. Rahman made use of the Continuum Fingerboardan instrument he had experimented with previously in the song "Rehna Tu" from Rakeysh Omprakash Mehra 's drama film Delhi-6 Where the collection does manage to veer from the usual, Rahman has managed to add his own quirky, creative notes to the songs. They may suit the script of the sci-film, but the audio is not impressive. Advance bookings for the film began two weeks before the release date in the Best photo caption maker robot States.
In the Jackson Heights neighbourhood in New York, tickets were sold out within ten minutes of going on sale. Rajamouli 's two-part historical fiction films Baahubali 2: The Conclusion and Baahubali: The Beginning[] [] and Pa. Ranjith 's gangster drama Kabali Enthiran received positive reviews from critics in India, with praise particularly directed at Rathnavelu's cinematography, Cyril's art direction, Srinivas Mohan's visual effects and Rajinikanth's performance as Chitti.
Kazmi called it "the perfect getaway film". Chopra criticised the film's portions in the second half, describing them as "needlessly stretched and cacophonous", [] but concluded her review by saying, " Robot rides on Rajinikanth's shoulders and he never stoops under the burden. Aided by snazzy clothes, make-up and special effects, he makes Chitti endearing.
This film, just a few feet too long, is fine entertainment by itself. Malini Mannath of The New Indian Express noted Enthiran for having "An engaging script, brilliant special effects, and a debonair hero who still carries his charisma effortlessly.
Club believed that Enthiran was "pretty good" and concluded that "if you prefer elaborate costumes and dance music mixed in with your killer-robot action, expect to enjoy up to an hour of Enthiran. In a personal appreciation letter to Shankar following the film's release, the director K.
Scenes from Enthiranparticularly one known as the "Black Sheep" scene, [Note 10] have been parodied in subsequent films, including Mankatha[] [] Osthe[] Singam II[] as well as in the Telugu films Dookudu and Nuvva Best photo caption maker robot On Rajinikanth's 64th birthday, an agency named Minimal Kollywood Posters designed posters of Rajinikanth's films, in which the Minion characters from the Despicable Me franchise are dressed as Rajinikanth.
In Septemberwriter Jeyamohan announced that the pre-production stage of a sequel to Enthiran was "going on in full swing" and that principal photography would commence once Rajinikanth finished filming for Kabaliby the end of that year. Rahman would return as music director, while Muthuraj would handle best photo caption maker robot art direction. The sequel would be shot in 3D, unlike best photo caption maker robot predecessor which was shot in 2D and converted to 3D in post-production.
From Wikipedia, the free encyclopedia. Enthiran Theatrical release poster. I thought that playing Chitti the robot would be very difficult. He is a machine. His movements should not be like a human being's. We had to draw a line.
Image caption generation models combine recent advances in computer vision and machine translation to produce realistic image captions using neural networks. Neural image caption models are trained to maximize the likelihood of producing a caption given an input image, and can be used to generate novel image descriptions. For example, the following are possible captions generated using a neural image caption generator trained on the MS COCO data set.
Recent successes in applying deep neural networks to computer vision and natural language best photo caption maker robot tasks have inspired AI researchers to explore new research opportunities at the intersection of these previously separate domains. Caption generation models have to balance an understanding of both visual cues and natural language. The intersection of these two traditionally unrelated fields has the possibility to effect change on a best photo caption maker robot scale.
While there are some straightforward applications of this technology, such as generating summaries for YouTube videos or captioning unlabeled images, more creative applications can drastically improve the quality of life for a wide cross section of the population. Similar to how traditional computer vision seeks to make the world more accessible and understandable for computers, this technology has the potential to make our best photo caption maker robot more accessible and understandable for us humans.
It can best photo caption maker robot as a tour guide, and can even serve as a visual aid for daily life, such as in the case of the Horus wearable device from the Italian AI firm Eyra. First, you will need to install Tensorflow. If this is your first time working with Tensorflow, we recommend that you first review the following article: Building and training your first TensorFlow model. You will need the pandas, opencv2, and Jupyter libraries to run the associated code.
However, to simplify the install process we highly recommend that you follow the Docker install instructions on our associated GitHub repo. You will also need to download the image embeddings and image captions for the Flickr30k data set.
Download links are also provided on our GitHub repo. At a high-level, this is the model we will be training. Each image will be encoded by a deep convolutional neural network into a 4, dimensional vector representation. A language generating RNN, or recurrent neural network, will then decode that representation sequentially into a natural language description. Image classification is a computer vision task with a lot of history and many strong models behind it. Classification requires models that can piece together relevant visual information about the shapes and objects present in an image, to place that image into an object category.
Machine learning models for other computer vision tasks such as object detection and image segmentation build on this by not only recognizing when information is present, but also by learning how to interpret 2D space, reconcile the two understandings, and determine where an object's information is distributed in the image. For caption generation, this raises two questions:.
We can take advantage of pre-existing models to help caption images. Transfer learning allows us to take the data transformations learned by neural networks trained on different tasks and apply them to our data. In our case, the VGG image best photo caption maker robot model takes in x pixel images and produces a 4, dimensional feature vector useful for categorizing images.
We can take the representation known as the image embedding from the VGG model and use it to train the rest of our model. For the scope of this article, we have abstracted away the architecture of VGG and have pre-computed the 4, dimensional features to speed up training.
Now that we have an image representation, we need our model to learn to decode that representation into an understandable caption. These networks are trained to predict the next word in a series given previous words and the image representation. Long short-term memory LSTM cells allow the model to better select what information to use in the sequence of caption words, what to remember, and what information to forget. TensorFlow provides a wrapper function to generate an LSTM layer for a given input and output dimension.
To transform words into a fixed-length representation suitable for LSTM input, we use an embedding layer that learns to map words to dimensional features or word-embeddings. Word-embeddings help us represent our words as vectors, where similar word-vectors are semantically similar. To learn more about how word-embeddings capture the relationships between different words, check out " Capturing semantic meaning using deep learning.
In the VGG image classifier, the convolutional layers extract a 4, dimensional representation to pass through a final softmax layer for classification. Because the LSTM cells expect dimensional textual features as input, we need to translate the image representation into the representation used for the target captions. To do this, we utilize another embedding layer that learns to map the 4, dimensional image features into the space of dimensional textual best photo caption maker robot.
The model is trained to minimize the negative best photo caption maker robot of the log probabilities of each word. After training, we have a model that gives the probability of a word appearing next in a caption, given the image and all previous words.
How can we use this to generate new captions? The simplest approach is to take an input image best photo caption maker robot iteratively output the next most probable word, building up a single caption. In many cases this works, but best photo caption maker robot "greedily" taking the most probable words, we may not end up with the most probable caption overall. One possible way to circumvent this is by using a method called " Beam Search. This allows one to explore a larger space of good captions while keeping inference computationally tractable.
The neural image caption generator gives a useful framework for learning to map from images to human-level image captions. By training on large numbers of image-caption pairs, the model learns best photo caption maker robot capture relevant semantic information from visual features.
However, with a static image, embedding our caption generator will focus on features of our images useful for image classification and best photo caption maker robot necessarily features useful for caption generation. To improve the amount of task-relevant information contained in each feature, we can train the image embedding model the VGG network used to encode features as a piece of the caption generation model, allowing us to fine-tune the image encoder to better fit the role of generating captions.
Also, if we actually look closely at the captions generated, we notice that they are rather mundane and commonplace. Take this possible image-caption pair for instance:. This is most certainly a "giraffe standing next to a tree. First, if you want to improve on the model explained here, take a look at Google's open source Show and Tell networktrainable with the MS COCO data set and an Inception-v3 image embedding.
Current state-of-the-art image captioning models include a visual attention mechanism, which allows the model to identify areas of interest in the image to selectively focus on when generating captions. Also, if you are interested in this state-of-the-art implementation of caption generation, check out the following paper: Show, Attend, and Tell: This post is a collaboration between O'Reilly and TensorFlow.
See our statement of editorial independence. Raul has contributed to research projects in several fields including but not limited to: However, the bulk of his research work is focused on Machine Learning and Machine Learning Systems with applications to security, anomaly detection, NLP, and computer vision and robotics.
Raul is also passionate about giving back to the community by teaching applied ML concepts and is a teaching assista Dan Ricciardelli is an undergraduate researcher at the University of California, Berkeley.
Dan is excited about making machine learning more accessible to technical and non-technical students and professionals alongside Machine Learning at Berkeley. The image caption generation model. Shannon Shih from Machine Learning at Berkeley. The image caption generation model Figure 2.
Caption generation as an extension of image classification Image classification is a computer vision task with a lot of history and many strong models behind it. For caption generation, this raises two questions: How can we build upon the success of image classification models, in retrieving important information from images? How can our model learn to reconcile an understanding of language, with an understanding of images? Leveraging transfer learning We can take advantage of pre-existing models to help caption images.
Loading the VGG image features and image best photo caption maker robot is relatively straightforward: Building and training the model All together, this is what the Show and Tell Model looks like: Limitations and discussion The neural image caption generator gives a useful framework for learning to map from images to human-level image captions.
Take this possible image-caption pair for instance: Next steps First, if you want to improve on the model explained here, take a look at Google's open source Show and Tell networktrainable with the MS COCO best photo caption maker robot set and an Inception-v3 image embedding.
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