Best photo caption maker robot

For Sabu Cyril's sets, Shankar required approximately twice as much studio floor space as for his previous film. After rejecting Ramoji Film City for technical reasons, Enthiran 's producer, Kalanithi Maran , took 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 he 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 Cinemas , noted 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 Fingerboard , an 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 United 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 Enthiran , particularly 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 Nena 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 September , writer 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 Kabali , by the end of that year. Rahman would return as music director, while Muthuraj would handle the art direction. The sequel would be shot in 3D, unlike its 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. If I deviated even slightly, Shankar would point it out and say I was being too human. After four to five days of shooting, we found a rhythm.

The residential section of the Incan city of Machu Picchu, which features in the song, "Kilimanjaro". List of accolades received by Enthiran. It developed as a result of shaping Rajinikanth's already grown beard. The Light Stage systems efficiently capture how an actor's face appears when lit from every possible lighting direction.

From this captured imagery, realistic virtual renditions of the actor are created in the illumination of any location or set, faithfully reproducing the colour, texture, shine, shading and translucency of the actor's skin. Vaseegaran disguised as him amidst the robot army. Archived from the original on 22 January Retrieved 22 January British Board of Film Classification. Retrieved 14 October Press Trust of India. Archived from the original on 4 February Retrieved 4 February Opening credits from Archived from the original on 5 October Retrieved 8 October Closing credits in 2: The Times of India.

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Retrieved 21 January Ramakrishnan, Deepa; Lakshmi, K. Archived from the original on 9 June Retrieved 9 June Krithika 29 November Archived from the original on 23 January Retrieved 23 January Retrieved 26 January Archived from the original on 27 January Retrieved 27 January Tilak, Sudha 10 October Moti; Dissanayake, Wimal Routledge Handbook of Indian Cinemas.

Retrieved 1 August Retrieved 28 January Archived from the original on 28 January Archived from the original on 13 April Retrieved 13 April Archived from the original on 16 March Retrieved 16 March Archived from the original on 24 January Retrieved 24 January Retrieved 30 July Archived from the original on 11 February Retrieved 11 February Retrieved 26 October The costliest film in India".

Archived from the original on 3 February Retrieved 11 August 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 features.

The model is trained to minimize the negative sum 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 and iteratively output the next most probable word, building up a single caption. In many cases this works, but by "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 to 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 not 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 network , trainable 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: