Because it only takes about ms for the brain to recognize an object quite accurately, it was unclear if these recurrent interconnections in the brain had any role at all in core object recognition. Perhaps those recurrent connections are only in place to keep the visual system in tune over long periods of time.
For example, the return gutters of the streets help slowly clear it of water and trash, but are not strictly needed to quickly move people from one end of town to the other. DiCarlo, along with lead author and CBMM postdoc Kohitij Kar, set out to test whether a subtle role of recurrent operations in rapid visual object recognition was being overlooked. The authors first needed to identify objects that are trivially decoded by the primate brain, but are challenging for artificial systems. Rather than trying to guess why deep learning was having problems recognizing an object is it due to clutter in the image?
Humans trying to guess why AI models were challenged turned out to be holding us back. Instead, the authors presented the deep learning system, as well as monkeys and humans, with images, homing in on "challenge images" where the primates could easily recognize the objects in those images, but a feedforward DCNN ran into problems. When they, and others, added appropriate recurrent processing to these DCNNs, object recognition in challenge images suddenly became a breeze. Kar used neural recording methods with very high spatial and temporal precision to determine whether these images were really so trivial for primates.
Diane Beck, professor of psychology and co-chair of the Intelligent Systems Theme at the Beckman Institute and not an author on the study, explains further. This study shows that, yes, feedback connections are very likely playing a role in object recognition after all. What does this mean for a self-driving car?
It shows that deep learning architectures involved in object recognition need recurrent components if they are to match the primate brain, and also indicates how to operationalize this procedure for the next generation of intelligent machines. Perhaps one day, the systems will not only recognize an object, such as a person, but also perform cognitive tasks that the human brain so easily manages, such as understanding the emotions of other people. MIT News Office. Browse or. Browse Most Popular. What a little more computing power can do Why did my classifier just mistake a turtle for a rifle?
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In particular, we will be looking at applications such as network compression, fine-grained image classification, captioning, texture synthesis, image search, and object tracking. Texture synthesis is used to generate a larger image containing the same texture.
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Given a normal image and an image that contains a specific style, then style transform not only retains the original contents of the image but transforms that image into the specified style. Feature inversion is the core concept behind texture synthesis and style transform. Given a middle layer feature, we hope to user iteration to create a feature and an image similar to the given feature.
Feature inversion can tell us how much image information gets contained in a middle layer feature.
Through the outer product, the Gram matrix captures the relationships between different features. It performs feature inversion on the gram matrix of a given texture image and makes the Gram matrix of each of the image's features similar to the Gram for each layer of the given texture image. The low layer features will tend toward capturing detailed information, while the high layer features can capture features across a larger area. There are two primary objectives to this optimization.
The first is to bring the contents of the generated image closer to that of the original image, while the second is make the style of the generated image match the specified style. The style is embodied by the Gram matrix, while the content is represented directly by the activation values of the neurons. A failing of the method outlined above is that we can only reach convergence after multiple iterations. The solution offered by related work is to train a neural network to create a style transformed image directly. Once training ends, the style transform only requires a single iteration through a feed-forward network, which is very useful.
During training, we take the generated image, the original image, and the style transformed image and feed them into a set network to extract features from different layers and calculate the loss algorithm. When instance normalization and batch normalization act on a batch, the image itself can determine the mean and variance of the sample normalization.
Experiments demonstrate that by using instance normalization, a style transform network can remove the comparative information related to the image to simplify the generation process.
Neural Networks for Image Recognition: Methods, Best Practices, Applications
One problem with the method described above is that we have to train a separate model for each different style. Since different styles sometimes contain similarities, this work can be improved by sharing parameters between style transform networks for different styles. Specifically, it changes the instance normalization for the style transform network so that it has N groups of zoom and translation parameters, each group corresponding to a specific style.
This way we can obtain N style transform images from a single feed-forward process. Face verification is where the system takes two images and determines whether or not they belong to the same person, while face recognition attempts to determine who the person in the given image is. Under typical conditions, there will only be one image for each person in the dataset, a situation called one-shot learning. As an issue of classification facing a massive number of classifications , or as an issue of metric learning.
If two images are of the same person, then we would hope that their deep features would be quite similar. Otherwise, their features should be dissimilar.
Later, verification gets applied according to the distance between the deep features setting a threshold of distance between features at which the point the images are determined to belong to different people , or recognition k nearest neighbor classification. DeepFace uses non-shared parameter locality connection.
This is because different parts of the human face have different features for example eyes and lips have different features , so the classic "shared parameters" nature of the traditional convolution layer makes it inapplicable to face verification.
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Therefore, face recognition networks use non-shared parameter locality connections. The siamese network it uses gets utilized in face verification. When the deep features of two images are smaller than the given threshold, they are considered to be of the same person. Three-factor input, where it is hoped that the distance between negative samples is larger than the distance between positive samples by a given amount ex: 0.
Selecting the most challenging group of three elements for example the farthest positive sample and closest negative sample puts that network into the most optimal situation. FaceNet uses a half-difficulty method, where it chooses negative samples that are farther away than the positive sample. This has been a hot research topic in recent years.
Since the differences within a category could be quite large, and the similarity between categories could likewise be quite high, no small amount of research has got aimed at elevating the ability of classic crossover loss to determine deep features. For example, the optimization goal of L-Softmax is to increase the angle of intersection between the parameter vectors and deep features of different categories. Practically, L-Softmax and A-Softmax are both challenging to converge, so during training, they used an annealing method to gradual anneal the standard softmax to the L-Softmax or A-Softmax.
Some methods that are currently popular in the industry is reading the changes in a person's facial expression, texture information, blinking, or requiring the user to complete a series of movements. Given an image that contains a specific instance for example a specific target, scene, or building , image searching is used to find images in the database that contain elements similar to the given instance.
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However, because the angle, lighting, and obstacles in two images are most often not the same, the issue of creating a search algorithm capable of dealing with these differences within a category of images poses a major challenge to researchers. First of all, we have to extract appropriate representative vectors from the image.
Secondly, apply Euclidean distance or Cosine distance to these vectors to perform a nearest neighbor search and find the most similar images. Finally, we use specific processing techniques to make small adjustments to the search results. We can see that the limiting factor in the performance of an image search engine is the representation of the image. Unsupervised image search uses a pre-trained ImageNet model, without outside supervision information, as the set feature extraction engine to extract representations of the image.
Supervised image search first takes the pre-trained ImageNet model and tunes it on another training dataset. Then it extracts image representations from this tuned model. To obtain a better result, the training dataset used to optimize the model is typically similar to the search dataset.
Furthermore, we can use a candidate regional network to extract a foreground region from the image that might contain the target. The objective of object tracking is to track the movements of a target in a video.
Where We See Shapes, AI Sees Textures
Normally, the target is located in the first frame of the video and marked by a box. We need to predict where the box will get located in the next frame. Object tracking is similar to target testing.