SCA-CNN is a new kind of convolutional neural network that is designed specifically for image captioning. It uses a combination of spatial and channel-wise attention-based mechanisms to help the model better understand which parts of the image to focus on during sentence generation.
SCA-CNN and Image Captioning
Image captioning is a challenging task that involves generating natural language descriptions of images, and requires an understanding of both visual and linguistic cues. SCA-CNN was d
Spatial & Temporal Attention: The Science of Adaptive Region and Time Selection
Spatial and temporal attention are two cognitive processes that humans use to process visual information. Spatial attention refers to the ability to focus on specific regions of space, while temporal attention is the ability to attend to specific moments in time. Spatial & temporal attention combines both of these advantages to adaptively select both important regions and key frames. This technique has been used in
Spatial Transformer Networks (STN) are a type of neural network that focus on important regions in images by learning invariance to different types of transformations, such as translation, scaling, and rotation. By explicitly predicting and paying attention to these regions, STNs provide a deep neural network with the necessary transformation invariance.
What is an Affine Transformation?
To understand how STNs work, we must first take a look at affine transformations. An affine transformation
In human action recognition, each type of action generally only depends on a few specific kinematic joints. Furthermore, over time, multiple actions may be performed. To address these observations, Song et al. proposed a joint spatial and temporal attention network based on LSTM, called STA-LSTM, to adaptively find discriminative features and keyframes. This network combines a spatial attention sub-network and a temporal attention sub-network to select important regions and key frames.
What is
Channel attention is a technique used in deep learning and neural networks to help improve their ability to recognize and understand images. This technique was pioneered by SENet, which is a neural network architecture that uses squeeze-and-excitation (SE) blocks to gather global information, capture channel-wise relationships, and improve representation ability.
What is SENet and How Does It Work?
SENet stands for Squeeze-and-Excitation Network and it is a neural network architecture that wa
The field of computer vision has come a long way in recent years, thanks to advancements in machine learning and the development of convolutional neural networks (CNNs). While CNNs have proven effective in a variety of image-based tasks, they are not without limitations. One such limitation concerns spatial pooling, which typically operates on a small region as opposed to being capable of capturing long-range dependencies. In order to address this issue, researchers have proposed a new pooling m
What is a Style-Based Recalibration Module (SRM)?
A Style-based Recalibration Module (SRM) is a unique module that uses a convolutional neural network to recalibrate intermediate feature maps, improving the representational ability of a CNN.
By analyzing the styles in the feature maps, SRM is able to adjust its weights and either emphasize or suppress information, helping the neural network better understand the data it is processing.
How does SRM work?
The SRM model consists of two main co
TAM: A Lightweight Method for Capturing Complex Temporal Relationships in Videos
If you're familiar with computer vision, you may already know that temporal modeling in videos is essential for recognizing complex actions, detecting anomalies, and tracking objects from frame to frame. However, doing so accurately and efficiently can be challenging. This is where Temporal Adaptive Modules (TAM) come in.
TAM is a lightweight method designed to capture complex temporal relationships efficiently an
What is Temporal Attention?
Temporal attention is a mechanism in our brain where we select and pay attention to things happening at specific moments in time. It's a way for us to process information efficiently and navigate through our environment with ease. Technically speaking, it's the ability to selectively process information at specific points in time.
Temporal attention can be seen as an important component of both visual and audio information processing. For example, when watching a vi