Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent, or “temporally deep”, are effective for tasks involving sequences, visual and otherwise. We develop a novel recurrent convolutional architecture suitable for large-scale visual learning which is end-to-end trainable, and demonstrate the value of these models on benchmark video recognition tasks, image description and retrieval problems, and video narration challenges. In contrast to current models which assume a fixed spatio-temporal receptive field or simple temporal averaging for sequential processing, recurrent convolutional models are “doubly deep” in that they can be compositional in spatial and temporal “layers”. Such models may have advantages when target concepts are complex and/or training data are limited. Learning long-term dependencies is possible when nonlinearities are incorporated into the network state updates. Long-term RNN models are appealing in that they directly can map variable-length inputs (e.g., video frames) to variable length outputs (e.g., natural language text) and can model complex temporal dynamics; yet they can be optimized with backpropagation. Our recurrent long-term models are directly connected to modern visual convnet models and can be jointly trained to simultaneously learn temporal dynamics and convolutional perceptual representations. Our results show such models have distinct advantages over state-of-the-art models for recognition or generation which are separately defined and/or optimized.

/pdf/long-term-recurrent-convolutional-networks-for-visual-3znyib4gwu.pdf

Long-term recurrent convolutional networks for visual recognition and description

We present a model that generates natural language descriptions of images and their regions. Our approach leverages datasets of images and their sentence descriptions to learn about the inter-modal correspondences between language and visual data. Our alignment model is based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural Networks over sentences, and a structured objective that aligns the two modalities through a multimodal embedding. We then describe a Multimodal Recurrent Neural Network architecture that uses the inferred alignments to learn to generate novel descriptions of image regions. We demonstrate that our alignment model produces state of the art results in retrieval experiments on Flickr8K, Flickr30K and MSCOCO datasets. We then show that the generated descriptions significantly outperform retrieval baselines on both full images and on a new dataset of region-level annotations.

/pdf/deep-visual-semantic-alignments-for-generating-image-511hd6wg22.pdf

Deep visual-semantic alignments for generating image descriptions

Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent, or "temporally deep", are effective for tasks involving sequences, visual and otherwise. We develop a novel recurrent convolutional architecture suitable for large-scale visual learning which is end-to-end trainable, and demonstrate the value of these models on benchmark video recognition tasks, image description and retrieval problems, and video narration challenges. In contrast to current models which assume a fixed spatio-temporal receptive field or simple temporal averaging for sequential processing, recurrent convolutional models are "doubly deep"' in that they can be compositional in spatial and temporal "layers". Such models may have advantages when target concepts are complex and/or training data are limited. Learning long-term dependencies is possible when nonlinearities are incorporated into the network state updates. Long-term RNN models are appealing in that they directly can map variable-length inputs (e.g., video frames) to variable length outputs (e.g., natural language text) and can model complex temporal dynamics; yet they can be optimized with backpropagation. Our recurrent long-term models are directly connected to modern visual convnet models and can be jointly trained to simultaneously learn temporal dynamics and convolutional perceptual representations. Our results show such models have distinct advantages over state-of-the-art models for recognition or generation which are separately defined and/or optimized.

Long-term Recurrent Convolutional Networks for Visual Recognition and Description

We present a model that generates natural language descriptions of images and their regions. Our approach leverages datasets of images and their sentence descriptions to learn about the inter-modal correspondences between language and visual data. Our alignment model is based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural Networks (RNN) over sentences, and a structured objective that aligns the two modalities through a multimodal embedding. We then describe a Multimodal Recurrent Neural Network architecture that uses the inferred alignments to learn to generate novel descriptions of image regions. We demonstrate that our alignment model produces state of the art results in retrieval experiments on Flickr8K, Flickr30K and MSCOCO datasets. We then show that the generated descriptions outperform retrieval baselines on both full images and on a new dataset of region-level annotations. Finally, we conduct large-scale analysis of our RNN language model on the Visual Genome dataset of 4.1 million captions and highlight the differences between image and region-level caption statistics.

Deep Visual-Semantic Alignments for Generating Image Descriptions

Our experience of the world is multimodal - we see objects, hear sounds, feel texture, smell odors, and taste flavors Modality refers to the way in which something happens or is experienced and a research problem is characterized as multimodal when it includes multiple such modalities In order for Artificial Intelligence to make progress in understanding the world around us, it needs to be able to interpret such multimodal signals together Multimodal machine learning aims to build models that can process and relate information from multiple modalities It is a vibrant multi-disciplinary field of increasing importance and with extraordinary potential Instead of focusing on specific multimodal applications, this paper surveys the recent advances in multimodal machine learning itself and presents them in a common taxonomy We go beyond the typical early and late fusion categorization and identify broader challenges that are faced by multimodal machine learning, namely: representation, translation, alignment, fusion, and co-learning This new taxonomy will enable researchers to better understand the state of the field and identify directions for future research

Multimodal Machine Learning: A Survey and Taxonomy

We present a system that produces sentential descriptions of video: who did what to whom, and where and how they did it. Action class is rendered as a verb, participant objects as noun phrases, properties of those objects as adjectival modifiers in those noun phrases, spatial relations between those participants as prepositional phrases, and characteristics of the event as prepositional-phrase adjuncts and adverbial modifiers. Extracting the information needed to render these linguistic entities requires an approach to event recognition that recovers object tracks, the track-to-role assignments, and changing body posture.

/pdf/video-in-sentences-out-4ex5p5klas.pdf

Video in sentences out

In this paper, we propose two new, perceptually motivated strategies to better measure the similarity of 2D shape instances that are in the form of closed contours. The first strategy handles shapes that can be decomposed into a base structure and a set of inward or outward pointing “strand” structures, where a strand structure represents a very thin, elongated shape part attached to the base structure. The similarity of two such shape contours can be better described by measuring the similarity of their base structures and strand structures in different ways. The second strategy handles shapes that exhibit good bilateral symmetry. In many cases, such shapes are invariant to a certain level of scaling transformation along their symmetry axis. In our experiments, we show that these two strategies can be integrated into available shape matching methods to improve the performance of shape classification on several widely-used shape data sets.

/pdf/two-perceptually-motivated-strategies-for-shape-30ux8ssi7h.pdf

Two perceptually motivated strategies for shape classification

Offline handwritten text recognition is a very challenging problem. Aside from the large variation of different handwriting styles, neighboring characters within a word are usually connected, and we may need to segment a word into individual characters for accurate character recognition. Many existing methods achieve text segmentation by evaluating the local stroke geometry and imposing constraints on the size of each resulting character, such as the character width, height and aspect ratio. These constraints are well suited for printed texts, but may not hold for handwritten texts. Other methods apply holistic approach by using a set of lexicons to guide and correct the segmentation and recognition. This approach may fail when the lexicon domain is insufficient. In this paper, we present a new global non-holistic method for handwritten text segmentation, which does not make any limiting assumptions on the character size and the number of characters in a word. Specifically, the proposed method finds the text segmentation with the maximum average likeliness for the resulting characters. For this purpose, we use a graph model that describes the possible locations for segmenting neighboring characters, and we then develop an average longest path algorithm to identify the globally optimal segmentation. We conduct experiments on real images of handwritten texts taken from the IAM handwriting database and compare the performance of the proposed method against an existing text segmentation algorithm that uses dynamic programming.

/pdf/handwritten-text-segmentation-using-average-longest-path-3spvx9k819.pdf

Handwritten text segmentation using average longest path algorithm

Object localization in an image is usually handled by searching for an optimal subwindow that tightly covers the object of interest. However, the subwindows considered in previous work are limited to rectangles or other specified, simple shapes. With such specified shapes, no subwindow can cover the object of interest tightly. As a result, the desired subwindow around the object of interest may not be optimal in terms of the localization objective function, and cannot be detected by a subwindow search algorithm. In this paper, we propose a new graph-theoretic approach for object localization by searching for an optimal subwindow without pre-specifying its shape. Instead, we require the resulting subwindow to be well aligned with edge pixels that are detected from the image. This requirement is quantified and integrated into the localization objective function based on the widely-used bag of visual words technique. We show that the ratio-contour graph algorithm can be adapted to find the optimal free-shape subwindow in terms of the new localization objective function. In the experiment, we test the proposed approach on the PASCAL VOC 2006 and VOC 2007 databases for localizing several categories of animals. We find that its performance is better than the previous efficient subwindow search algorithm.

/pdf/free-shape-subwindow-search-for-object-localization-48rmhcvm8d.pdf

Free-shape subwindow search for object localization

Segmentation propagation, similar to tracking, is the problem of transferring a segmentation of an image to a neighboring image in a sequence. This problem is of particular importance to materials science, where the accurate segmentation of a series of 2D serial-sectioned images of multiple, contiguous 3D structures has important applications. Such structures may have distinct shape, appearance, and topology, which can be considered to improve segmentation accuracy. For example, some materials images may have structures with a specific shape or appearance in each serial section slice, which only changes minimally from slice to slice, and some materials may exhibit specific inter-structure topology that constrains their neighboring relations. Some of these properties have been individually incorporated to segment specific materials images in prior work. In this paper, we develop a propagation framework for materials image segmentation where each propagation is formulated as an optimal labeling problem that can be efficiently solved using the graph-cut algorithm. Our framework makes three key contributions: 1) a homomorphic propagation approach, which considers the consistency of region adjacency in the propagation; 2) incorporation of shape and appearance consistency in the propagation; and 3) a local non-homomorphism strategy to handle newly appearing and disappearing substructures during this propagation. To show the effectiveness of our framework, we conduct experiments on various 3D materials images, and compare the performance against several existing image segmentation methods.

Jarrell Waggoner

Papers

Video in sentences out

Two perceptually motivated strategies for shape classification

Handwritten text segmentation using average longest path algorithm

Free-shape subwindow search for object localization

3D Materials Image Segmentation by 2D Propagation: A Graph-Cut Approach Considering Homomorphism