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1. On the correlation between second order texture features and human observer detection performance in digital images

   https://doi.org/10.1038/s41598-020-69816-z  

   Nisbett, William H. ; Kavuri, Amar ; Das, Mini ( August 2020 , Scientific Reports)

   Image texture, the relative spatial arrangement of intensity values in an image, encodes valuable information about the scene. As it stands, much of this potential information remains untapped. Understanding how to decipher textural details would afford another method of extracting knowledge of the physical world from images. In this work, we attempt to bridge the gap in research between quantitative texture analysis and the visual perception of textures. The impact of changes in image texture on human observer’s ability to perform signal detection and localization tasks in complex digital images is not understood. We examine this critical question by studying task-based human observer performance in detecting and localizing signals in tomographic breast images. We have also investigated how these changes impact the formation of second-order image texture. We used digital breast tomosynthesis (DBT) an FDA approved tomographic X-ray breast imaging method as the modality of choice to show our preliminary results. Our human observer studies involve localization ROC (LROC) studies for low contrast mass detection in DBT. Simulated images are used as they offer the benefit of known ground truth. Our results prove that changes in system geometry or processing leads to changes in image texture magnitudes. We show that the variations in several well-known texture features estimated in digital images correlate with human observer detection–localization performance for signals embedded in them. This insight can allow efficient and practical techniques to identify the best imaging system design and algorithms or filtering tools by examining the changes in these texture features. This concept linking texture feature estimates and task based image quality assessment can be extended to several other imaging modalities and applications as well. It can also offer feedback in system and algorithm designs with a goal to improve perceptual benefits. Broader impact can be in wide array of areas including imaging system design, image processing, data science, machine learning, computer vision, perceptual and vision science. Our results also point to the caution that must be exercised in using these texture features as image-based radiomic features or as predictive markers for risk assessment as they are sensitive to system or image processing changes.

    

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2. FIXMYPOSE: Pose Correctional Captioning and Retrieval

   Kim, Hyounghun ; Zala, Abhay ; Burri, Graham ; Bansal, Mohit ( February 2021 , Proceedings of the AAAI Conference on Artificial Intelligence)

   null (Ed.)

   Interest in physical therapy and individual exercises such as yoga/dance has increased alongside the well-being trend, and people globally enjoy such exercises at home/office via video streaming platforms. However, such exercises are hard to follow without expert guidance. Even if experts can help, it is almost impossible to give personalized feedback to every trainee remotely. Thus, automated pose correction systems are required more than ever, and we introduce a new captioning dataset named FixMyPose to address this need. We collect natural language descriptions of correcting a “current” pose to look like a “target” pose. To support a multilingual setup, we collect descriptions in both English and Hindi. The collected descriptions have interesting linguistic properties such as egocentric relations to the environment objects, analogous references, etc., requiring an understanding of spatial relations and commonsense knowledge about postures. Further, to avoid ML biases, we maintain a balance across characters with diverse demographics, who perform a variety of movements in several interior environments (e.g., homes, offices). From our FixMyPose dataset, we introduce two tasks: the pose-correctional-captioning task and its reverse, the target-pose-retrieval task. During the correctional-captioning task, models must generate the descriptions of how to move from the current to the target pose image, whereas in the retrieval task, models should select the correct target pose given the initial pose and the correctional description. We present strong cross-attention baseline models (uni/multimodal, RL, multilingual) and also show that our baselines are competitive with other models when evaluated on other image-difference datasets. We also propose new task-specific metrics (object-match, body-part-match, direction-match) and conduct human evaluation for more reliable evaluation, and we demonstrate a large human-model performance gap suggesting room for promising future work. Finally, to verify the sim-to-real transfer of our FixMyPose dataset, we collect a set of real images and show promising performance on these images. Data and code are available: https://fixmypose-unc.github.io. 

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3. Improving information extraction from visually rich documents using visual span representations

   https://doi.org/10.14778/3446095.3446104  

   Sarkhel, Ritesh ; Nandi, Arnab ( January 2021 , Proceedings of the VLDB Endowment)

   null (Ed.)

   Along with textual content, visual features play an essential role in the semantics of visually rich documents. Information extraction (IE) tasks perform poorly on these documents if these visual cues are not taken into account. In this paper, we present Artemis - a visually aware, machine-learning-based IE method for heterogeneous visually rich documents. Artemis represents a visual span in a document by jointly encoding its visual and textual context for IE tasks. Our main contribution is two-fold. First, we develop a deep-learning model that identifies the local context boundary of a visual span with minimal human-labeling. Second, we describe a deep neural network that encodes the multimodal context of a visual span into a fixed-length vector by taking its textual and layout-specific features into account. It identifies the visual span(s) containing a named entity by leveraging this learned representation followed by an inference task. We evaluate Artemis on four heterogeneous datasets from different domains over a suite of information extraction tasks. Results show that it outperforms state-of-the-art text-based methods by up to 17 points in F1-score. 

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4. MUTEX: Learning Unified Policies from Multimodal Task Specifications

   Shah, Rutav ; Martín-Martín, Roberto ; Zhu, Yuke ( November 2023 , Conference on Robot Learning)

   Humans use different modalities, such as speech, text, images, videos, etc., to communicate their intent and goals with teammates. For robots to become better assistants, we aim to endow them with the ability to follow instructions and understand tasks specified by their human partners. Most robotic policy learning methods have focused on one single modality of task specification while ignoring the rich cross-modal information. We present MUTEX, a unified approach to policy learning from multimodal task specifications. It trains a transformer-based architecture to facilitate cross-modal reasoning, combining masked modeling and cross-modal matching objectives in a two-stage training procedure. After training, MUTEX can follow a task specification in any of the six learned modalities (video demonstrations, goal images, text goal descriptions, text instructions, speech goal descriptions, and speech instructions) or a combination of them. We systematically evaluate the benefits of MUTEX in a newly designed dataset with 100 tasks in simulation and 50 tasks in the real world, annotated with multiple instances of task specifications in different modalities, and observe improved performance over methods trained specifically for any single modality. 

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5. Multimodal Alignment for Affective Content

   Haduong, Nikita ; Nester, David ; Vaidyanathan, Preethi ; Prud'hommeaux, Emily ; Bailey, Reynold ; Alm, Cecilia ( January 2018 , AAAI Workshop on Affective Content Analysis)

   Humans routinely extract important information from images and videos, relying on their gaze. In contrast, computational systems still have difficulty annotating important visual information in a human-like manner, in part because human gaze is often not included in the modeling process. Human input is also particularly relevant for processing and interpreting affective visual information. To address this challenge, we captured human gaze, spoken language, and facial expressions simultaneously in an experiment with visual stimuli characterized by subjective and affective content. Observers described the content of complex emotional images and videos depicting positive and negative scenarios and also their feelings about the imagery being viewed. We explore patterns of these modalities, for example by comparing the affective nature of participant-elicited linguistic tokens with image valence. Additionally, we expand a framework for generating automatic alignments between the gaze and spoken language modalities for visual annotation of images. Multimodal alignment is challenging due to their varying temporal offset. We explore alignment robustness when images have affective content and whether image valence influences alignment results. We also study if word frequency-based filtering impacts results, with both the unfiltered and filtered scenarios performing better than baseline comparisons, and with filtering resulting in a substantial decrease in alignment error rate. We provide visualizations of the resulting annotations from multimodal alignment. This work has implications for areas such as image understanding, media accessibility, and multimodal data fusion. 

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