WiCV@CVPR2023: The Eleventh Women In Computer Vision Workshop at the Annual CVPR Conference

In this paper, we present the details of Women in Computer Vision Workshop - WiCV 2023, organized alongside the hybrid CVPR 2023 in Vancouver, Canada. WiCV aims to amplify the voices of underrepresented women in the computer vision community, fostering increased visibility in both academia and industry. We believe that such events play a vital role in addressing gender imbalances within the field. The annual WiCV@CVPR workshop offers a) opportunity for collaboration between researchers from minority groups, b) mentorship for female junior researchers, c) financial support to presenters to alleviate finanacial burdens and d) a diverse array of role models who can inspire younger researchers at the outset of their careers. In this paper, we present a comprehensive report on the workshop program, historical trends from the past WiCV@CVPR events, and a summary of statistics related to presenters, attendees, and sponsorship for the WiCV 2023 workshop

My View is the Best View: Procedure Learning from Egocentric Videos

Procedure learning involves identifying the key-steps and determining their logical order to perform a task. Existing approaches commonly use third-person videos for learning the procedure, making the manipulated object small in appearance and often occluded by the actor, leading to significant errors. In contrast, we observe that videos obtained from first-person (egocentric) wearable cameras provide an unobstructed and clear view of the action. However, procedure learning from egocentric videos is challenging because (a) the camera view undergoes extreme changes due to the wearer's head motion, and (b) the presence of unrelated frames due to the unconstrained nature of the videos. Due to this, current state-of-the-art methods' assumptions that the actions occur at approximately the same time and are of the same duration, do not hold. Instead, we propose to use the signal provided by the temporal correspondences between key-steps across videos. To this end, we present a novel self-supervised Correspond and Cut (CnC) framework for procedure learning. CnC identifies and utilizes the temporal correspondences between the key-steps across multiple videos to learn the procedure. Our experiments show that CnC outperforms the state-of-the-art on the benchmark ProceL and CrossTask datasets by 5.2% and 6.3%, respectively. Furthermore, for procedure learning using egocentric videos, we propose the EgoProceL dataset consisting of 62 hours of videos captured by 130 subjects performing 16 tasks. The source code and the dataset are available on the project page https://sid2697.github.io/egoprocel/.Comment: 25 pages, 6 figures, Accepted in European Conference on Computer Vision (ECCV) 202

Towards Human-Centered AI-Powered Assistants for the Visually Impaired

Artificial intelligence has become ubiquitous in today's society, aiding us in many everyday tasks. Given particular prowess of today's AI technologies in visual perception and speech recognition, an area where AI can have tremendous societal impact is in assistive technologies for the visually impaired. Although assisting the visually impaired for tasks such as environment navigation and item localization improves independence and autonomy, concerns over privacy arise. Taking privacy of personal data into consideration, we present the design of a human-centered AI-powered assistant for object localization for impaired vision (OLIV). OLIV integrates multi-modal perception (custom-designed visual scene understanding and speech recognition and synthesis) for the purpose of assisting the visually impaired in locating misplaced items in indoor environments. OLIV is comprised of three main components: speech recognition, custom-designed visual scene understanding, and synthesis. Speech recognition allows these individuals to independently query and interact with the system, increasing their level of independence. Visual scene understanding performs on-device object detection and depth estimation to build up a representation of the surrounding 3D scene. Synthesis then combines the detected objects along with their locations and depths with the user’s intent to construct a verbal semantic description that is verbally conveyed via speech synthesis. An important component of OLIV is scene understanding. Current state-of-the-art deep neural networks for the two tasks have been shown to achieve superior performance, but requires high computation and memory, making them cost prohibitive for on-device operation. On-device operation is necessary to address privacy concerns related to misuse of personal data. By performing on-device scene understanding, data captured by the camera will remain on the device. To address the challenge of high computation and memory requirements, two different architecture design exploration approaches, micro-architecture exploration and human-machine collaborative design strategy, are taken to design efficient neural networks with an optimal trade-off between accuracy, speed and size. Micro-architecture exploration approach resulted in a highly compact single shot network architecture for object detection. Human-machine collaborative design strategy resulted in a highly compact densely-connected encoder-decoder network architecture for monocular depth estimation. Through experiments on two indoor datasets to simulate environments OLIV operates in, the object detection network and depth estimation network were able to achieve CPU speeds of 17 FPS and 9.35 FPS, sizes of 6.99 and 3.46 million parameters, respectively, while maintaining comparable accuracy performance. Size and speed are important for on-device scene understanding on OLIV to provide a more private assistance for the visually impaired

Deep Active Learning Explored Across Diverse Label Spaces

abstract: Deep learning architectures have been widely explored in computer vision and have depicted commendable performance in a variety of applications. A fundamental challenge in training deep networks is the requirement of large amounts of labeled training data. While gathering large quantities of unlabeled data is cheap and easy, annotating the data is an expensive process in terms of time, labor and human expertise. Thus, developing algorithms that minimize the human effort in training deep models is of immense practical importance. Active learning algorithms automatically identify salient and exemplar samples from large amounts of unlabeled data and can augment maximal information to supervised learning models, thereby reducing the human annotation effort in training machine learning models. The goal of this dissertation is to fuse ideas from deep learning and active learning and design novel deep active learning algorithms. The proposed learning methodologies explore diverse label spaces to solve different computer vision applications. Three major contributions have emerged from this work; (i) a deep active framework for multi-class image classication, (ii) a deep active model with and without label correlation for multi-label image classi- cation and (iii) a deep active paradigm for regression. Extensive empirical studies on a variety of multi-class, multi-label and regression vision datasets corroborate the potential of the proposed methods for real-world applications. Additional contributions include: (i) a multimodal emotion database consisting of recordings of facial expressions, body gestures, vocal expressions and physiological signals of actors enacting various emotions, (ii) four multimodal deep belief network models and (iii) an in-depth analysis of the effect of transfer of multimodal emotion features between source and target networks on classification accuracy and training time. These related contributions help comprehend the challenges involved in training deep learning models and motivate the main goal of this dissertation.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Spatio-Temporal Multimedia Big Data Analytics Using Deep Neural Networks

With the proliferation of online services and mobile technologies, the world has stepped into a multimedia big data era, where new opportunities and challenges appear with the high diversity multimedia data together with the huge amount of social data. Nowadays, multimedia data consisting of audio, text, image, and video has grown tremendously. With such an increase in the amount of multimedia data, the main question raised is how one can analyze this high volume and variety of data in an efficient and effective way. A vast amount of research work has been done in the multimedia area, targeting different aspects of big data analytics, such as the capture, storage, indexing, mining, and retrieval of multimedia big data. However, there is insufficient research that provides a comprehensive framework for multimedia big data analytics and management. To address the major challenges in this area, a new framework is proposed based on deep neural networks for multimedia semantic concept detection with a focus on spatio-temporal information analysis and rare event detection. The proposed framework is able to discover the pattern and knowledge of multimedia data using both static deep data representation and temporal semantics. Specifically, it is designed to handle data with skewed distributions. The proposed framework includes the following components: (1) a synthetic data generation component based on simulation and adversarial networks for data augmentation and deep learning training, (2) an automatic sampling model to overcome the imbalanced data issue in multimedia data, (3) a deep representation learning model leveraging novel deep learning techniques to generate the most discriminative static features from multimedia data, (4) an automatic hyper-parameter learning component for faster training and convergence of the learning models, (5) a spatio-temporal deep learning model to analyze dynamic features from multimedia data, and finally (6) a multimodal deep learning fusion model to integrate different data modalities. The whole framework has been evaluated using various large-scale multimedia datasets that include the newly collected disaster-events video dataset and other public datasets

Multimodal Sensing and Data Processing for Speaker and Emotion Recognition using Deep Learning Models with Audio, Video and Biomedical Sensors

The focus of the thesis is on Deep Learning methods and their applications on multimodal data, with a potential to explore the associations between modalities and replace missing and corrupt ones if necessary. We have chosen two important real-world applications that need to deal with multimodal data: 1) Speaker recognition and identification; 2) Facial expression recognition and emotion detection. The first part of our work assesses the effectiveness of speech-related sensory data modalities and their combinations in speaker recognition using deep learning models. First, the role of electromyography (EMG) is highlighted as a unique biometric sensor in improving audio-visual speaker recognition or as a substitute in noisy or poorly-lit environments. Secondly, the effectiveness of deep learning is empirically confirmed through its higher robustness to all types of features in comparison to a number of commonly used baseline classifiers. Not only do deep models outperform the baseline methods, their power increases when they integrate multiple modalities, as different modalities contain information on different aspects of the data, especially between EMG and audio. Interestingly, our deep learning approach is word-independent. Plus, the EMG, audio, and visual parts of the samples from each speaker do not need to match. This increases the flexibility of our method in using multimodal data, particularly if one or more modalities are missing. With a dataset of 23 individuals speaking 22 words five times, we show that EMG can replace the audio/visual modalities, and when combined, significantly improve the accuracy of speaker recognition. The second part describes a study on automated emotion recognition using four different modalities – audio, video, electromyography (EMG), and electroencephalography (EEG). We collected a dataset by recording the 4 modalities as 12 human subjects expressed six different emotions or maintained a neutral expression. Three different aspects of emotion recognition were investigated: model selection, feature selection, and data selection. Both generative models (DBNs) and discriminative models (LSTMs) were applied to the four modalities, and from these analyses we conclude that LSTM is better for audio and video together with their corresponding sophisticated feature extractors (MFCC and CNN), whereas DBN is better for both EMG and EEG. By examining these signals at different stages (pre-speech, during-speech, and post-speech) of the current and following trials, we have found that the most effective stages for emotion recognition from EEG occur after the emotion has been expressed, suggesting that the neural signals conveying an emotion are long-lasting

WiCV 2018: The Fourth Women In Computer Vision Workshop

We present WiCV 2018 - Women in Computer Vision Workshop to increase the visibility and inclusion of women researchers in computer vision field, organized in conjunction with CVPR 2018. Computer vision and machine learning have made incredible progress over the past years, yet the number of female researchers is still low both in academia and industry. WiCV is organized to raise visibility of female researchers, to increase the collaboration, and to provide mentorship and give opportunities to female-identifying junior researchers in the field. In its fourth year, we are proud to present the changes and improvements over the past years, summary of statistics for presenters and attendees, followed by expectations from future generations