CAR-Net: Clairvoyant Attentive Recurrent Network

We present an interpretable framework for path prediction that leverages dependencies between agents' behaviors and their spatial navigation environment. We exploit two sources of information: the past motion trajectory of the agent of interest and a wide top-view image of the navigation scene. We propose a Clairvoyant Attentive Recurrent Network (CAR-Net) that learns where to look in a large image of the scene when solving the path prediction task. Our method can attend to any area, or combination of areas, within the raw image (e.g., road intersections) when predicting the trajectory of the agent. This allows us to visualize fine-grained semantic elements of navigation scenes that influence the prediction of trajectories. To study the impact of space on agents' trajectories, we build a new dataset made of top-view images of hundreds of scenes (Formula One racing tracks) where agents' behaviors are heavily influenced by known areas in the images (e.g., upcoming turns). CAR-Net successfully attends to these salient regions. Additionally, CAR-Net reaches state-of-the-art accuracy on the standard trajectory forecasting benchmark, Stanford Drone Dataset (SDD). Finally, we show CAR-Net's ability to generalize to unseen scenes.Comment: The 2nd and 3rd authors contributed equall

Deep Learning for Dense Interpretation of Video: Survey of Various Approach, Challenges, Datasets and Metrics

Video interpretation has garnered considerable attention in computer vision and natural language processing fields due to the rapid expansion of video data and the increasing demand for various applications such as intelligent video search, automated video subtitling, and assistance for visually impaired individuals. However, video interpretation presents greater challenges due to the inclusion of both temporal and spatial information within the video. While deep learning models for images, text, and audio have made significant progress, efforts have recently been focused on developing deep networks for video interpretation. A thorough evaluation of current research is necessary to provide insights for future endeavors, considering the myriad techniques, datasets, features, and evaluation criteria available in the video domain. This study offers a survey of recent advancements in deep learning for dense video interpretation, addressing various datasets and the challenges they present, as well as key features in video interpretation. Additionally, it provides a comprehensive overview of the latest deep learning models in video interpretation, which have been instrumental in activity identification and video description or captioning. The paper compares the performance of several deep learning models in this field based on specific metrics. Finally, the study summarizes future trends and directions in video interpretation

EC^2: Emergent Communication for Embodied Control

Embodied control requires agents to leverage multi-modal pre-training to quickly learn how to act in new environments, where video demonstrations contain visual and motion details needed for low-level perception and control, and language instructions support generalization with abstract, symbolic structures. While recent approaches apply contrastive learning to force alignment between the two modalities, we hypothesize better modeling their complementary differences can lead to more holistic representations for downstream adaption. To this end, we propose Emergent Communication for Embodied Control (EC^2), a novel scheme to pre-train video-language representations for few-shot embodied control. The key idea is to learn an unsupervised "language" of videos via emergent communication, which bridges the semantics of video details and structures of natural language. We learn embodied representations of video trajectories, emergent language, and natural language using a language model, which is then used to finetune a lightweight policy network for downstream control. Through extensive experiments in Metaworld and Franka Kitchen embodied benchmarks, EC^2 is shown to consistently outperform previous contrastive learning methods for both videos and texts as task inputs. Further ablations confirm the importance of the emergent language, which is beneficial for both video and language learning, and significantly superior to using pre-trained video captions. We also present a quantitative and qualitative analysis of the emergent language and discuss future directions toward better understanding and leveraging emergent communication in embodied tasks.Comment: Published in CVPR202

Causally-Inspired Generalizable Deep Learning Methods under Distribution Shifts

Deep learning methods achieved remarkable success in various areas of artificial intelligence, due to their powerful distribution-matching capabilities. However, these successes rely heavily on the i.i.d assumption, i.e., the data distributions in the training and test datasets are the same. In this way, current deep learning methods typically exhibit poor generalization under distribution shift, performing poorly on test data with a distribution that differs from the training data. This significantly hinders the application of deep learning methods to real-world scenarios, as the distribution of test data is not always the same as the training distribution in our rapidly evolving world. This thesis aims to discuss how to construct generalizable deep learning methods under distribution shifts. To achieve this, the thesis first models one prediction task as a structural causal model (SCM) which establishes the relationship between variables using directed acyclic graphs. In an SCM, some variables are easily changed across domains while others are not. However, deep learning methods often unintentionally mix invariant variables with easily changed variables, and thus deviate the learned model from the true one, resulting in the poor generalization ability under distribution shift. To remedy this issue, we propose specific algorithms to model such an invariant part of the SCM with deep learning methods, and experimentally show it is beneficial for the trained model to generalize well into different distributions of the same task. Last, we further propose to identify and model the variant information in the new test distribution so that we can fully adapt the trained deep learning model accordingly. We show the method can be extended for several practical applications, such as classification under label shift, image translation under semantics shift, robotics control in dynamics generalization and generalizing large language models into visual question-answer tasks

Gesture Recognition and Control for Semi-Autonomous Robotic Assistant Surgeons

The next stage for robotics development is to introduce autonomy and cooperation with human agents in tasks that require high levels of precision and/or that exert considerable physical strain. To guarantee the highest possible safety standards, the best approach is to devise a deterministic automaton that performs identically for each operation. Clearly, such approach inevitably fails to adapt itself to changing environments or different human companions. In a surgical scenario, the highest variability happens for the timing of different actions performed within the same phases. This thesis explores the solutions adopted in pursuing automation in robotic minimally-invasive surgeries (R-MIS) and presents a novel cognitive control architecture that uses a multi-modal neural network trained on a cooperative task performed by human surgeons and produces an action segmentation that provides the required timing for actions while maintaining full phase execution control via a deterministic Supervisory Controller and full execution safety by a velocity-constrained Model-Predictive Controller

Deep Architectures for Visual Recognition and Description

In recent times, digital media contents are inherently of multimedia type, consisting of the form text, audio, image and video. Several of the outstanding computer Vision (CV) problems are being successfully solved with the help of modern Machine Learning (ML) techniques. Plenty of research work has already been carried out in the field of Automatic Image Annotation (AIA), Image Captioning and Video Tagging. Video Captioning, i.e., automatic description generation from digital video, however, is a different and complex problem altogether. This study compares various existing video captioning approaches available today and attempts their classification and analysis based on different parameters, viz., type of captioning methods (generation/retrieval), type of learning models employed, the desired output description length generated, etc. This dissertation also attempts to critically analyze the existing benchmark datasets used in various video captioning models and the evaluation metrics for assessing the final quality of the resultant video descriptions generated. A detailed study of important existing models, highlighting their comparative advantages as well as disadvantages are also included. In this study a novel approach for video captioning on the Microsoft Video Description (MSVD) dataset and Microsoft Video-to-Text (MSR-VTT) dataset is proposed using supervised learning techniques to train a deep combinational framework, for achieving better quality video captioning via predicting semantic tags. We develop simple shallow CNN (2D and 3D) as feature extractors, Deep Neural Networks (DNNs and Bidirectional LSTMs (BiLSTMs) as tag prediction models and Recurrent Neural Networks (RNNs) (LSTM) model as the language model. The aim of the work was to provide an alternative narrative to generating captions from videos via semantic tag predictions and deploy simpler shallower deep model architectures with lower memory requirements as solution so that it is not very memory extensive and the developed models prove to be stable and viable options when the scale of the data is increased. This study also successfully employed deep architectures like the Convolutional Neural Network (CNN) for speeding up automation process of hand gesture recognition and classification of the sign languages of the Indian classical dance form, ‘Bharatnatyam’. This hand gesture classification is primarily aimed at 1) building a novel dataset of 2D single hand gestures belonging to 27 classes that were collected from (i) Google search engine (Google images), (ii) YouTube videos (dynamic and with background considered) and (iii) professional artists under staged environment constraints (plain backgrounds). 2) exploring the effectiveness of CNNs for identifying and classifying the single hand gestures by optimizing the hyperparameters, and 3) evaluating the impacts of transfer learning and double transfer learning, which is a novel concept explored for achieving higher classification accuracy