Fingerspelling PoseNet: Enhancing Fingerspelling Translation with Pose-Based Transformer Models

We address the task of American Sign Language fingerspelling translation using videos in the wild. We exploit advances in more accurate hand pose estimation and propose a novel architecture that leverages the transformer based encoder-decoder model enabling seamless contextual word translation. The translation model is augmented by a novel loss term that accurately predicts the length of the finger-spelled word, benefiting both training and inference. We also propose a novel two-stage inference approach that re-ranks the hypotheses using the language model capabilities of the decoder. Through extensive experiments, we demonstrate that our proposed method outperforms the state-of-the-art models on ChicagoFSWild and ChicagoFSWild+ achieving more than 10% relative improvement in performance. Our findings highlight the effectiveness of our approach and its potential to advance fingerspelling recognition in sign language translation. Code is also available at https://github.com/pooyafayyaz/Fingerspelling-PoseNet.

Labeling Indoor Scenes with Fusion of Out-of-the-Box Perception Models

The image annotation stage is a critical and often the most time-consuming part required for training and evaluating object detection and semantic segmentation models. Deployment of the existing models in novel environments often requires detecting novel semantic classes not present in the training data. Furthermore, indoor scenes contain significant viewpoint variations, which need to be handled properly by trained perception models. We propose to leverage the recent advancements in state-of-the-art models for bottom-up segmentation (SAM), object detection (Detic), and semantic segmentation (MaskFormer), all trained on large-scale datasets. We aim to develop a cost-effective labeling approach to obtain pseudo-labels for semantic segmentation and object instance detection in indoor environments, with the ultimate goal of facilitating the training of lightweight models for various downstream tasks. We also propose a multi-view labeling fusion stage, which considers the setting where multiple views of the scenes are available and can be used to identify and rectify single-view inconsistencies. We demonstrate the effectiveness of the proposed approach on the Active Vision dataset and the ADE20K dataset. We evaluate the quality of our labeling process by comparing it with human annotations. Also, we demonstrate the effectiveness of the obtained labels in downstream tasks such as object goal navigation and part discovery. In the context of object goal navigation, we depict enhanced performance using this fusion approach compared to a zero-shot baseline that utilizes large monolithic vision-language pre-trained models.

Towards Grounded Visual Spatial Reasoning in Multi-Modal Vision Language Models

With the advances in large scale vision-and-language models (VLMs) it is of interest to assess their performance on various visual reasoning tasks such as counting, referring expressions and general visual question answering. The focus of this work is to study the ability of these models to understanding spatial relations. Previously, this has been tackled using image-text matching (Liu, Emerson, and Collier 2022) or visual question answering task, both showing poor performance and a large gap compared to human performance. To better understand the gap, we present fine-grained compositional grounding of spatial relationships and propose a bottom up approach for ranking spatial clauses and evaluating the performance of spatial relationship reasoning task. We propose to combine the evidence from grounding noun phrases corresponding to objects and their locations to compute the final rank of the spatial clause. We demonstrate the approach on representative vision-language models (Tan and Bansal 2019; Gupta et al. 2022; Kamath et al. 2021) and compare and highlight their abilities to reason about spatial relationships.

Graph-CoVis: GNN-based Multi-view Panorama Global Pose Estimation

In this paper, we address the problem of wide-baseline camera pose estimation from a group of 360$^\circ$ panoramas under upright-camera assumption. Recent work has demonstrated the merit of deep-learning for end-to-end direct relative pose regression in 360$^\circ$ panorama pairs [11]. To exploit the benefits of multi-view logic in a learning-based framework, we introduce Graph-CoVis, which non-trivially extends CoVisPose [11] from relative two-view to global multi-view spherical camera pose estimation. Graph-CoVis is a novel Graph Neural Network based architecture that jointly learns the co-visible structure and global motion in an end-to-end and fully-supervised approach. Using the ZInD [4] dataset, which features real homes presenting wide-baselines, occlusion, and limited visual overlap, we show that our model performs competitively to state-of-the-art approaches.

U2RLE: Uncertainty-Guided 2-Stage Room Layout Estimation

While the existing deep learning-based room layout estimation techniques demonstrate good overall accuracy, they are less effective for distant floor-wall boundary. To tackle this problem, we propose a novel uncertainty-guided approach for layout boundary estimation introducing new two-stage CNN architecture termed U2RLE. The initial stage predicts both floor-wall boundary and its uncertainty and is followed by the refinement of boundaries with high positional uncertainty using a different, distance-aware loss. Finally, outputs from the two stages are merged to produce the room layout. Experiments using ZInD and Structure3D datasets show that U2RLE improves over current state-of-the-art, being able to handle both near and far walls better. In particular, U2RLE outperforms current state-of-the-art techniques for the most distant walls.

Comparison of Model-Free and Model-Based Learning-Informed Planning for PointGoal Navigation

In recent years several learning approaches to point goal navigation in previously unseen environments have been proposed. They vary in the representations of the environments, problem decomposition, and experimental evaluation. In this work, we compare the state-of-the-art Deep Reinforcement Learning based approaches with Partially Observable Markov Decision Process (POMDP) formulation of the point goal navigation problem. We adapt the (POMDP) sub-goal framework proposed by [1] and modify the component that estimates frontier properties by using partial semantic maps of indoor scenes built from images' semantic segmentation. In addition to the well-known completeness of the model-based approach, we demonstrate that it is robust and efficient in that it leverages informative, learned properties of the frontiers compared to an optimistic frontier-based planner. We also demonstrate its data efficiency compared to the end-to-end deep reinforcement learning approaches. We compare our results against an optimistic planner, ANS and DD-PPO on Matterport3D dataset using the Habitat Simulator. We show comparable, though slightly worse performance than the SOTA DD-PPO approach, yet with far fewer data.

Learning-Augmented Model-Based Planning for Visual Exploration

We consider the problem of time-limited robotic exploration in previously unseen environments where exploration is limited by a predefined amount of time. We propose a novel exploration approach using learning-augmented model-based planning. We generate a set of subgoals associated with frontiers on the current map and derive a Bellman Equation for exploration with these subgoals. Visual sensing and advances in semantic mapping of indoor scenes are exploited for training a deep convolutional neural network to estimate properties associated with each frontier: the expected unobserved area beyond the frontier and the expected timesteps (discretized actions) required to explore it. The proposed model-based planner is guaranteed to explore the whole scene if time permits. We thoroughly evaluate our approach on a large-scale pseudo-realistic indoor dataset (Matterport3D) with the Habitat simulator. We compare our approach with classical and more recent RL-based exploration methods, demonstrating its clear advantages in several settings.

Self-supervised Pre-training for Semantic Segmentation in an Indoor Scene

The ability to endow maps of indoor scenes with semantic information is an integral part of robotic agents which perform different tasks such as target driven navigation, object search or object rearrangement. The state-of-the-art methods use Deep Convolutional Neural Networks (DCNNs) for predicting semantic segmentation of an image as useful representation for these tasks. The accuracy of semantic segmentation depends on the availability and the amount of labeled data from the target environment or the ability to bridge the domain gap between test and training environment. We propose RegConsist, a method for self-supervised pre-training of a semantic segmentation model, exploiting the ability of the agent to move and register multiple views in the novel environment. Given the spatial and temporal consistency cues used for pixel level data association, we use a variant of contrastive learning to train a DCNN model for predicting semantic segmentation from RGB views in the target environment. The proposed method outperforms models pre-trained on ImageNet and achieves competitive performance when using models that are trained for exactly the same task but on a different dataset. We also perform various ablation studies to analyze and demonstrate the efficacy of our proposed method.

Using Unmanned Aerial Systems (UAS) for Assessing and Monitoring Fall Hazard Prevention Systems in High-rise Building Projects

This study develops a framework for unmanned aerial systems (UASs) to monitor fall hazard prevention systems near unprotected edges and openings in high-rise building projects. A three-step machine-learning-based framework was developed and tested to detect guardrail posts from the images captured by UAS. First, a guardrail detector was trained to localize the candidate locations of posts supporting the guardrail. Since images were used in this process collected from an actual job site, several false detections were identified. Therefore, additional constraints were introduced in the following steps to filter out false detections. Second, the research team applied a horizontal line detector to the image to properly detect floors and remove the detections that were not close to the floors. Finally, since the guardrail posts are installed with approximately normal distribution between each post, the space between them was estimated and used to find the most likely distance between the two posts. The research team used various combinations of the developed approaches to monitor guardrail systems in the captured images from a high-rise building project. Comparing the precision and recall metrics indicated that the cascade classifier achieves better performance with floor detection and guardrail spacing estimation. The research outcomes illustrate that the proposed guardrail recognition system can improve the assessment of guardrails and facilitate the safety engineer's task of identifying fall hazards in high-rise building projects.

Object Pose Estimation using Mid-level Visual Representations

This work proposes a novel pose estimation model for object categories that can be effectively transferred to previously unseen environments. The deep convolutional network models (CNN) for pose estimation are typically trained and evaluated on datasets specifically curated for object detection, pose estimation, or 3D reconstruction, which requires large amounts of training data. In this work, we propose a model for pose estimation that can be trained with small amount of data and is built on the top of generic mid-level representations \cite{taskonomy2018} (e.g. surface normal estimation and re-shading). These representations are trained on a large dataset without requiring pose and object annotations. Later on, the predictions are refined with a small CNN neural network that exploits object masks and silhouette retrieval. The presented approach achieves superior performance on the Pix3D dataset \cite{pix3d} and shows nearly 35\% improvement over the existing models when only 25\% of the training data is available. We show that the approach is favorable when it comes to generalization and transfer to novel environments. Towards this end, we introduce a new pose estimation benchmark for commonly encountered furniture categories on challenging Active Vision Dataset \cite{Ammirato2017ADF} and evaluated the models trained on the Pix3D dataset.