DatasetEquity: Are All Samples Created Equal? In The Quest For Equity Within Datasets

Data imbalance is a well-known issue in the field of machine learning, attributable to the cost of data collection, the difficulty of labeling, and the geographical distribution of the data. In computer vision, bias in data distribution caused by image appearance remains highly unexplored. Compared to categorical distributions using class labels, image appearance reveals complex relationships between objects beyond what class labels provide. Clustering deep perceptual features extracted from raw pixels gives a richer representation of the data. This paper presents a novel method for addressing data imbalance in machine learning. The method computes sample likelihoods based on image appearance using deep perceptual embeddings and clustering. It then uses these likelihoods to weigh samples differently during training with a proposed $\textbf{Generalized Focal Loss}$ function. This loss can be easily integrated with deep learning algorithms. Experiments validate the method's effectiveness across autonomous driving vision datasets including KITTI and nuScenes. The loss function improves state-of-the-art 3D object detection methods, achieving over $200\%$ AP gains on under-represented classes (Cyclist) in the KITTI dataset. The results demonstrate the method is generalizable, complements existing techniques, and is particularly beneficial for smaller datasets and rare classes. Code is available at: https://github.com/towardsautonomy/DatasetEquityComment: ICCV 2023 Worksho

Ref-DVGO: Reflection-Aware Direct Voxel Grid Optimization for an Improved Quality-Efficiency Trade-Off in Reflective Scene Reconstruction

Neural Radiance Fields (NeRFs) have revolutionized the field of novel view synthesis, demonstrating remarkable performance. However, the modeling and rendering of reflective objects remain challenging problems. Recent methods have shown significant improvements over the baselines in handling reflective scenes, albeit at the expense of efficiency. In this work, we aim to strike a balance between efficiency and quality. To this end, we investigate an implicit-explicit approach based on conventional volume rendering to enhance the reconstruction quality and accelerate the training and rendering processes. We adopt an efficient density-based grid representation and reparameterize the reflected radiance in our pipeline. Our proposed reflection-aware approach achieves a competitive quality efficiency trade-off compared to competing methods. Based on our experimental results, we propose and discuss hypotheses regarding the factors influencing the results of density-based methods for reconstructing reflective objects. The source code is available at https://github.com/gkouros/ref-dvgo.Comment: 5 pages, 4 figures, 3 tables, ICCV TRICKY 2023 Worksho

Look Both Ways: Bidirectional Visual Sensing for Automatic Multi-Camera Registration

This work describes the automatic registration of a large network (approximately 40) of fixed, ceiling-mounted environment cameras spread over a large area (approximately 800 squared meters) using a mobile calibration robot equipped with a single upward-facing fisheye camera and a backlit ArUco marker for easy detection. The fisheye camera is used to do visual odometry (VO), and the ArUco marker facilitates easy detection of the calibration robot in the environment cameras. In addition, the fisheye camera is also able to detect the environment cameras. This two-way, bidirectional detection constrains the pose of the environment cameras to solve an optimization problem. Such an approach can be used to automatically register a large-scale multi-camera system used for surveillance, automated parking, or robotic applications. This VO based multi-camera registration method has been extensively validated using real-world experiments, and also compared against a similar approach which uses a LiDAR - an expensive, heavier and power hungry sensor

RGB-X Object Detection via Scene-Specific Fusion Modules

Multimodal deep sensor fusion has the potential to enable autonomous vehicles to visually understand their surrounding environments in all weather conditions. However, existing deep sensor fusion methods usually employ convoluted architectures with intermingled multimodal features, requiring large coregistered multimodal datasets for training. In this work, we present an efficient and modular RGB-X fusion network that can leverage and fuse pretrained single-modal models via scene-specific fusion modules, thereby enabling joint input-adaptive network architectures to be created using small, coregistered multimodal datasets. Our experiments demonstrate the superiority of our method compared to existing works on RGB-thermal and RGB-gated datasets, performing fusion using only a small amount of additional parameters. Our code is available at https://github.com/dsriaditya999/RGBXFusion.Comment: Accepted to 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024

Structure Aware and Class Balanced 3D Object Detection on nuScenes Dataset

3-D object detection is pivotal for autonomous driving. Point cloud based methods have become increasingly popular for 3-D object detection, owing to their accurate depth information. NuTonomy's nuScenes dataset greatly extends commonly used datasets such as KITTI in size, sensor modalities, categories, and annotation numbers. However, it suffers from severe class imbalance. The Class-balanced Grouping and Sampling paper addresses this issue and suggests augmentation and sampling strategy. However, the localization precision of this model is affected by the loss of spatial information in the downscaled feature maps. We propose to enhance the performance of the CBGS model by designing an auxiliary network, that makes full use of the structure information of the 3D point cloud, in order to improve the localization accuracy. The detachable auxiliary network is jointly optimized by two point-level supervisions, namely foreground segmentation and center estimation. The auxiliary network does not introduce any extra computation during inference, since it can be detached at test time

Domain Adaptation for Object Detection using SE Adaptors and Center Loss

Despite growing interest in object detection, very few works address the extremely practical problem of cross-domain robustness especially for automative applications. In order to prevent drops in performance due to domain shift, we introduce an unsupervised domain adaptation method built on the foundation of faster-RCNN with two domain adaptation components addressing the shift at the instance and image levels respectively and apply a consistency regularization between them. We also introduce a family of adaptation layers that leverage the squeeze excitation mechanism called SE Adaptors to improve domain attention and thus improves performance without any prior requirement of knowledge of the new target domain. Finally, we incorporate a center loss in the instance and image level representations to improve the intra-class variance. We report all results with Cityscapes as our source domain and Foggy Cityscapes as the target domain outperforming previous baselines

Urodynamic profile in acute transverse myelitis patients: Its correlation with neurological outcome

Objective: The objective of this study was to observe urodynamic profile of acute transverse myelitis (ATM) patients and its correlation with neurological outcome. Patients and Methods: This prospective study was conducted in the neurorehabilitation unit of a tertiary university research hospital from July 2012 to June 2014. Forty-three patients (19 men) with ATM with bladder dysfunction, admitted in the rehabilitation unit, were included in this study. Urodynamic study (UDS) was performed in all the patients. Their neurological status was assessed using ASIA impairment scale and functional status was assessed using spinal cord independence measure. Bladder management was based on UDS findings. Results: In total, 17 patients had tetraplegia and 26 had paraplegia. Thirty-six patients (83.7%) had complaints of increased frequency and urgency of urine with 26 patients reported at least one episode of urge incontinence. Seven patients reported obstructive urinary complaints in the form of straining to void with 13 patients reported both urgency and straining to void and 3 also had stress incontinence. Thirty-seven (86.1%) patients had neurogenic overactive detrusor with or without sphincter dyssynergia and five patients had acontractile detrusor on UDS. No definitive pattern was observed between neurological status and bladder characteristics. All patients showed significant neurological and functional recovery with inpatient rehabilitation (P< 0.05 and P< 0.001, respectively). Conclusions: The problem of neurogenic bladder dysfunction is integral to ATM. Bladder management in these patients should be based on UDS findings. Bladder characteristics have no definitive pattern consistent with the neurological status

Neurogenic bladder following myelopathies: Has it any correlation with neurological and functional recovery?

Objectives: To observe neurogenic bladder pattern in patients with myelopathy by performing urodynamic study (UDS) and to observe whether it has any correlation with functional and neurological recovery. Patients and Methods: This prospective study was conducted with 90 patients with myelopathy, both traumatic and non-traumatic (males = 65) in a university tertiary research hospital in India between January 2011 and December 2013. Mean age was 33.5 ± 13.2 years (range 15-65 years), mean duration of injury was 82.63 ± 88.3 days (range 14-365 days) and mean length of stay (LOS) in the rehabilitation unit 42.5 ± 23.3 days (range 14-130 days). The urodynamic study was performed in all the patients to assess the neurogenic bladder pattern. Management was based on the UDS findings. Functional recovery was assessed using Barthel index (BI) scores and spinal cord independence measures (SCIM) scores. Neurological recovery was assessed using ASIA impairment scale (AIS). We tried to correlate neurogenic bladder patterns with recovery. Results: Fifty patients (55.6%) had overactive detrusor with 25 each had detrusor sphincter dyssynergia (DSD) and synergic sphincter. Thirty-eight patients had hypoactive/acontractile detrusor and two had normal studies. No significant correlation observed between neurogenic bladder pattern and change in BI scores (P = 0.696), SCIM scores (P = 0.135) or change in ASIA status (P = 0.841) in the study. Conclusions: More than half of the patients with myelopathies had overactive detrusor with or without dyssynergic sphincter according to the urodynamic study. Neurogenic bladder patterns had no significant correlation with functional and neurological recovery in these patients