Source-Free and Image-Only Unsupervised Domain Adaptation for Category Level Object Pose Estimation

We consider the problem of source-free unsupervised category-level pose estimation from only RGB images to a target domain without any access to source domain data or 3D annotations during adaptation. Collecting and annotating real-world 3D data and corresponding images is laborious, expensive, yet unavoidable process, since even 3D pose domain adaptation methods require 3D data in the target domain. We introduce 3DUDA, a method capable of adapting to a nuisance-ridden target domain without 3D or depth data. Our key insight stems from the observation that specific object subparts remain stable across out-of-domain (OOD) scenarios, enabling strategic utilization of these invariant subcomponents for effective model updates. We represent object categories as simple cuboid meshes, and harness a generative model of neural feature activations modeled at each mesh vertex learnt using differential rendering. We focus on individual locally robust mesh vertex features and iteratively update them based on their proximity to corresponding features in the target domain even when the global pose is not correct. Our model is then trained in an EM fashion, alternating between updating the vertex features and the feature extractor. We show that our method simulates fine-tuning on a global pseudo-labeled dataset under mild assumptions, which converges to the target domain asymptotically. Through extensive empirical validation, including a complex extreme UDA setup which combines real nuisances, synthetic noise, and occlusion, we demonstrate the potency of our simple approach in addressing the domain shift challenge and significantly improving pose estimation accuracy.Comment: 36 pages, 9 figures, 50 tables; ICLR 2024 (Poster

SuryaKiran at MEDIQA-Sum 2023: Leveraging LoRA for Clinical Dialogue Summarization

Finetuning Large Language Models helps improve the results for domain-specific use cases. End-to-end finetuning of large language models is time and resource intensive and has high storage requirements to store the finetuned version of the large language model. Parameter Efficient Fine Tuning (PEFT) methods address the time and resource challenges by keeping the large language model as a fixed base and add additional layers, which the PEFT methods finetune. This paper demonstrates the evaluation results for one such PEFT method Low Rank Adaptation (LoRA), for Clinical Dialogue Summarization. The evaluation results show that LoRA works at par with end-to-end finetuning for a large language model. The paper presents the evaluations done for solving both the Subtask A and B from ImageCLEFmedical {https://www.imageclef.org/2023/medical

SPARSH: a camp based approach for orthopaedic disabilities and its success in central India

Background: Around 15% of population in the world is living with disability. The present study was carried out during the special project for assistance, rehabilitation and strengthening of handicapped (SPARSH) camp to know the current pattern of locomotor disability and to observe the outcome of the camp surgeries for the correction of deformity.Methods: This prospective observational cross-sectional study was conducted at the department of orthopaedics and traumatology Gandhi Medical College at SPARSH camp organised by the Government of M. P. at J. K. hospital Bhopal. All the patients with locomotor disability attending the SPARSH camp irrespective of age, sex and cause, were included in the studyResults: In total 287 patients attended the camp in which majority of the patients were suffering from cerebral palsy. 107 patients were selected for operative intervention in which tendo-achilles lengthening was performed most commonly.Conclusions: The corrective surgical camp provides an avenue of healthcare opportunity for the underprivileged sector of society. A camp based approach helps in identification, gradation & rehabilitation of orthopaedic deformities

ACOUSTIC SPEECH RECOGNITION FOR MARATHI LANGUAGE USING SPHINX

Speech recognition or speech to text processing, is a process of recognizing human speech by the computer and converting into text. In speech recognition, transcripts are created by taking recordings of speech as audio and their text transcriptions. Speech based applications which include Natural Language Processing (NLP) techniques are popular and an active area of research. Input to such applications is in natural language and output is obtained in natural language. Speech recognition mostly revolves around three approaches namely Acoustic phonetic approach, Pattern recognition approach and Artificial intelligence approach. Creation of acoustic model requires a large database of speech and training algorithms. The output of an ASR system is recognition and translation of spoken language into text by computers and computerized devices. ASR today finds enormous application in tasks that require human machine interfaces like, voice dialing, and etc. Our key contribution in this paper is to create corpora for Marathi language and explore the use of Sphinx engine for automatic speech recognitio

Proportion of sleep-related breathing disorders and their association with echocardiographic parameters in stable patients with chronic obstructive pulmonary disease: a cross-sectional observational exploratory study

Chronic obstructive pulmonary disease (COPD) is a major cause of morbidity and mortality throughout the world. The coexistence of COPD and obstructive sleep apnea (OSA) (i.e., overlap syndrome) has been reported in several studies. Both disorders independently increase the risk of cardiovascular complications. Hence, there is a theoretical possibility that cardiovascular parameters may be worse in patients with overlap syndrome compared to those with only COPD. However, this has been sparsely assessed in the literature. This study aimed to compare the clinical characteristics, echocardiography, and sleep parameters amongst COPD patients with and without sleep-related breathing disorders (SRBD). This observational, cross-sectional study included 30 patients with stable COPD. All participants underwent a detailed clinical evaluation, followed by level 1 polysomnography (PSG). Each participant underwent echocardiographic evaluation to estimate mean pulmonary artery pressure from right ventricular systolic pressure (RVSP). Based on their PSG findings, participants were classified into non-SRBD and SRBD groups. Both groups were further compared with respect to clinical characteristics, echocardiographic, and PSG parameters. We found that most of the participants (93.3%) were male, and the mean age of the study population was 56±8.2 years. The only SRBD identified in this study was OSA, which was observed in 80% of participants. In this group, OSA was not associated with obesity. Systemic hypertension (50%) was the most common comorbidity, followed by diabetes mellitus (26.67%), but both were not significantly different between the groups. The mean RVSP was significantly higher amongst OSA patients than non-OSA patients (41.25±14.98 versus 30.83±5.84, respectively; p=0.01). OSA was seen in 80% of participants with stable COPD, even in the absence of obesity. The presence of OSA was associated with a higher RVSP in this patient group