A known flaw of statistical machine learning is the reliance on spurious correlations. Due to this, standard machine learning models often have degraded performance on minority sub-groups despite the presence of consistent causal signals. Group robustness solutions aim to address this by largely focusing on minimizing the expected error on the worst-performing minority group, often at the expense of overall performance. These state-of-the-art approaches also rely on accurate group annotations, which are difficult to obtain in practice, and makes the model's performance highly sensitive to label noise. In this work, we address these gaps with a simple yet effective solution: we show that integrating group variance minimization with the standard ERM objective is an upper bound on the worst-group loss, enabling a multi-objective optimization framework that improves both average and worst-group performance while effectively balancing their trade-off. We further propose a generalized cross entropy based multi-objective optimization training paradigm which demonstrates Pareto optimal performance under 10% noisy label conditions, and also maintains strong performance for both metrics across varying noise levels while state-of-the-art methods succeed at one metric but fail at another. We evaluate our method on standard benchmark - Waterbirds and CelebA datasets and demonstrate Pareto-optimal performance in both noise-free and noisy settings.
Colorectal cancer is a major cause of cancer mortality, with a significant proportion of polyps being missed during colonoscopy. To address this challenge, we propose EvoPolypNet, an automated polyp segmentation framework optimized through evolutionary neural architecture search (NAS). EvoPolypNet integrates convolutional feature extraction, attention mechanisms, multi-scale processing, and feature recalibration within an encoder-decoder architecture. The evolutionary NAS employs crossover, mutation, and elitism to explore diverse architectural configurations, guided by a defined search space. Evaluated on CVC-ClinicDB and Kvasir-SEG datasets, EvoPolypNet achieves superior performance with an mDice of 0.939 and mIoU of 0.898 on CVC-ClinicDB and demonstrates strong generalization on the unseen Kvasir-SEG dataset with an mDice of 0.919. The resulting model contains only 2.3 million parameters, enabling high segmentation accuracy with computational efficiency suitable for clinical deployment. Visual comparisons further validate the model's precision in delineating polyp boundaries. EvoPolyp-Net establishes a new benchmark for polyp segmentation by balancing performance, generalization, and efficiency.
Recent advances in immersive technologies have fueled efforts to bring real-world experiences into virtual environments, particularly in the art and performance domains. One emerging direction is the digital recreation of dance performances from a single monocular image. This task presents significant challenges due to the need for accurate 3D reconstruction, realistic geometry fusion, and photorealistic relighting under novel illumination. In this work, we propose a complete pipeline that reconstructs 3D dance scenes from a single image and enables real-time relighting in a browser-based renderer. The system combines monocular geometry estimation for the static stage and high-fidelity human mesh recovery for the performer. After mesh alignment and integration, the final scene is relit interactively using physically-based shading, environment map illumination, and dynamic directional lights, with realistic shadows computed via custom shader programming. The method operates fully on the client side, without requiring camera calibration or multi-view supervision. To assess the perceptual quality of our relit scenes, we conduct a user study focused on realism and material-light interaction. Results demonstrate that our approach generates compelling 3D representations of performance scenes, enabling realistic visualization under varied lighting conditions from minimal input.
Autism Spectrum Disorder ( ASD) often includes challenges in verbal communication, particularly for minimally verbal ( mv*) individuals. Mv* individuals with ASD often find it difficult to communicate with people since their vocalizations are highly personalized and often non-verbal, making it extremely difficult for people to understand their needs. Recent advances in machine learning and deep learning have made it possible for researchers to explore new avenues and provide personalized care for individuals with ASD. This research explores transfer learning and Mel spectrograms to classify non-verbal vocalizations from mv* ASD individuals, aiming to improve understanding and communication. We conduct three experiments to verify the robustness and generalizability of our approach in different setups. Using a dataset of 7077 vocalizations from eight mv* individuals collected from noisy, real-world settings, we convert the audio signals to Mel spectrograms and evaluate six transfer learning models and a custom convolutional neural network model. Our experiments demonstrated that DenseNet161, ResNet101, and VGG19, particularly when combined in an ensemble, effectively classify vocalizations into six categories: self-talk, frustration, delight, dysregulation, social, and request. Results suggest that this ensemble achieves state-of-the-art performance with an F1 score of 0.79, outperforming traditional machine learning methods and highlighting the potential of artificial intelligence in enhancing care for mv* ASD individuals.
We present Consistent3D, a training-free framework for high-fidelity 3D reconstruction from sparse views, leveraging point-cloud-guided video diffusion model to generate geometrically consistent novel views for 3D Gaussian Splatting. To address the severe multi-view inconsistency commonly observed in diffusion-based novel view synthesis, we introduce a geometry-guided pipeline. Specifically, we leverage the point cloud-estimated from sparse input images-as 3D priors to guide the video diffusion process, enabling both geometrically plausible and frame-consistent novel view synthesis. Furthermore, to achieve geometrically consistent depth, we introduce a Local Depth Alignment (LDA) strategy that adjusts monocular depth estimates into a scale-aware representation. This strategy is performed relative to the sparse input point cloud prior, resolving the inherent scale ambiguity in monocular depth prediction. We then propose a consistency evaluation module that computes 2D reprojection error and 3D depth discrepancy across views, yielding confidence scores. These views with high confidence scores are fused into a regularized 3D Gaussian Splatting (3DGS) pipeline, where parameters of Gaussians are optimized under confidenceweighted RGB and depth constraints. By enforcing 3D consistency through point-cloud-guided video diffusion model and a comprehensive confidence-weighted 3DGS optimization-which integrates depth alignment, confidence prediction, incremental fusion, soft constraint optimization, and real-time visual refinement-Consistent3D achieves high-fidelity and visually smooth 3DGS reconstruction from sparse observations without requiring model training. Our framework effectively bridges the generative power of diffusion models with the representational efficiency of 3DGS, delivering geometrically consistent and visually compelling 3D scenes.
