Design a multi-class classification and interpretability pipeline that distinguishes rheumatoid arthritis (RA), psoriatic arthritis (PsA), and osteoarthritis (OA) from hand/feet X-ray images. The architecture combines a CNN backbone (e.g., DenseNet or EfficientNet) for disease classification with an attention-based or Grad-CAM explanation module to

Design 'EfficientDenseNet' — a DenseNet variant under 10M params that incorporates VoVNet-style one-shot aggregation (OSA), depthwise separable bottlenecks, squeeze-and-excitation attention, LayerNorm pre-activation, and aggressive compound scaling. The goal is to train 2-3× faster than DenseNet-121 while maintaining or improving accuracy on ImageN

Design an enhanced ConvNeXt V2 architecture incorporating multi-scale dynamic convolutions, adaptive feature recalibration via a lightweight coordinate-aware gating mechanism, and a refined pretraining strategy (improved FCMAE with spatial-aware masking and hierarchical reconstruction loss) to improve representation quality and downstream performan

Design a CNN architecture augmented with L1 (Lasso) regularization that applies sparsity-inducing penalties on convolutional and fully-connected weights, producing a sparse feature hierarchy. The design covers integration via weight decay, structured sparsity variants (group lasso on filters), and a forward-pass formulation where lasso-proximal ste

Design a multi-shopper, multi-product autonomous checkout vision system that generalises a gesture-based prototype. The system integrates hand-item association (shopper-ID to item-ID), temporal cart-state modelling via a state machine or transformer, and robustness to occlusions, put-backs, and hand-offs. Evaluation targets on-device latency and pr