Hear talks from experts on the latest topics in AI, ML and Computer Vision!

Recent 3D deep networks such as SwinUNETR, SwinUNETRv2, and 3D UX-Net have shown promising performance by leveraging self-attention and large-kernel convolutions to capture the volumetric context. However, their substantial computational requirements limit their use in real-time and resource-constrained environments. In this paper, we propose EffiDec3D, an optimized 3D decoder that employs a channel reduction strategy across all decoder stages and removes the high-resolution layers when their contribution to segmentation quality is minimal. Our optimized EffiDec3D decoder achieves a 96.4% reduction in #Params and a 93.0% reduction in #FLOPs compared to the decoder of original 3D UX-Net. Our extensive experiments on 12 different medical imaging tasks confirm that EffiDec3D not only significantly reduces the computational demands, but also maintains a performance level comparable to original models, thus establishing a new standard for efficient 3D medical image segmentation.

As AI and computer vision models are leveraged more broadly in society, we should be better prepared for adversarial attacks by bad actors. In this talk, we'll cover some of the common methods for performing adversarial attacks on CV models. Adversarial attacks are deliberate attempts to deceive neural networks into generating incorrect predictions by making subtle alterations to the input data.

Getting autonomous vehicle data ready for real use, whether for training, simulation, or evaluation, isn’t just about collecting LIDAR and camera frames. It’s about making sure every point lands where it should, in the right frame, at the right time.

In this talk, we’ll break down what it actually takes to go from raw logs to a clean, usable AV dataset. We’ll walk through the practical process of validating transformations, aligning coordinate systems, checking intrinsics and extrinsics, and making sure your projected points actually show up on camera images. Along the way, we’ll share a checklist of common failure points and hard-won debugging tips.

Finally, we’ll show how doing this right unlocks downstream tools like Omniverse Nurec and Cosmos—enabling powerful workflows like digital reconstruction, simulation, and large-scale synthetic data generation

In today’s AI landscape, these techniques are crucial. Clustering methods help organize unstructured data into meaningful groups, aiding knowledge discovery, feature analysis, and retrieval-augmented generation. From k-means to DBSCAN and hierarchical approaches like FINCH, selecting the right method is key: including balancing scalability, managing noise sensitivity, and fitting computational demands. This presentation provides an in-depth exploration of the current state-of-the-art of clustering techniques with a strong focus on their applications within computer vision.