The UN estimates 2-5% of global GDP or $0.8 - $2.0 trillion dollars are laundered

However, many use cases of neural network verification require solving the inverse problem, or over-approximating the set of inputs that lead to certain outputs.

In Tab. 1, we conduct the test on a Tesla V100 to In Tab. 2, we train five ResNet34 models for the CIFAR benchmarks (CIFAR10 and CIFAR100), The blocks, labeled as block1 to block5, correspond to the output features obtained from shallow to deep. This can be expained as the model's In Section 4.1, we introduce channel-wise average abnormality under the assumption that Gradient-based Class Activation Mapping (GradCAM) can be regarded as having only first-order independent Here we provide a proof (from [18]) for this assumption. Then based on Eq. 2, we The issue of attribution can be viewed as the assignment of credit in cooperative game theory. Null Player Axiom: If removal of a feature across all potential coalitions with other features has no impact on the output, it should be assigned zero importance. In Section 4.2, we introduce the two-stage fusion strategy for GAIA-A and in Section 5.3, we briefly Eq. 8, the effect of output component is similar to the The extensive results are shown in Tab. 3. It indicates the effectiveness of our fusion strategy.

Consequently, we investigate how attribution gradients lead to uncertain explanation outcomes and introduce two forms of abnormalities for OOD detection: the zero-deflation abnormality and the channel-wise average abnormality.

LiDAR-based 3D detection methods currently use bird's-eye view (BEV) or range

Foundation models have achieved remarkable results in 2D and language tasks like image segmentation, object detection, and visual-language understanding. However, their potential to enrich 3D scene representation learning is largely untapped due to the existence of the domain gap. In this work, we propose an innovative methodology called Bridge3D to address this gap by pre-training 3D models using features, semantic masks, and captions sourced from foundation models. Specifically, our method employs semantic masks from foundation models to guide the masking and reconstruction process for the masked autoen-coder, enabling more focused attention on foreground representations.

Historical documents are invaluable carriers of human cultural heritage, containing important information about human history, culture, and literary arts.