Imagine hearing a dog bark and instinctively turning toward the sound--only to find a parked car, while a silent dog sits nearby. Such moments of sensory conflict challenge perception, yet humans flexibly resolve these discrepancies, prioritizing auditory cues over misleading visuals to accurately localize sounds. Despite the rapid advancement of multimodal AI models that integrate vision and sound, little is known about how these systems handle cross-modal conflicts or whether they favor one modality over another. Here, we systematically and quantitatively examine modality bias and conflict resolution in AI models for Sound Source Localization (SSL). We evaluate a wide range of state-of-the-art multimodal models and compare them against human performance in psychophysics experiments spanning six audiovisual conditions, including congruent, conflicting, and absent visual and audio cues.

Table 3 outlines the and reasoning tasks included in the MMPerspective benchmark. Sample cases and representative questions are included to illustrate the task format and input style. We also show examples of perspective-invariant image operations for robustness evaluation in Figure 17, including cropping, masking, flipping, and rotation. Where is the vanishing point in this image? Critical Line Perception (CLP) 123 Figure 9 Determine which of the highlighted lines is the horizon line. Which line highlighted in the image is the Horizon Line?

Seeing hidden structures and objects around corners is critical for robots operating in complex, cluttered environments. Existing methods, however, are limited to detecting and tracking hidden objects rather than reconstructing the occluded full scene.

To compute the discrepancy term dst, we add a per-location domain classifier h tw ˆ . It W consti semantic tutes map corresponds to the either source or target domain. On the other hand, hˆ predicts the Bird-Eye View binary segmentation map. In figure 9.1 we show the Lift-Splat Adapt diagram. Our training strategy requires little modification to the original architecture, e.g.

The requirement of generating predictions that exactly fulfill the fundamental symmetry of the corresponding physical quantities has profoundly shaped the development of machine-learning models for physical simulations. In many cases, models are built using constrained mathematical forms that ensure that symmetries are enforced exactly. However, unconstrained models that do not obey rotational symmetries are often found to have competitive performance, and to be able to \emph{learn} to a high level of accuracy an approximate equivariant behavior with a simple data augmentation strategy. In this paper, we introduce rigorous metrics to measure the symmetry content of the learned representations in such models, and assess the accuracy by which the outputs fulfill the equivariant condition. We apply these metrics to two unconstrained, transformer-based models operating on decorated point clouds (a graph neural network for atomistic simulations and a PointNet-style architecture for particle physics) to investigate how symmetry information is processed across architectural layers and is learned during training. Based on these insights, we establish a rigorous framework for diagnosing spectral failure modes in ML models. Enabled by this analysis, we demonstrate that one can achieve superior stability and accuracy by strategically injecting the minimum required inductive biases, preserving the high expressivity and scalability of unconstrained architectures while guaranteeing physical fidelity.

Detecting 3D keypoints with semantic consistency is widely used in many scenarios such as pose estimation, shape registration and robotics. Currently, most unsupervised 3D keypoint detection methods focus on the rigid-body objects. However, when faced with deformable objects, the keypoints they identify do not preserve semantic consistency well. In this paper, we introduce an innovative unsupervised keypoint detector Key-Grid for both the rigid-body and deformable objects, which is an autoencoder framework. The encoder predicts keypoints and the decoder utilizes the generated keypoints to reconstruct the objects. Unlike previous work, we leverage the identified keypoint in formation to form a 3D grid feature heatmap called grid heatmap, which is used in the decoder section.

The current methods follow the paradigm of adapting the visual task outputs to language-oriented formats.