However, the number of learned group tokens in GroupViT is a hyper-parameter and there is no constraint on it. The textembeddings is used inacontrastiveloss tomatch with the global visual representations. Figure 1: Comparison results of recall for all 25 classes between HAN [2] and the proposed MGN in terms of event-level audio, visual and audio-visual metrics,i.e.,Event_A,Event_V,and Event_AV.

This applies to common objects such as chairs, but also to novel objects that wehaveneverseenbefore.

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Diffusion backbone, across a diverse set of computer vision tasks ranging from classification to object detection to OOD generalization and more.

See Theorem 2 in Section 3.1 formoredetail.

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For example, while prior work has suggested that theglobally optimal VAEsolution canlearn thecorrect manifold dimension, anecessary (butnotsufficient)condition forproducing samplesfrom the true data distribution, this has never been rigorously proven. Moreover, it remains unclear how such considerations would change when various types of conditioning variablesare introduced, or when the data support is extended to a union of manifolds (e.g., as is likely the case for MNIST digits and related). In this work, we address these points by first proving that VAE global minima are indeed capable of recovering the correct manifold dimension.