In this paper, we study the challenging instance-wise vision-language tasks, where the free-form language is required to align with the objects instead of the whole image.
Also, the two-stream autoencoder works as a unified framework for the gating model and the unseen expert, which makes the proposed method computationally efficient.
To articulate the significance of the model perspective in novelty detection, we utilize backpropagated gradients.
To complement the learned information from activation-based representation, we propose utilizing a gradient-based representation that explicitly focuses on missing information.
In this paper, we utilize weight gradients from backpropagation to characterize the representation space learned by deep learning algorithms.
In this paper, we generate and control semantically interpretable filters that are directly learned from natural images in an unsupervised fashion.
This is a full-reference tempospatial approach that considers both temporal and spatial PSD characteristics.
We benchmark the performance of existing solutions in real-world scenarios and analyze the performance variation with respect to challenging conditions.