Search Results for author:
Found 11 papers, 6 papers with code
X-DETR: A Versatile Architecture for Instance-wise Vision-Language Tasks
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.
A Gating Model for Bias Calibration in Generalized Zero-shot Learning
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.
Novelty Detection Through Model-Based Characterization of Neural Networks
To articulate the significance of the model perspective in novelty detection, we utilize backpropagated gradients.
Contrastive Explanations in Neural Networks
Current modes of visual explanations answer questions of the form $`Why \text{ } P?'$.
Backpropagated Gradient Representations for Anomaly Detection
Anomalies require more drastic model updates to fully represent them compared to normal data.
Characterizing Missing Information in Deep Networks Using Backpropagated Gradients
To complement the learned information from activation-based representation, we propose utilizing a gradient-based representation that explicitly focuses on missing information.
Leveraging Adversarial Examples to Obtain Robust Second-Order Representations
Such a positioning scheme is based on a data point’s second-order property.
Distorted Representation Space Characterization Through Backpropagated Gradients
In this paper, we utilize weight gradients from backpropagation to characterize the representation space learned by deep learning algorithms.
Semantically Interpretable and Controllable Filter Sets
In this paper, we generate and control semantically interpretable filters that are directly learned from natural images in an unsupervised fashion.
Power of Tempospatially Unified Spectral Density for Perceptual Video Quality Assessment
This is a full-reference tempospatial approach that considers both temporal and spatial PSD characteristics.
CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition
We benchmark the performance of existing solutions in real-world scenarios and analyze the performance variation with respect to challenging conditions.
