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Found 14 papers, 2 papers with code
Visual Concept Connectome (VCC): Open World Concept Discovery and their Interlayer Connections in Deep Models
Understanding what deep network models capture in their learned representations is a fundamental challenge in computer vision.
Multi-modal News Understanding with Professionally Labelled Videos (ReutersViLNews)
Towards a solution for designing this ability in algorithms, we present a large-scale analysis on an in-house dataset collected by the Reuters News Agency, called Reuters Video-Language News (ReutersViLNews) dataset which focuses on high-level video-language understanding with an emphasis on long-form news.
Understanding Video Transformers via Universal Concept Discovery
Concretely, we seek to explain the decision-making process of video transformers based on high-level, spatiotemporal concepts that are automatically discovered.
Quantifying and Learning Static vs. Dynamic Information in Deep Spatiotemporal Networks
(ii) Some datasets that are assumed to be biased toward dynamics are actually biased toward static information.
A Deeper Dive Into What Deep Spatiotemporal Networks Encode: Quantifying Static vs. Dynamic Information
To show the efficacy of our approach, we analyse two widely studied tasks, action recognition and video object segmentation.
Simpler Does It: Generating Semantic Labels with Objectness Guidance
Extensive experiments demonstrate the high quality of our generated pseudo-labels and effectiveness of the proposed framework in a variety of domains.
SegMix: Co-occurrence Driven Mixup for Semantic Segmentation and Adversarial Robustness
The premise is based on the notion of feature binding, which is defined as the process by which activations spread across space and layers in the network are successfully integrated to arrive at a correct inference decision.
Global Pooling, More than Meets the Eye: Position Information is Encoded Channel-Wise in CNNs
In this paper, we challenge the common assumption that collapsing the spatial dimensions of a 3D (spatial-channel) tensor in a convolutional neural network (CNN) into a vector via global pooling removes all spatial information.
Position, Padding and Predictions: A Deeper Look at Position Information in CNNs
; (ii) Does position encoding affect the learning of semantic representations?
Shape or Texture: Understanding Discriminative Features in CNNs
Contrasting the previous evidence that neurons in the later layers of a Convolutional Neural Network (CNN) respond to complex object shapes, recent studies have shown that CNNs actually exhibit a `texture bias': given an image with both texture and shape cues (e. g., a stylized image), a CNN is biased towards predicting the category corresponding to the texture.
Boundary Effects in CNNs: Feature or Bug?
Finally, we demonstrate the implications of these findings on a number of real-world tasks to show that position information can act as a feature or a bug.
Shape or Texture: Disentangling Discriminative Features in CNNs
Contrasting the previous evidence that neurons in the later layers of a Convolutional Neural Network (CNN) respond to complex object shapes, recent studies have shown that CNNs actually exhibit a 'texture bias': given an image with both texture and shape cues (e. g., a stylized image), a CNN is biased towards predicting the category corresponding to the texture.
Feature Binding with Category-Dependant MixUp for Semantic Segmentation and Adversarial Robustness
In this paper, we present a strategy for training convolutional neural networks to effectively resolve interference arising from competing hypotheses relating to inter-categorical information throughout the network.
Region Tracking in an Image Sequence: Preventing Driver Inattention
Driver inattention is a large problem on the roads around the world.
