Posts by Collection

Feature Binding with Category-Dependant MixUp for Semantic Segmentation and Adversarial Robustness

Published in BMVC (Oral), 2020

We propose a new type of data augmentation for dense labelling tasks. We train neural networks to seperate and label mixed images based on their co-occurance probabilities.

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Shape or Texture: Understanding Discriminative Features in CNNs

Published in ICLR, 2021

We present two approaches for quantifying the shape and texture encoded in convolutional neural networks.

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SegMix: Co-occurrence Driven Mixup for Semantic Segmentation and Adversarial Robustness

Published in International Journal of Computer Vision, 2021

We present a strategy for training convolutional neural networks for the task of dense image labelling by blending images based on (i) categorical clustering or (ii) the co-occurrence likelihood of categories.

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Global Pooling, More than Meets the Eye: Position Information is Encoded Channel-Wise in CNNs

Published in ICCV, 2021

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. Specifically, we demonstrate that positional information is encoded based on the ordering of the channel dimensions, while semantic information is largely not.

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Simpler Does It: Generating Semantic Labels with Objectness Guidance

Published in BMVC, 2021

We design a novel, efficient, and robust weakly-supervised method for generating semantic segmentation pseudo-labels from CAMs or bounding boxes.

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A Deeper Dive Into What Deep Spatiotemporal Networks Encode: Quantifying Static vs. Dynamic Information

Published in CVPR, 2022

We quantify and explore the static and dynamic information contained in deep spatiotemporal networks.

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Quantifying and Learning Static vs. Dynamic Information in Deep Spatiotemporal Networks

Published in TPAMI, 2022

We quantify, and design algorithms to learn, static and dynamic information in deep spatiotemporal networks.

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Position, Padding and Predictions: A Deeper Look at Position Information in CNNs

Published in International Journal of Computer Vision, 2024

We explore the existance of absolute position information in CNNs in relation to their padding type or other border heuristics.

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Understanding Video Transformers via Universal Concept Discovery

Published in CVPR (Highlight), 2024

We design a method for discovering universal spatiotemporal concepts in deep video transformers.

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Visual Concept Connectome (VCC): Open World Concept Discovery and their Interlayer Connections in Deep Models

Published in CVPR (Highlight), 2024

We discover and quantify concept connections in multi-layer deep models.

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Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.