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03/30/2023
Adaptive Cross Batch Normalization for Metric Learning
Metric learning is a fundamental problem in computer vision whereby a mo...
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12/22/2022
Understanding and Improving the Role of Projection Head in Self-Supervised Learning
Self-supervised learning (SSL) aims to produce useful feature representa...
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02/22/2022
Retrieval Augmented Classification for Long-Tail Visual Recognition
We introduce Retrieval Augmented Classification (RAC), a generic approac...
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03/25/2021
Few-shot Weakly-Supervised Object Detection via Directional Statistics
Detecting novel objects from few examples has become an emerging topic i...
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10/09/2020
Deep Learning Superpixel Semantic Segmentation with Transparent Initialization and Sparse Encoder
Even though deep learning greatly improves the performance of semantic s...
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10/06/2020
RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNs
Although 3D Convolutional Neural Networks (CNNs) are essential for most ...
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06/23/2020
Post-hoc Calibration of Neural Networks
Calibration of neural networks is a critical aspect to consider when inc...
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06/23/2020
Calibration of Neural Networks using Splines
Calibrating neural networks is of utmost importance when employing them ...
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06/22/2020
Bidirectional Self-Normalizing Neural Networks
The problem of exploding and vanishing gradients has been a long-standin...
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03/30/2020
Improved Gradient based Adversarial Attacks for Quantized Networks
Neural network quantization has become increasingly popular due to effic...
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03/18/2020
In Defense of Graph Inference Algorithms for Weakly Supervised Object Localization
Weakly Supervised Object Localization (WSOL) methods have become increas...
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10/24/2019
Fast and Differentiable Message Passing for Stereo Vision
Despite the availability of many Markov Random Field (MRF) optimization ...
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10/18/2019
Mirror Descent View for Neural Network Quantization
Quantizing large Neural Networks (NN) while maintaining the performance ...
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06/14/2019
A Signal Propagation Perspective for Pruning Neural Networks at Initialization
Network pruning is a promising avenue for compressing deep neural networ...
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04/05/2019
Learning to Adapt for Stereo
Real world applications of stereo depth estimation require models that a...
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02/27/2019
Continual Learning with Tiny Episodic Memories
Learning with less supervision is a major challenge in artificial intell...
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12/11/2018
Proximal Mean-field for Neural Network Quantization
Compressing large neural networks by quantizing the parameters, while ma...
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11/22/2018
Generalized Range Moves
We consider move-making algorithms for energy minimization of multi-labe...
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10/04/2018
SNIP: Single-shot Network Pruning based on Connection Sensitivity
Pruning large neural networks while maintaining the performance is often...
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05/23/2018
Efficient Relaxations for Dense CRFs with Sparse Higher Order Potentials
Dense conditional random fields (CRFs) with Gaussian pairwise potentials...
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04/17/2018
DGPose: Disentangled Semi-supervised Deep Generative Models for Human Body Analysis
Deep generative modelling for robust human body analysis is an emerging ...
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01/30/2018
Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence
We study the incremental learning problem for the classification task, a...
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11/29/2016
Efficient Linear Programming for Dense CRFs
The fully connected conditional random field (CRF) with Gaussian pairwis...
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11/24/2014