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04/24/2023
A Cookbook of Self-Supervised Learning
Self-supervised learning, dubbed the dark matter of intelligence, is a p...
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04/04/2023
Defending Against Patch-based Backdoor Attacks on Self-Supervised Learning
Recently, self-supervised learning (SSL) was shown to be vulnerable to p...
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10/26/2022
Is Multi-Task Learning an Upper Bound for Continual Learning?
Continual and multi-task learning are common machine learning approaches...
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06/17/2022
SimA: Simple Softmax-free Attention for Vision Transformers
Recently, vision transformers have become very popular. However, deployi...
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06/16/2022
Backdoor Attacks on Vision Transformers
Vision Transformers (ViT) have recently demonstrated exemplary performan...
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06/16/2022
PRANC: Pseudo RAndom Networks for Compacting deep models
Communication becomes a bottleneck in various distributed Machine Learni...
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04/11/2022
A Simple Approach to Adversarial Robustness in Few-shot Image Classification
Few-shot image classification, where the goal is to generalize to tasks ...
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03/12/2022
Sparsity and Heterogeneous Dropout for Continual Learning in the Null Space of Neural Activations
Continual/lifelong learning from a non-stationary input data stream is a...
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02/18/2022
Amenable Sparse Network Investigator
As the optimization problem of pruning a neural network is nonconvex and...
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01/13/2022
SimReg: Regression as a Simple Yet Effective Tool for Self-supervised Knowledge Distillation
Feature regression is a simple way to distill large neural network model...
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12/27/2021
A Fistful of Words: Learning Transferable Visual Models from Bag-of-Words Supervision
Using natural language as a supervision for training visual recognition ...
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12/08/2021
Constrained Mean Shift Using Distant Yet Related Neighbors for Representation Learning
We are interested in representation learning in self-supervised, supervi...
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12/08/2021
STAF: A Spatio-Temporal Attention Fusion Network for Few-shot Video Classification
We propose STAF, a Spatio-Temporal Attention Fusion network for few-shot...
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11/30/2021
ATS: Adaptive Token Sampling For Efficient Vision Transformers
While state-of-the-art vision transformer models achieve promising resul...
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10/22/2021
A Simple Baseline for Low-Budget Active Learning
Active learning focuses on choosing a subset of unlabeled data to be lab...
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10/19/2021
Constrained Mean Shift for Representation Learning
We are interested in representation learning from labeled or unlabeled d...
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10/01/2021
Consistent Explanations by Contrastive Learning
Understanding and explaining the decisions of neural networks are critic...
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05/21/2021
Backdoor Attacks on Self-Supervised Learning
Large-scale unlabeled data has allowed recent progress in self-supervise...
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05/15/2021
Mean Shift for Self-Supervised Learning
Most recent self-supervised learning (SSL) algorithms learn features by ...
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12/16/2020
ISD: Self-Supervised Learning by Iterative Similarity Distillation
Recently, contrastive learning has achieved great results in self-superv...
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11/01/2020
COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning
Many real-world video-text tasks involve different levels of granularity...
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10/28/2020
CompRess: Self-Supervised Learning by Compressing Representations
Self-supervised learning aims to learn good representations with unlabel...
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12/26/2019
A simple baseline for domain adaptation using rotation prediction
Recently, domain adaptation has become a hot research area with lots of ...
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09/30/2019
Adversarial Patches Exploiting Contextual Reasoning in Object Detection
The usefulness of spatial context in most fast object detection algorith...
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09/30/2019
Hidden Trigger Backdoor Attacks
With the success of deep learning algorithms in various domains, studyin...
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06/26/2019
Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs
The unprecedented success of deep neural networks in various application...
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12/06/2018
Towards Hiding Adversarial Examples from Network Interpretation
Deep networks have been shown to be fooled rather easily using adversari...
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05/01/2018
Boosting Self-Supervised Learning via Knowledge Transfer
In self-supervised learning, one trains a model to solve a so-called pre...
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08/22/2017
Representation Learning by Learning to Count
We introduce a novel method for representation learning that uses an art...
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11/24/2016
Weakly Supervised Cascaded Convolutional Networks
Object detection is a challenging task in visual understanding domain, a...
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10/27/2016
Cross-Modal Scene Networks
People can recognize scenes across many different modalities beyond natu...
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09/08/2016
Generating Videos with Scene Dynamics
We capitalize on large amounts of unlabeled video in order to learn a mo...
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08/10/2016
DeepCAMP: Deep Convolutional Action & Attribute Mid-Level Patterns
The recognition of human actions and the determination of human attribut...
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07/25/2016
Learning Aligned Cross-Modal Representations from Weakly Aligned Data
People can recognize scenes across many different modalities beyond natu...
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04/25/2016
Joint Semantic Segmentation and Depth Estimation with Deep Convolutional Networks
Multi-scale deep CNNs have been used successfully for problems mapping e...
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04/29/2015
Anticipating Visual Representations from Unlabeled Video
Anticipating actions and objects before they start or appear is a diffic...
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02/19/2015
Visualizing Object Detection Features
We introduce algorithms to visualize feature spaces used by object detec...
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10/17/2014
Learning visual biases from human imagination
Although the human visual system can recognize many concepts under chall...
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06/20/2014
Predicting Motivations of Actions by Leveraging Text
Understanding human actions is a key problem in computer vision. However...
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11/25/2013
