849 citations · 1.1k across the 20 of their papers we have counts for
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SlowFormer: Universal Adversarial Patch for Attack on Compute and Energy Efficiency of Inference Efficient Vision Transformers
KL Navaneet, Soroush Abbasi Koohpayegani, Essam Sleiman +1
Recently, there has been a lot of progress in reducing the computation of deep models at inference time. These methods can reduce both the computational needs and power usage of de…
Defending Against Patch-based Backdoor Attacks on Self-Supervised Learning
Ajinkya Tejankar, Maziar Sanjabi, Qifan Wang +4
Recently, self-supervised learning (SSL) was shown to be vulnerable to patch-based data poisoning backdoor attacks. It was shown that an adversary can poison a small part of the un…
Backdoor Attacks on Vision Transformers
Akshayvarun Subramanya, Aniruddha Saha, Soroush Abbasi Koohpayegani +2
Vision Transformers (ViT) have recently demonstrated exemplary performance on a variety of vision tasks and are being used as an alternative to CNNs. Their design is based on a sel…
SimA: Simple Softmax-free Attention for Vision Transformers
Soroush Abbasi Koohpayegani, Hamed Pirsiavash
Recently, vision transformers have become very popular. However, deploying them in many applications is computationally expensive partly due to the Softmax layer in the attention b…
A Simple Approach to Adversarial Robustness in Few-shot Image Classification
Akshayvarun Subramanya, Hamed Pirsiavash
Few-shot image classification, where the goal is to generalize to tasks with limited labeled data, has seen great progress over the years. However, the classifiers are vulnerable t…
SimReg: Regression as a Simple Yet Effective Tool for Self-supervised Knowledge Distillation
K L Navaneet, Soroush Abbasi Koohpayegani, Ajinkya Tejankar +1
Feature regression is a simple way to distill large neural network models to smaller ones. We show that with simple changes to the network architecture, regression can outperform m…
A Fistful of Words: Learning Transferable Visual Models from Bag-of-Words Supervision
Ajinkya Tejankar, Maziar Sanjabi, Bichen Wu +4
Using natural language as a supervision for training visual recognition models holds great promise. Recent works have shown that if such supervision is used in the form of alignmen…
Constrained Mean Shift Using Distant Yet Related Neighbors for Representation Learning
KL Navaneet, Soroush Abbasi Koohpayegani, Ajinkya Tejankar +3
We are interested in representation learning in self-supervised, supervised, and semi-supervised settings. Some recent self-supervised learning methods like mean-shift (MSF) cluste…
MASTAF: A Model-Agnostic Spatio-Temporal Attention Fusion Network for Few-shot Video Classification
Rex Liu, Huanle Zhang, Hamed Pirsiavash +1
We propose MASTAF, a Model-Agnostic Spatio-Temporal Attention Fusion network for few-shot video classification. MASTAF takes input from a general video spatial and temporal represe…
Adaptive Token Sampling For Efficient Vision Transformers
Mohsen Fayyaz, Soroush Abbasi Koohpayegani, Farnoush Rezaei Jafari +5
While state-of-the-art vision transformer models achieve promising results in image classification, they are computationally expensive and require many GFLOPs. Although the GFLOPs…
Constrained Mean Shift for Representation Learning
Ajinkya Tejankar, Soroush Abbasi Koohpayegani, Hamed Pirsiavash
We are interested in representation learning from labeled or unlabeled data. Inspired by recent success of self-supervised learning (SSL), we develop a non-contrastive representati…
A Simple Baseline for Low-Budget Active Learning
Kossar Pourahmadi, Parsa Nooralinejad, Hamed Pirsiavash
Active learning focuses on choosing a subset of unlabeled data to be labeled. However, most such methods assume that a large subset of the data can be annotated. We are interested…
Consistent Explanations by Contrastive Learning
Vipin Pillai, Soroush Abbasi Koohpayegani, Ashley Ouligian +2
Post-hoc explanation methods, e.g., Grad-CAM, enable humans to inspect the spatial regions responsible for a particular network decision. However, it is shown that such explanation…
Backdoor Attacks on Self-Supervised Learning
Aniruddha Saha, Ajinkya Tejankar, Soroush Abbasi Koohpayegani +1
Large-scale unlabeled data has spurred recent progress in self-supervised learning methods that learn rich visual representations. State-of-the-art self-supervised methods for lear…
Mean Shift for Self-Supervised Learning
Soroush Abbasi Koohpayegani, Ajinkya Tejankar, Hamed Pirsiavash
Most recent self-supervised learning (SSL) algorithms learn features by contrasting between instances of images or by clustering the images and then contrasting between the image c…
ISD: Self-Supervised Learning by Iterative Similarity Distillation
Ajinkya Tejankar, Soroush Abbasi Koohpayegani, Vipin Pillai +2
Recently, contrastive learning has achieved great results in self-supervised learning, where the main idea is to push two augmentations of an image (positive pairs) closer compared…
COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning
Simon Ging, Mohammadreza Zolfaghari, Hamed Pirsiavash +1
Many real-world video-text tasks involve different levels of granularity, such as frames and words, clip and sentences or videos and paragraphs, each with distinct semantics. In th…
CompRess: Self-Supervised Learning by Compressing Representations
Soroush Abbasi Koohpayegani, Ajinkya Tejankar, Hamed Pirsiavash
Self-supervised learning aims to learn good representations with unlabeled data. Recent works have shown that larger models benefit more from self-supervised learning than smaller…
A simple baseline for domain adaptation using rotation prediction
Ajinkya Tejankar, Hamed Pirsiavash
Recently, domain adaptation has become a hot research area with lots of applications. The goal is to adapt a model trained in one domain to another domain with scarce annotated dat…
Role of Spatial Context in Adversarial Robustness for Object Detection
Aniruddha Saha, Akshayvarun Subramanya, Koninika Patil +1
The benefits of utilizing spatial context in fast object detection algorithms have been studied extensively. Detectors increase inference speed by doing a single forward pass per i…
Hidden Trigger Backdoor Attacks
Aniruddha Saha, Akshayvarun Subramanya, Hamed Pirsiavash
With the success of deep learning algorithms in various domains, studying adversarial attacks to secure deep models in real world applications has become an important research topi…
Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs
Soheil Kolouri, Aniruddha Saha, Hamed Pirsiavash +1
The unprecedented success of deep neural networks in many applications has made these networks a prime target for adversarial exploitation. In this paper, we introduce a benchmark…
Fooling Network Interpretation in Image Classification
Akshayvarun Subramanya, Vipin Pillai, Hamed Pirsiavash
Deep neural networks have been shown to be fooled rather easily using adversarial attack algorithms. Practical methods such as adversarial patches have been shown to be extremely e…
Boosting Self-Supervised Learning via Knowledge Transfer
Mehdi Noroozi, Ananth Vinjimoor, Paolo Favaro +1
In self-supervised learning, one trains a model to solve a so-called pretext task on a dataset without the need for human annotation. The main objective, however, is to transfer th…