73 citations · 414 across the 36 of their papers we have counts for
42 papers
Simple Transferability Estimation for Regression Tasks
Cuong N. Nguyen, Phong Tran, Lam Si Tung Ho +4
We consider transferability estimation, the problem of estimating how well deep learning models transfer from a source to a target task. We focus on regression tasks, which receive…
MaLP: Manipulation Localization Using a Proactive Scheme
Vishal Asnani, Xi Yin, Tal Hassner +1
Advancements in the generation quality of various Generative Models (GMs) has made it necessary to not only perform binary manipulation detection but also localize the modified pix…
You Only Need a Good Embeddings Extractor to Fix Spurious Correlations
Raghav Mehta, Vítor Albiero, Li Chen +4
Spurious correlations in training data often lead to robustness issues since models learn to use them as shortcuts. For example, when predicting whether an object is a cow, a model…
A Whac-A-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies Others
Zhiheng Li, Ivan Evtimov, Albert Gordo +5
Machine learning models have been found to learn shortcuts -- unintended decision rules that are unable to generalize -- undermining models' reliability. Previous works address thi…
Task Grouping for Multilingual Text Recognition
Jing Huang, Kevin J Liang, Rama Kovvuri +1
Most existing OCR methods focus on alphanumeric characters due to the popularity of English and numbers, as well as their corresponding datasets. On extending the characters to mor…
GliTr: Glimpse Transformers with Spatiotemporal Consistency for Online Action Prediction
Samrudhdhi B Rangrej, Kevin J Liang, Tal Hassner +1
Many online action prediction models observe complete frames to locate and attend to informative subregions in the frames called glimpses and recognize an ongoing action based on g…
Generalization Bounds for Deep Transfer Learning Using Majority Predictor Accuracy
Cuong N. Nguyen, Lam Si Tung Ho, Vu Dinh +2
We analyze new generalization bounds for deep learning models trained by transfer learning from a source to a target task. Our bounds utilize a quantity called the majority predict…
Few-shot Learning with Noisy Labels
Kevin J Liang, Samrudhdhi B. Rangrej, Vladan Petrovic +1
Few-shot learning (FSL) methods typically assume clean support sets with accurately labeled samples when training on novel classes. This assumption can often be unrealistic: suppor…
Proactive Image Manipulation Detection
Vishal Asnani, Xi Yin, Tal Hassner +2
Image manipulation detection algorithms are often trained to discriminate between images manipulated with particular Generative Models (GMs) and genuine/real images, yet generalize…
FSGANv2: Improved Subject Agnostic Face Swapping and Reenactment
Yuval Nirkin, Yosi Keller, Tal Hassner
We present Face Swapping GAN (FSGAN) for face swapping and reenactment. Unlike previous work, we offer a subject agnostic swapping scheme that can be applied to pairs of faces with…
Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection
Koby Bibas, Meir Feder, Tal Hassner
Detecting out-of-distribution (OOD) samples is vital for developing machine learning based models for critical safety systems. Common approaches for OOD detection assume access to…
TextStyleBrush: Transfer of Text Aesthetics from a Single Example
Praveen Krishnan, Rama Kovvuri, Guan Pang +2
We present a novel approach for disentangling the content of a text image from all aspects of its appearance. The appearance representation we derive can then be applied to new con…
Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Images
Vishal Asnani, Xi Yin, Tal Hassner +1
State-of-the-art (SOTA) Generative Models (GMs) can synthesize photo-realistic images that are hard for humans to distinguish from genuine photos. Identifying and understanding man…
TextOCR: Towards large-scale end-to-end reasoning for arbitrary-shaped scene text
Amanpreet Singh, Guan Pang, Mandy Toh +3
A crucial component for the scene text based reasoning required for TextVQA and TextCaps datasets involve detecting and recognizing text present in the images using an optical char…
A Multiplexed Network for End-to-End, Multilingual OCR
Jing Huang, Guan Pang, Rama Kovvuri +5
Recent advances in OCR have shown that an end-to-end (E2E) training pipeline that includes both detection and recognition leads to the best results. However, many existing methods…
HyperSeg: Patch-wise Hypernetwork for Real-time Semantic Segmentation
Yuval Nirkin, Lior Wolf, Tal Hassner
We present a novel, real-time, semantic segmentation network in which the encoder both encodes and generates the parameters (weights) of the decoder. Furthermore, to allow maximal…
img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation
Vítor Albiero, Xingyu Chen, Xi Yin +2
We propose real-time, six degrees of freedom (6DoF), 3D face pose estimation without face detection or landmark localization. We observe that estimating the 6DoF rigid transformati…
DeepFake Detection Based on the Discrepancy Between the Face and its Context
Yuval Nirkin, Lior Wolf, Yosi Keller +1
We propose a method for detecting face swapping and other identity manipulations in single images. Face swapping methods, such as DeepFake, manipulate the face region, aiming to ad…
Mask TextSpotter v3: Segmentation Proposal Network for Robust Scene Text Spotting
Minghui Liao, Guan Pang, Jing Huang +2
Recent end-to-end trainable methods for scene text spotting, integrating detection and recognition, showed much progress. However, most of the current arbitrary-shape scene text sp…
LEEP: A New Measure to Evaluate Transferability of Learned Representations
Cuong V. Nguyen, Tal Hassner, Matthias Seeger +1
We introduce a new measure to evaluate the transferability of representations learned by classifiers. Our measure, the Log Expected Empirical Prediction (LEEP), is simple and easy…
Balancing Specialization, Generalization, and Compression for Detection and Tracking
Dotan Kaufman, Koby Bibas, Eran Borenstein +2
We propose a method for specializing deep detectors and trackers to restricted settings. Our approach is designed with the following goals in mind: (a) Improving accuracy in restri…
Transferability and Hardness of Supervised Classification Tasks
Anh T. Tran, Cuong V. Nguyen, Tal Hassner
We propose a novel approach for estimating the difficulty and transferability of supervised classification tasks. Unlike previous work, our approach is solution agnostic and does n…
FSGAN: Subject Agnostic Face Swapping and Reenactment
Yuval Nirkin, Yosi Keller, Tal Hassner
We present Face Swapping GAN (FSGAN) for face swapping and reenactment. Unlike previous work, FSGAN is subject agnostic and can be applied to pairs of faces without requiring train…
Toward Understanding Catastrophic Forgetting in Continual Learning
Cuong V. Nguyen, Alessandro Achille, Michael Lam +3
We study the relationship between catastrophic forgetting and properties of task sequences. In particular, given a sequence of tasks, we would like to understand which properties o…