Search Results for author:
Found 14 papers, 9 papers with code
CLIP meets Model Zoo Experts: Pseudo-Supervision for Visual Enhancement
While CLIP is scalable, promptable, and robust to distribution shifts on image classification tasks, it lacks object localization capabilities.
SHARCS: Efficient Transformers through Routing with Dynamic Width Sub-networks
We introduce SHARCS for adaptive inference that takes into account the hardness of input samples.
Time Does Tell: Self-Supervised Time-Tuning of Dense Image Representations
Our paper aims to address this gap by proposing a novel approach that incorporates temporal consistency in dense self-supervised learning.
InForecaster: Forecasting Influenza Hemagglutinin Mutations Through the Lens of Anomaly Detection
This helps the model to learn a shared unique representation between normal training samples as much as possible, which improves the discernibility and detectability of mutated samples from the unmutated ones at the test time.
SwinCheX: Multi-label classification on chest X-ray images with transformers
Our model has been tested with several number of MLP layers for the head setting, each achieves a competitive AUC score on all classes.
Fake It Till You Make It: Towards Accurate Near-Distribution Novelty Detection
Effectiveness of our method for both the near-distribution and standard novelty detection is assessed through extensive experiments on datasets in diverse applications such as medical images, object classification, and quality control.
Ranked #2 on
Anomaly Detection
on One-class CIFAR-10
(using extra training data)
ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts
Our method, called ATTEMPT (ATTEntional Mixtures of Prompt Tuning), obtains source prompts as encodings of large-scale source tasks into a small number of parameters and trains an attention module to interpolate the source prompts and a newly initialized target prompt for every instance in the target task.
MERLOT Reserve: Neural Script Knowledge through Vision and Language and Sound
Given a video, we replace snippets of text and audio with a MASK token; the model learns by choosing the correct masked-out snippet.
Ranked #6 on
Action Classification
on Kinetics-600
(using extra training data)
A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges
To date, several research domains tackle the problem of detecting unfamiliar samples, including anomaly detection, novelty detection, one-class learning, open set recognition, and out-of-distribution detection.
Ano-Graph: Learning Normal Scene Contextual Graphs to Detect Video Anomalies
Video anomaly detection has proved to be a challenging task owing to its unsupervised training procedure and high spatio-temporal complexity existing in real-world scenarios.
Multiresolution Knowledge Distillation for Anomaly Detection
Unsupervised representation learning has proved to be a critical component of anomaly detection/localization in images.
Puzzle-AE: Novelty Detection in Images through Solving Puzzles
Puzzle-solving, as a pretext task of self-supervised learning (SSL) methods, has earlier proved its ability in learning semantically meaningful features.
Paraphrase Generation by Learning How to Edit from Samples
With its novel editor module, the model then paraphrases the input sequence by editing it using the extracted relations between the retrieved pair of sentences.
ARAE: Adversarially Robust Training of Autoencoders Improves Novelty Detection
To address this problem, we propose a novel AE that can learn more semantically meaningful features.
