Search Results for author:
Found 15 papers, 5 papers with code
Modality-specific Distillation
In this paper, we propose modality-specific distillation (MSD) to effectively transfer knowledge from a teacher on multimodal datasets.
ER-TEST: Evaluating Explanation Regularization Methods for NLP Models
Plus, little is understood about how ER model performance is affected by the choice of ER criteria or by the number/choice of training instances with human rationales.
Detecting and Understanding Harmful Memes: A Survey
One interesting finding is that many types of harmful memes are not really studied, e. g., such featuring self-harm and extremism, partly due to the lack of suitable datasets.
Detection, Disambiguation, Re-ranking: Autoregressive Entity Linking as a Multi-Task Problem
Without the use of a knowledge base or candidate sets, our model sets a new state of the art in two benchmark datasets of entity linking: COMETA in the biomedical domain, and AIDA-CoNLL in the news domain.
Understanding Failure Modes of Self-Supervised Learning
In this paper, we tackle these issues and study the representation space of self-supervised models by understanding the underlying reasons for misclassifications in a downstream task.
BARACK: Partially Supervised Group Robustness With Guarantees
Theoretically, we provide generalization bounds for our approach in terms of the worst-group performance, which scale with respect to both the total number of training points and the number of training points with group labels.
A Fistful of Words: Learning Transferable Visual Models from Bag-of-Words Supervision
In this paper, we focus on teasing out what parts of the language supervision are essential for training zero-shot image classification models.
UNIREX: A Unified Learning Framework for Language Model Rationale Extraction
An extractive rationale explains a language model's (LM's) prediction on a given task instance by highlighting the text inputs that most influenced the prediction.
Detecting Propaganda Techniques in Memes
We further create and release a new corpus of 950 memes, carefully annotated with 22 propaganda techniques, which can appear in the text, in the image, or in both.
SemEval-2021 Task 6: Detection of Persuasion Techniques in Texts and Images
We describe SemEval-2021 task 6 on Detection of Persuasion Techniques in Texts and Images: the data, the annotation guidelines, the evaluation setup, the results, and the participating systems.
A Survey on Multimodal Disinformation Detection
Recent years have witnessed the proliferation of fake news, propaganda, misinformation, and disinformation online.
MSD: Saliency-aware Knowledge Distillation for Multimodal Understanding
The idea aims at mimicking a teacher's modality-specific predictions by introducing auxiliary loss terms for each modality.
Adversarial Evaluation of Multimodal Models under Realistic Gray Box Assumption
This work examines the vulnerability of multimodal (image + text) models to adversarial threats similar to those discussed in previous literature on unimodal (image- or text-only) models.
The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes
This work proposes a new challenge set for multimodal classification, focusing on detecting hate speech in multimodal memes.
Supervised Multimodal Bitransformers for Classifying Images and Text
Self-supervised bidirectional transformer models such as BERT have led to dramatic improvements in a wide variety of textual classification tasks.
