Search Results for author:
Found 39 papers, 18 papers with code
Lifelong Event Detection with Knowledge Transfer
We focus on lifelong event detection as an exemplar case and propose a new problem formulation that is also generalizable to other IE tasks.
TrainFors: A Large Benchmark Training Dataset for Image Manipulation Detection and Localization
The evaluation datasets and metrics for image manipulation detection and localization (IMDL) research have been standardized.
Alexa, play with robot: Introducing the First Alexa Prize SimBot Challenge on Embodied AI
The Alexa Prize program has empowered numerous university students to explore, experiment, and showcase their talents in building conversational agents through challenges like the SocialBot Grand Challenge and the TaskBot Challenge.
AMPERE: AMR-Aware Prefix for Generation-Based Event Argument Extraction Model
However, existing generation-based EAE models mostly focus on problem re-formulation and prompt design, without incorporating additional information that has been shown to be effective for classification-based models, such as the abstract meaning representation (AMR) of the input passages.
Alexa Arena: A User-Centric Interactive Platform for Embodied AI
We introduce Alexa Arena, a user-centric simulation platform for Embodied AI (EAI) research.
AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model
In this work, we demonstrate that multilingual large-scale sequence-to-sequence (seq2seq) models, pre-trained on a mixture of denoising and Causal Language Modeling (CLM) tasks, are more efficient few-shot learners than decoder-only models on various tasks.
Ranked #14 on
Natural Language Inference
on CommitmentBank
MONet: Multi-scale Overlap Network for Duplication Detection in Biomedical Images
Manipulation of biomedical images to misrepresent experimental results has plagued the biomedical community for a while.
Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems
We present results from a large-scale experiment on pretraining encoders with non-embedding parameter counts ranging from 700M to 9. 3B, their subsequent distillation into smaller models ranging from 17M-170M parameters, and their application to the Natural Language Understanding (NLU) component of a virtual assistant system.
Cross-Lingual Natural Language Inference
intent-classification
+5
MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages
We present the MASSIVE dataset--Multilingual Amazon Slu resource package (SLURP) for Slot-filling, Intent classification, and Virtual assistant Evaluation.
Ranked #1 on
Slot Filling
on MASSIVE
A Thousand Words Are Worth More Than a Picture: Natural Language-Centric Outside-Knowledge Visual Question Answering
Outside-knowledge visual question answering (OK-VQA) requires the agent to comprehend the image, make use of relevant knowledge from the entire web, and digest all the information to answer the question.
Transform-Retrieve-Generate: Natural Language-Centric Outside-Knowledge Visual Question Answering
Most previous works address the problem by first fusing the image and question in the multi-modal space, which is inflexible for further fusion with a vast amount of external knowledge.
Ranked #19 on
Visual Question Answering (VQA)
on OK-VQA
BioFors: A Large Biomedical Image Forensics Dataset
Our results and analysis show that existing algorithms developed on common computer vision datasets are not robust when applied to biomedical images, validating that more research is required to address the unique challenges of biomedical image forensics.
DEGREE: A Data-Efficient Generation-Based Event Extraction Model
Given a passage and a manually designed prompt, DEGREE learns to summarize the events mentioned in the passage into a natural sentence that follows a predefined pattern.
SIGN: Spatial-information Incorporated Generative Network for Generalized Zero-shot Semantic Segmentation
Unlike conventional zero-shot classification, zero-shot semantic segmentation predicts a class label at the pixel level instead of the image level.
``Nice Try, Kiddo'': Investigating Ad Hominems in Dialogue Responses
Ad hominem attacks are those that target some feature of a person{'}s character instead of the position the person is maintaining.
Learning Better Visual Dialog Agents with Pretrained Visual-Linguistic Representation
Most existing work for Guesser encode the dialog history as a whole and train the Guesser models from scratch on the GuessWhat?!
Style-Aware Normalized Loss for Improving Arbitrary Style Transfer
Neural Style Transfer (NST) has quickly evolved from single-style to infinite-style models, also known as Arbitrary Style Transfer (AST).
MEG: Multi-Evidence GNN for Multimodal Semantic Forensics
The problem setup requires algorithms to perform multimodal semantic forensics to authenticate a query multimedia package using a reference dataset of potentially related packages as evidences.
Recurrent Convolutional Strategies for Face Manipulation Detection in Videos
The spread of misinformation through synthetically generated yet realistic images and videos has become a significant problem, calling for robust manipulation detection methods.
Image-to-GPS Verification Through A Bottom-Up Pattern Matching Network
The image-to-GPS verification problem asks whether a given image is taken at a claimed GPS location.
Policy Design for Active Sequential Hypothesis Testing using Deep Learning
Deep learning can be used as a tool for designing better heuristics in such problems.
A Byte-sized Approach to Named Entity Recognition
In biomedical literature, it is common for entity boundaries to not align with word boundaries.
BusterNet: Detecting Copy-Move Image Forgery with Source/Target Localization
We introduce a novel deep neural architecture for image copy-move forgery detection (CMFD), code-named BusterNet.
Deep Multimodal Image-Repurposing Detection
Nefarious actors on social media and other platforms often spread rumors and falsehoods through images whose metadata (e. g., captions) have been modified to provide visual substantiation of the rumor/falsehood.
Learn to Combine Modalities in Multimodal Deep Learning
Combining complementary information from multiple modalities is intuitively appealing for improving the performance of learning-based approaches.
A Sequential Embedding Approach for Item Recommendation with Heterogeneous Attributes
Our ablation experiments demonstrate the effectiveness of the two components to address heterogeneous attribute challenges including variable lengths and attribute sparseness.
Implicit Language Model in LSTM for OCR
Neural networks have become the technique of choice for OCR, but many aspects of how and why they deliver superior performance are still unknown.
WMRB: Learning to Rank in a Scalable Batch Training Approach
We propose a new learning to rank algorithm, named Weighted Margin-Rank Batch loss (WMRB), to extend the popular Weighted Approximate-Rank Pairwise loss (WARP).
A Batch Learning Framework for Scalable Personalized Ranking
In designing personalized ranking algorithms, it is desirable to encourage a high precision at the top of the ranked list.
Self-Organized Text Detection With Minimal Post-Processing via Border Learning
In this paper we propose a new solution to the text detection problem via border learning.
An Investigation into the Pedagogical Features of Documents
We refer to this learning utility as the "pedagogical value" of the document to the learner.
Deep Matching and Validation Network -- An End-to-End Solution to Constrained Image Splicing Localization and Detection
Here the task is to estimate the probability that the donor image has been used to splice the query image, and obtain the splicing masks for both the query and donor images.
Temporal Learning and Sequence Modeling for a Job Recommender System
We present our solution to the job recommendation task for RecSys Challenge 2016.
Pooling Faces: Template based Face Recognition with Pooled Face Images
We propose a novel approach to template based face recognition.
Pose-Aware Face Recognition in the Wild
We propose a method to push the frontiers of unconstrained face recognition in the wild, focusing on the problem of extreme pose variations.
Face Recognition Using Deep Multi-Pose Representations
In our representation, a face image is processed by several pose-specific deep convolutional neural network (CNN) models to generate multiple pose-specific features.
Ranked #14 on
Face Verification
on IJB-A
Facial Landmark Detection with Tweaked Convolutional Neural Networks
We present a novel convolutional neural network (CNN) design for facial landmark coordinate regression.
