Search Results for author:
Found 35 papers, 18 papers with code
Foundation Metrics for Evaluating Effectiveness of Healthcare Conversations Powered by Generative AI
The purpose of this paper is to explore state-of-the-art LLM-based evaluation metrics that are specifically applicable to the assessment of interactive conversational models in healthcare.
Affective Visual Dialog: A Large-Scale Benchmark for Emotional Reasoning Based on Visually Grounded Conversations
We introduce Affective Visual Dialog, an emotion explanation and reasoning task as a testbed for research on understanding the formation of emotions in visually grounded conversations.
Non-inferiority of Deep Learning Acute Ischemic Stroke Segmentation on Non-Contrast CT Compared to Expert Neuroradiologists
The optimized model trained on expert A was compared to test experts B and C. We used a one-sided Wilcoxon signed-rank test to test for the non-inferiority of the model-expert compared to the inter-expert agreement.
USE-Evaluator: Performance Metrics for Medical Image Segmentation Models with Uncertain, Small or Empty Reference Annotations
We study how uncertain, small, and empty reference annotations influence the value of metrics for medical image segmentation on an in-house data set regardless of the model.
ALBench: A Framework for Evaluating Active Learning in Object Detection
To facilitate the research in this field, this paper contributes an active learning benchmark framework named as ALBench for evaluating active learning in object detection.
YMIR: A Rapid Data-centric Development Platform for Vision Applications
This paper introduces an open source platform to support the rapid development of computer vision applications at scale.
Generative Modeling for Small-Data Object Detection
This paper explores object detection in the small data regime, where only a limited number of annotated bounding boxes are available due to data rarity and annotation expense.
Feature Partitioning for Efficient Multi-Task Architectures
Multi-task learning holds the promise of less data, parameters, and time than training of separate models.
Eidetic 3D LSTM: A Model for Video Prediction and Beyond
We first evaluate the E3D-LSTM network on widely-used future video prediction datasets and achieve the state-of-the-art performance.
Ranked #1 on
Video Prediction
on KTH
(Cond metric)
Composing Text and Image for Image Retrieval - An Empirical Odyssey
In this paper, we study the task of image retrieval, where the input query is specified in the form of an image plus some text that describes desired modifications to the input image.
Ranked #2 on
Image Retrieval with Multi-Modal Query
on MIT-States
Focal Visual-Text Attention for Memex Question Answering
In addition to a text answer, a few grounding photos are also given to justify the answer.
Ranked #1 on
Memex Question Answering
on MemexQA
Vision-Based Gait Analysis for Senior Care
As the senior population rapidly increases, it is challenging yet crucial to provide effective long-term care for seniors who live at home or in senior care facilities.
NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection
Comparing to standard Faster RCNN, it contains three highlights: an ensemble of two classification heads and a distillation head to avoid overfitting on noisy labels and improve the mining precision, masking the negative sample loss in box predictor to avoid the harm of false negative labels, and training box regression head only on seed annotations to eliminate the harm from inaccurate boundaries of mined bounding boxes.
Distributed Asynchronous Optimization with Unbounded Delays: How Slow Can You Go?
One of the most widely used optimization methods for large-scale machine learning problems is distributed asynchronous stochastic gradient descent (DASGD).
Focal Visual-Text Attention for Visual Question Answering
Recent insights on language and vision with neural networks have been successfully applied to simple single-image visual question answering.
Ranked #1 on
Memex Question Answering
on MemexQA
Iterative Visual Reasoning Beyond Convolutions
The framework consists of two core modules: a local module that uses spatial memory to store previous beliefs with parallel updates; and a global graph-reasoning module.
Attention-based Graph Neural Network for Semi-supervised Learning
Recently popularized graph neural networks achieve the state-of-the-art accuracy on a number of standard benchmark datasets for graph-based semi-supervised learning, improving significantly over existing approaches.
Ranked #10 on
Graph Regression
on Lipophilicity
AMC: AutoML for Model Compression and Acceleration on Mobile Devices
Model compression is a critical technique to efficiently deploy neural network models on mobile devices which have limited computation resources and tight power budgets.
A Goal-oriented Neural Conversation Model by Self-Play
One challenge in applying such techniques to building goal-oriented conversation models is that maximum likelihood-based models are not optimized toward accomplishing goals.
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels
Recent deep networks are capable of memorizing the entire data even when the labels are completely random.
Ranked #16 on
Image Classification
on WebVision-1000
Progressive Neural Architecture Search
We propose a new method for learning the structure of convolutional neural networks (CNNs) that is more efficient than recent state-of-the-art methods based on reinforcement learning and evolutionary algorithms.
Ranked #15 on
Neural Architecture Search
on NAS-Bench-201, ImageNet-16-120
(Accuracy (Val) metric)
Thoracic Disease Identification and Localization with Limited Supervision
Accurate identification and localization of abnormalities from radiology images play an integral part in clinical diagnosis and treatment planning.
Deep Reinforcement Learning-based Image Captioning with Embedding Reward
The policy network serves as a local guidance by providing the confidence of predicting the next word according to the current state.
Learning from Noisy Labels with Distillation
The ability of learning from noisy labels is very useful in many visual recognition tasks, as a vast amount of data with noisy labels are relatively easy to obtain.
Dense Captioning with Joint Inference and Visual Context
The goal is to densely detect visual concepts (e. g., objects, object parts, and interactions between them) from images, labeling each with a short descriptive phrase.
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
Despite progress in perceptual tasks such as image classification, computers still perform poorly on cognitive tasks such as image description and question answering.
Image Retrieval Using Scene Graphs
We introduce a novel dataset of 5, 000 human-generated scene graphs grounded to images and use this dataset to evaluate our method for image retrieval.
Best of Both Worlds: Human-Machine Collaboration for Object Annotation
This paper brings together the latest advancements in object detection and in crowd engineering into a principled framework for accurately and efficiently localizing objects in images.
YFCC100M: The New Data in Multimedia Research
We present the Yahoo Flickr Creative Commons 100 Million Dataset (YFCC100M), the largest public multimedia collection that has ever been released.
Multimedia Computers and Society H.3.7
Multi-view Face Detection Using Deep Convolutional Neural Networks
In this paper we propose Deep Dense Face Detector (DDFD), a method that does not require pose/landmark annotation and is able to detect faces in a wide range of orientations using a single model based on deep convolutional neural networks.
Visual Sentiment Prediction with Deep Convolutional Neural Networks
Images have become one of the most popular types of media through which users convey their emotions within online social networks.
Large-Scale Multi-Label Learning with Incomplete Label Assignments
Unlike prior works, our method can effectively and efficiently consider missing labels and label correlations simultaneously, and is very scalable, that has linear time complexities over the size of the data.
Co-localization in Real-World Images
In this paper, we tackle the problem of co-localization in real-world images.
Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification
Robust low-level image features have been proven to be effective representations for a variety of visual recognition tasks such as object recognition and scene classification; but pixels, or even local image patches, carry little semantic meanings.
Large Margin Learning of Upstream Scene Understanding Models
This paper presents a joint max-margin and max-likelihood learning method for upstream scene understanding models, in which latent topic discovery and prediction model estimation are closely coupled and well-balanced.
