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Found 22 papers, 10 papers with code
MAQA: A Multimodal QA Benchmark for Negation
Multimodal learning can benefit from the representation power of pretrained Large Language Models (LLMs).
Towards Efficient Neural Scene Graphs by Learning Consistency Fields
Neural Radiance Fields (NeRF) achieves photo-realistic image rendering from novel views, and the Neural Scene Graphs (NSG) \cite{ost2021neural} extends it to dynamic scenes (video) with multiple objects.
MuLan: A Joint Embedding of Music Audio and Natural Language
Music tagging and content-based retrieval systems have traditionally been constructed using pre-defined ontologies covering a rigid set of music attributes or text queries.
Bilateral Self-unbiased Learning from Biased Implicit Feedback
Implicit feedback has been widely used to build commercial recommender systems.
Future Transformer for Long-term Action Anticipation
The task of predicting future actions from a video is crucial for a real-world agent interacting with others.
Unconditional Image-Text Pair Generation with Multimodal Cross Quantizer
To learn a multimodal semantic correlation in a quantized space, we combine VQ-VAE with a Transformer encoder and apply an input masking strategy.
Boundary-aware Self-supervised Learning for Video Scene Segmentation
Inspired from this, we tackle video scene segmentation, which is a task of temporally localizing scene boundaries in a video, with a self-supervised learning framework where we mainly focus on designing effective pretext tasks.
A Conservative Approach for Unbiased Learning on Unknown Biases
Although convolutional neural networks (CNNs) achieve state-of-the-art in image classification, recent works address their unreliable predictions due to their excessive dependence on biased training data.
Continuous-Time Video Generation via Learning Motion Dynamics with Neural ODE
In order to perform unconditional video generation, we must learn the distribution of the real-world videos.
Translation-equivariant Image Quantizer for Bi-directional Image-Text Generation
Recently, vector-quantized image modeling has demonstrated impressive performance on generation tasks such as text-to-image generation.
Boundary-aware Pre-training for Video Scene Segmentation
Inspired from this, we tackle video scene segmentation, which is a task of temporally localizing scene boundaries in a video, with a self-supervised learning framework where we mainly focus on designing effective pretext tasks.
Session-aware Linear Item-Item Models for Session-based Recommendation
Session-based recommendation aims at predicting the next item given a sequence of previous items consumed in the session, e. g., on e-commerce or multimedia streaming services.
Local Collaborative Autoencoders
Top-N recommendation is a challenging problem because complex and sparse user-item interactions should be adequately addressed to achieve high-quality recommendation results.
A Hierarchical Multi-Modal Encoder for Moment Localization in Video Corpus
Identifying a short segment in a long video that semantically matches a text query is a challenging task that has important application potentials in language-based video search, browsing, and navigation.
Vid-ODE: Continuous-Time Video Generation with Neural Ordinary Differential Equation
Video generation models often operate under the assumption of fixed frame rates, which leads to suboptimal performance when it comes to handling flexible frame rates (e. g., increasing the frame rate of the more dynamic portion of the video as well as handling missing video frames).
Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing
Although essential to revealing biased performance, well intentioned attempts at algorithmic auditing can have effects that may harm the very populations these measures are meant to protect.
Computers and Society
REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs
As part of REFUGE, we have publicly released a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one.
N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification
Graph Convolutional Networks (GCNs) have shown significant improvements in semi-supervised learning on graph-structured data.
Ranked #41 on
Node Classification
on Pubmed
Network of Graph Convolutional Networks Trained on Random Walks
Graph Convolutional Networks (GCNs) are a recently proposed architecture which has had success in semi-supervised learning on graph-structured data.
YouTube-8M: A Large-Scale Video Classification Benchmark
Despite the size of the dataset, some of our models train to convergence in less than a day on a single machine using TensorFlow.
Ranked #1 on
Action Recognition In Videos
on ActivityNet
Personalized Academic Research Paper Recommendation System
To ease this difficulty, we propose a Personalized Academic Research Paper Recommendation System, which recommends related articles, for each researcher, that may be interesting to her/him.
Automatic Feature Induction for Stagewise Collaborative Filtering
Recent approaches to collaborative filtering have concentrated on estimating an algebraic or statistical model, and using the model for predicting missing ratings.
