Search Results for author:
Found 23 papers, 3 papers with code
EBMs vs. CL: Exploring Self-Supervised Visual Pretraining for Visual Question Answering
In a simple setting similar to CLEVR, we find that CL representations also improve systematic generalization, and even match the performance of representations from a larger, supervised, ImageNet-pretrained model.
Reasoning over Vision and Language: Exploring the Benefits of Supplemental Knowledge
The technique brings clear benefits to knowledge-demanding question answering tasks (OK-VQA, FVQA) by capturing semantic and relational knowledge absent from existing models.
Visual Question Answering with Prior Class Semantics
We present a novel mechanism to embed prior knowledge in a model for visual question answering.
Joint Learning of Set Cardinality and State Distribution
We present a novel approach for learning to predict sets using deep learning.
Visual Question Answering with Memory-Augmented Networks
In this paper, we exploit a memory-augmented neural network to predict accurate answers to visual questions, even when those answers occur rarely in the training set.
Bayesian Conditional Generative Adverserial Networks
Traditional GANs use a deterministic generator function (typically a neural network) to transform a random noise input $z$ to a sample $\mathbf{x}$ that the discriminator seeks to distinguish.
DeepSetNet: Predicting Sets with Deep Neural Networks
This paper addresses the task of set prediction using deep learning.
Infinite Variational Autoencoder for Semi-Supervised Learning
This paper presents an infinite variational autoencoder (VAE) whose capacity adapts to suit the input data.
Visual Question Answering: A Survey of Methods and Datasets
Visual Question Answering (VQA) is a challenging task that has received increasing attention from both the computer vision and the natural language processing communities.
FVQA: Fact-based Visual Question Answering
We evaluate several baseline models on the FVQA dataset, and describe a novel model which is capable of reasoning about an image on the basis of supporting facts.
Ranked #2 on
Visual Question Answering (VQA)
on F-VQA
Joint Probabilistic Matching Using m-Best Solutions
Matching between two sets of objects is typically approached by finding the object pairs that collectively maximize the joint matching score.
Online Multi-Target Tracking Using Recurrent Neural Networks
Here, we propose for the first time, an end-to-end learning approach for online multi-target tracking.
Image Captioning and Visual Question Answering Based on Attributes and External Knowledge
Much recent progress in Vision-to-Language problems has been achieved through a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
Joint Probabilistic Data Association Revisited
In this paper, we revisit the joint probabilistic data association (JPDA) technique and propose a novel solution based on recent developments in finding the m-best solutions to an integer linear program.
Ask Me Anything: Free-form Visual Question Answering Based on Knowledge from External Sources
Priming a recurrent neural network with this combined information, and the submitted question, leads to a very flexible visual question answering approach.
Explicit Knowledge-based Reasoning for Visual Question Answering
We describe a method for visual question answering which is capable of reasoning about contents of an image on the basis of information extracted from a large-scale knowledge base.
Online Metric-Weighted Linear Representations for Robust Visual Tracking
Object identification results for an entire video sequence are achieved by systematically combining the tracking information and visual recognition at each frame.
What value do explicit high level concepts have in vision to language problems?
Much of the recent progress in Vision-to-Language (V2L) problems has been achieved through a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
Part-Based Modelling of Compound Scenes From Images
We propose a method to recover the structure of a compound scene from multiple silhouettes.
Deconstruction of compound objects from image sets
We propose a method to recover the structure of a compound object from multiple silhouettes.
Context-Aware Hypergraph Construction for Robust Spectral Clustering
Using both CAHSM and DHPC, a robust spectral clustering algorithm is developed.
Contextual Hypergraph Modelling for Salient Object Detection
In this work, we model an image as a hypergraph that utilizes a set of hyperedges to capture the contextual properties of image pixels or regions.
Learning Compact Binary Codes for Visual Tracking
A key problem in visual tracking is to represent the appearance of an object in a way that is robust to visual changes.
