11 accepted papers
Recent advancements in pre-trained vision models have made them pivotal in computer vision, emphasizing the need for their thorough evaluation and benchmarking. This evaluation needs to consider various factors of variation, their potential biases, shortcuts, and inaccuracies that ultimately lead to…
In this paper, we exploit memory-augmented neural networks to predict accurate answers to visual questions, even when those answers rarely occur in the training set. The memory network incorporates both internal and external memory blocks and selectively pays attention to each training exemplar. We…
This paper addresses the task of set prediction using deep learning. This is important because the output of many computer vision tasks, including image tagging and object detection, are naturally expressed as sets of entities rather than vectors. As opposed to a vector, the size of a set is not fix…
This paper presents an infinite variational autoencoder (VAE) whose capacity adapts to suit the input data. This is achieved using a mixture model where the mixing coefficients are modeled by a Dirichlet process, allowing us to integrate over the coefficients when performing inference. Critically, t…
We propose a method for visual question answering which combines an internal representation of the content of an image with information extracted from a general knowledge base to answer a broad range of image-based questions. This allows more complex questions to be answered using the predominant ne…
Matching between two sets of objects is typically approached by finding the object pairs that collectively maximize the joint matching score. In this paper, we argue that this single solution does not necessarily lead to the optimal matching accuracy and that general one-to-one assignment problems c…
We explore the capabilities of Auto-Encoders to fuse the information available from cameras and depth sensors, and to reconstruct missing data, for scene understanding tasks. In particular we consider three input modalities: RGB images; depth images; and semantic label information. We seek to genera…
Much recent progress in Vision-to-Language (V2L) problems has been achieved through a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). This approach does not explicitly represent high-level semantic concepts, but rather seeks to progress directly from image f…
We propose a semantic scene understanding system that is suitable for real robotic operations. The system solves different tasks (semantic segmentation and object detections) in an opportunistic and distributed fashion but still allows communication between modules to improve their respective perfor…
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. The key advantage of this approach is that it makes JPDA computationally tractable in applicatio…
We propose a method to recover the structure of a compound scene from multiple silhouettes. Structure is expressed as a collection of 3D primitives chosen from a pre-defined library, each with an associated pose. This has several advantages over a volume or mesh representation both for estimation an…