22 accepted papers
We propose action-agnostic point-level (AAPL) supervision for temporal action detection to achieve accurate action instance detection with a lightly annotated dataset. In the proposed scheme, a small portion of video frames is sampled in an unsupervised manner and presented to human annotators, who…
Result diversification (RD) is a crucial technique in Text-to-Image Retrieval for enhancing the efficiency of a practical application. Conventional methods focus solely on increasing the diversity metric of image appearances. However, the diversity metric and its desired value vary depending on the
We present a novel self-supervised feature learning method using Vision Transformers (ViT) as the backbone, specifically designed for object detection and instance segmentation. Our approach addresses the challenge of extracting features that capture both class and positional information, which are…
Visual localization is an important sub-task in SfM and visual SLAM that involves estimating a 6-DoF camera pose for an input query image relative to a given 3D model of the environment. The most accurate approach is a hierarchical one that splits the task into two stages: image retrieval and camera…
Recent non-contrastive methods for self-supervised representation learning show promising performance. While they are attractive since they do not need negative samples, it necessitates some mechanism to avoid collapsing into a trivial solution. Currently, there are two approaches to collapse preven…
"Current state-of-the-art methods for image captioning employ region-based features, as they provide object-level information that is essential to describe the content of images; they are usually extracted by an object detector such as Faster R-CNN. However, they have several issues, such as lack of…
Visual exploration is a task that seeks to visit all the navigable areas of an environment as quickly as possible. The existing methods employ deep reinforcement learning (RL) as the standard tool for the task. However, they tend to be vulnerable to statistical shifts between the training and test d…
Bundle adjustment (BA) occupies a large portion of SfM and visual SLAM's total execution time. Local BA over the latest several keyframes plays a crucial role in visual SLAM. Its execution time should be sufficiently short for robust tracking; this is especially critical for embedded systems with a…
There is a growing interest in the community in making an embodied AI agent perform a complicated task while interacting with an environment following natural language directives. Recent studies have tackled the problem using ALFRED, a well-designed dataset for the task, but achieved only very low a…
This paper considers matching images of low-light scenes, aiming to widen the frontier of SfM and visual SLAM applications. Recent image sensors can record the brightness of scenes with more than eight-bit precision, available in their RAW-format image. We are interested in making full use of such h…
It has been a primary concern in recent studies of vision and language tasks to design an effective attention mechanism dealing with interactions between the two modalities. The Transformer has recently been extended and applied to several bi-modal tasks, yielding promising results. For visual dialo…
Underwater active landmarks (UALs) are widely used for short-range underwater navigation in underwater robotics tasks. Detection of UALs is challenging due to large variance of underwater illumination, water quality and change of camera viewpoint. Moreover, improvement of detection accuracy relies u…
Many studies have been conducted so far on image restoration, the problem of restoring a clean image from its distorted version. There are many different types of distortion affecting image quality. Previous studies have focused on single types of distortion, proposing methods for removing them. How…
In this paper, we study design of deep neural networks for tasks of image restoration. We propose a novel style of residual connections dubbed "dual residual connection", which exploits the potential of paired operations, e.g., up- and down-sampling or convolution with large- and small-size kernels.…
It is still challenging to build an AI system that can perform tasks that involve vision and language at human level. So far, researchers have singled out individual tasks separately, for each of which they have designed networks and trained them on its dedicated datasets. Although this approach has…
Recently, convolutional neural networks (CNNs) have shown great success on the task of monocular depth estimation. A fundamental yet unanswered question is: how CNNs can infer depth from a single image. Toward answering this question, we consider visualization of inference of a CNN by identifying re…
Researchers have applied deep neural networks to image restoration tasks, in which they proposed various network architectures, loss functions, and training methods. In particular, adversarial training, which is employed in recent studies, seems to be a key ingredient to success. In this paper, we s…
In this work, we address the problem of improving robustness of convolutional neural networks (CNNs) to image distortion. We argue that higher moment statistics of feature distributions can be shifted due to image distortion, and the shift leads to performance decrease and cannot be reduced by ordin…
A key solution to visual question answering (VQA) exists in how to fuse visual and language features extracted from an input image and question. We show that an attention mechanism that enables dense, bi-directional interactions between the two modalities contributes to boost accuracy of prediction…
In this paper we consider critical motion sequences (CMSs) of rolling-shutter (RS) SfM. Employing an RS camera model with linearized pure rotation, we show that the RS distortion can be approximately expressed by two internal parameters of an "imaginary" camera plus one-parameter nonlinear transform…
In this paper, we propose a binary tree architecture to truncate architecture of wide networks by reducing the width of the networks. More precisely, in the proposed architecture, the width is incrementally reduced from lower layers to higher layers in order to increase the expressive capacity of ne…
This paper presents a generic method for transforming MRFs for the marginal inference problem. Its major application is to downsize MRFs to speed up the computation. Unlike the MAP inference, there are only classical algorithms for the marginal inference problem such as BP etc. that require large co…