34 accepted papers
Despite recent progress in 3D self-supervised learning, collecting large-scale 3D scene scans remains expensive and labor-intensive. In this work, we investigate whether 3D representations can be learned from unlabeled videos recorded without any real 3D sensors. We present Laplacian-Aware Multi-lev
Density ratio estimation is a core concept in statistical machine learning because it provides a unified mechanism for tasks such as importance weighting, divergence estimation, and likelihood-free inference, but its potential in vision and language models has not been fully explored. Modern vision-
Hand-object interaction (HOI) involves dynamics where human manipulations produce spatio-temporal effects on objects. However, existing semantic HOI benchmarks focus on either manipulation or effects at a coarse level, lacking fine-grained spatio-temporal reasoning to capture HOI dynamics. We introd
Contrastive pre-training frameworks such as CLIP have demonstrated impressive zero-shot performance across a range of vision-language tasks. Recent studies have shown that aligning individual text tokens with specific image patches or regions enhances fine-grained compositional understanding. Howeve
Salient object detection exemplifies data-bounded tasks where expensive pixel-precise annotations force separate model training for related subtasks like DIS and HR-SOD. We present a method that dramatically improves generalization through large-scale synthetic data generation and ambiguity-aware ar…
Precise automated understanding of agricultural tasks such as disease identification is essential for the sustainable crop production. Recent advances in vision-language models (VLMs) are expected to further expand the range of agricultural tasks by facilitating human-model interaction through easy,…
Wildlife observation plays an important role in biodiversity conservation, necessitating robust methodologies for monitoring wildlife populations and interspecies interactions. Recent advances in computer vision have significantly contributed to automating fundamental wildlife observation tasks, suc…
Pre-trained Vision-Language Models (VLMs) exhibit strong generalization capabilities, enabling them to recognize a wide range of objects across diverse domains without additional training. However, they often retain irrelevant information beyond the requirements of specific target downstream tasks,…
In this paper, we propose a novel formula-driven supervised learning (FDSL) framework for pre-training an environmental sound analysis model by leveraging acoustic signals parametrically synthesized through formula-driven methods. Specifically, we outline detailed procedures and evaluate their effec…
"Throughout the history of computer vision, while research has explored the integration of images (visual) and point clouds (geometric), many advancements in image and 3D object recognition have tended to process these modalities separately. We aim to bridge this divide by integrating images and poi…
In outdoor environments, Vision-and-Language Navigation (VLN) requires an agent to rely on multi-modal cues from real-world urban environments and natural language instructions. While existing outdoor VLN models predict actions using a combination of panorama and instruction features, this approach…
Out-of-distribution (OOD) detection, which aims to determine whether an input is outside the training data distribution or not, is an indispensable task in many computer vision applications. In many of the previous studies on OOD detection for image classification, the class-conditional distribution…
"In this work, we investigate the understudied effect of the training data used for image super-resolution (SR). Most commonly, novel SR methods are developed and benchmarked on common training datasets such as DIV2K and DF2K. However, we investigate and rethink the training data from the perspectiv…
Visual inspection robots used in factories and outdoor environments require the ability to accurately recognize visual differences between similar objects and further verbalize the recognition results to present the differences to humans. Despite the application of Large Language Models (LLMs) and m…
Diffusion Models (DMs) have shown remarkable capabilities in various image-generation tasks. However there are growing concerns that DMs could be used to imitate unauthorized creations and thus raise copyright issues. To address this issue we propose a novel framework that embeds personal watermarks…
Image manipulation techniques have drawn growing concerns as manipulated images might cause morality and security problems. Various methods have been proposed to detect manipulations and achieved promising performance. However, these methods might be vulnerable to adversarial attacks. In this work,…
This paper proposes a new visual reasoning formulation that aims at discovering changes between image pairs and their temporal orders. Recognizing scene dynamics and their chronological orders is a fundamental aspect of human cognition. The aforementioned abilities make it possible to follow step-by…
Formula-driven supervised learning (FDSL) is a pre-training method that relies on synthetic images generated from mathematical formulae such as fractals. Prior work on FDSL has shown that pre-training vision transformers on such synthetic datasets can yield competitive accuracy on a wide range of d…
We introduce a new task of question generation to eliminate the uncertainty of referring expressions in 3D indoor environments (3D-REQ). Referring to an object using natural language is one of the most common occurrences in daily human conversations; therefore, instructing robots to identify a certa…
Pre-training is a strong strategy for enhancing visual models to efficiently train them with a limited number of labeled images. In semantic segmentation, creating annotation masks requires an intensive amount of labor and time, and therefore, a large-scale pre-training dataset with semantic labels…
Traffic accident recognition is essential in developing automated driving and Advanced Driving Assistant System technologies. A large dataset of annotated traffic accidents is necessary to improve the accuracy of traffic accident recognition using deep learning models. Conventional traffic accident…
Formula-driven supervised learning (FDSL) has been shown to be an effective method for pre-training vision transformers, where ExFractalDB-21k was shown to exceed the pre-training effect of ImageNet-21k. These studies also indicate that contours mattered more than textures when pre-training vision t…
Is it possible to complete Vision Transformer (ViT) pre-training without natural images and human-annotated labels? This question has become increasingly relevant in recent months because while current ViT pre-training tends to rely heavily on a large number of natural images and human-annotated lab…
"The success of neural fields for 3D vision tasks is now indisputable. Following this trend, several methods aiming for visual localization (e.g., SLAM) have been proposed to estimate distance or density fields using neural fields. However, it is difficult to achieve high localization performance by…
The construction of 3D point cloud datasets requires a great deal of human effort. Therefore, constructing a largescale 3D point clouds dataset is difficult. In order to remedy this issue, we propose a newly developed point cloud fractal database (PC-FractalDB), which is a novel family of formula-dr…
In the present work, we show that the performance of formula-driven supervised learning (FDSL) can match or even exceed that of ImageNet-21k without the use of real images, human-, and self-supervision during the pre-training of Vision Transformers (ViTs). For example, ViT-Base pre-trained on ImageN…
Existing change captioning studies have mainly focused on a single change. However, detecting and describing multiple changed parts in image pairs is essential for enhancing adaptability to complex scenarios. We solve the above issues from three aspects: (i) We propose a simulation-based multi-chang…
The paper proposes a method for automatic multi-view dataset construction based on formula-driven supervised learning (FDSL). Although data collection and human annotation of 3D objects are labor-intensive, we automatically generate their training data and labels in the proposed multi-view dataset.…
In this letter, we propose a framework that recognizes and describes changes that occur in a scene observed from multiple viewpoints in natural language text. The ability to recognize and describe changes that occurred in a 3D scene plays an essential role in a variety of human-robot interaction app
The paper presents a pedestrian near-miss detector with temporal analysis that provides both pedestrian detection and risk-level predictions which are demonstrated on a self-collected database. Our work makes three primary contributions: (i) The framework of pedestrian near-miss detection is propose…
The paper presents an unsupervised out-of-context action (O2CA) paradigm that is based on facilitating understanding by separately presenting both human action and context within a video sequence. As a means of generating an unsupervised label, we comprehensively evaluate responses from action-based…
In this paper, we propose a novel approach for traffic accident anticipation through (i) Adaptive Loss for Early Anticipation (AdaLEA) and (ii) a large-scale self-annotated incident database. The proposed AdaLEA allows us to gradually learn an earlier anticipation as training progresses. The loss fu…
The purpose of this study is to determine whether current video datasets have sufficient data for training very deep convolutional neural networks (CNNs) with spatio-temporal three-dimensional (3D) kernels. Recently, the performance levels of 3D CNNs in the field of action recognition have improved…
Because of their recent introduction, self-driving cars and advanced driver assistance system (ADAS) equipped vehicles have had little opportunity to learn, the dangerous traffic (including near-miss incident) scenarios that provide normal drivers with strong motivation to drive safely. Accordingly,…