18 accepted papers
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,…
While depth maps of indoor scenes are often essential for a variety of applications, measuring depth maps usually requires dedicated depth sensors, which are not always available. Echo-based depth estimation has been explored as a promising alternative solution. However, most existing methods assume…
This paper introduces a novel task to evaluate the robust understanding capability of Large Multimodal Models (LMMs), termed Unsolvable Problem Detection (UPD). Multiple-choice question answering (MCQA) is widely used to assess the understanding capability of LMMs, but it does not guarantee that LMM…
Large-scale pre-trained models (PTMs) provide remarkable zero-shot classification capability covering a wide variety of object classes. However, practical applications do not always require the classification of all kinds of objects, and leaving the model capable of recognizing unnecessary classes n…
Given only acoustic signals without any high-level information, such as voices or sounds of scenes/actions, how much can we infer about the behavior of humans? Unlike existing methods, which suffer from privacy issues because they use signals that include human speech or the sounds of specific actio…
We present a novel vision-language prompt learning approach for few-shot out-of-distribution (OOD) detection. Few-shot OOD detection aims to detect OOD images from classes that are unseen during training using only a few labeled in-distribution (ID) images. While prompt learning methods such as CoOp…
Echoes reflect a geometric structure of a scene surrounding a sound source. In this paper, we address the problem of estimating depth maps of indoor scenes based on echoes. First, we experimentally show that fusing multiple acoustic features, especially spectrogram and angular spectrum, can improve…
Unsupervised domain adaptation (UDA) has been highly successful in transferring knowledge acquired from a label-rich source domain to a label-scarce target domain. Open-set domain adaptation (open-set DA) and universal domain adaptation (UniDA) have been proposed as solutions to the problem concerni…
We consider the problem of 2D motion retargeting, which is to transfer the motion of one 2D skeleton to another skeleton of a different body shape. Existing methods decompose the input motion skeleton into dynamic (motion) and static (body shape, viewpoint angle, and emotion) features and synthesize…
Many variants of unsupervised domain adaptation (UDA) problems have been proposed and solved individually. Its side effect is that a method that works for one variant is often ineffective for or not even applicable to another, which has prevented practical applications. In this paper, we give a gene…
Lifelong learning aims to train a highly expressive model for a new task while retaining all knowledge for previous tasks. However, many practical scenarios do not always require the system to remember all of the past knowledge. Instead, ethical considerations call for selective and proactive forget…
Semi-supervised learning (SSL) has been proposed to leverage unlabeled data for training powerful models when only limited labeled data is available. While existing SSL methods assume that samples in the labeled and unlabeled data share the classes of their samples, we address a more complex novel s…
Finding a region of an image which matches to a query from a large number of candidates is a fundamental problem in image processing. The exhaustive nature of the sliding window approach has encouraged works that can reduce the run time by skipping unnecessary windows or pixels that do not play a su…
Sounds provide us with vast amounts of information about surrounding objects and can even remind us visual images of them. Is it possible to implement this noteworthy human ability on machines? In this paper, we study a new task that consists of predicting image recognition results in the form of se…
Subspace clustering is the problem of partitioning data drawn from a union of multiple subspaces. The most popular subspace clustering framework in recent years is the graph clustering-based approach, which performs subspace clustering in two steps: graph construction and graph clustering. Although…
In query expansion for object retrieval, there is substantial danger of query drift, where irrelevant information is inferred from pseudo-relevant images to enrich the query. To address this issue, we propose a query expansion method from the viewpoint of diffusion. It explores the structure of high…
Neural word vector (NWV) such as word2vec is a powerful text representation tool that can encode extensive semantic information into compact vectors. This ability poses an interesting question in relation to image processing research - Can we learn better semantic image features from NWVs? We empiri…
This paper addresses the problem of unsupervised learning of binary hash codes for efficient cross-modal retrieval. Many unimodal hashing studies have proven that both similarity preservation of data and maintenance of quantization quality are essential for improving retrieval performance with binar…