10 accepted papers
Self-supervised visual foundation models produce powerful embeddings that achieve remarkable performance on a wide range of downstream tasks. However, unlike vision-language models such as CLIP, self-supervised visual features are not readily aligned with language, hindering their adoption in open-v…
We address the problem of few-shot pattern detection, which aims to detect all instances of a given pattern, typically represented by a few exemplars, from an input image. Although similar problems have been studied in few-shot object counting and detection (FSCD), previous methods and their benchma…
We address the problem of generalized category discovery (GCD) that aims to partition a partially labeled collection of images; only a small part of the collection is labeled and the total number of target classes is unknown. To address this generalized image clustering problem we revisit the mean-s…
"We present Lazy Visual Grounding for open-vocabulary semantic segmentation, which decouples unsupervised object mask discovery from object grounding. Plenty of the previous art casts this task as pixel-to-text classification without object-level comprehension, leveraging the image-to-text classific…
We address the task of weakly-supervised few-shot image classification and segmentation, by leveraging a Vision Transformer (ViT) pretrained with self-supervision. Our proposed method takes token representations from the self-supervised ViT and leverages their correlations, via self-attention, to pr…
We introduce the integrative task of few-shot classification and segmentation (FS-CS) that aims to both classify and segment target objects in a query image when the target classes are given with a few examples. This task combines two conventional few-shot learning problems, few-shot classification…
Few-shot semantic segmentation aims at learning to segment a target object from a query image using only a few annotated support images of the target class. This challenging task requires to understand diverse levels of visual cues and analyze fine-grained correspondence relations between the query…
We propose to address the problem of few-shot classification by meta-learning "what to observe" and "where to attend" in a relational perspective. Our method leverages relational patterns within and between images via self-correlational representation (SCR) and cross-correlational attention (CCA). W…
In smart home environments, central interfaces that take commands from users and give orders to each relevant device appropriately are increasingly important. We investigated the type of central interface that consumers are more willing to adopt and whether these interfaces enhance the evaluation of…
The purpose of this study is to investigate a robot's impression perceived by users as well as the accuracy of perception of location information, which the robot provided according to the modality type of the robot. To explore this, we designed two 2 (verbal types: deictic vs. descriptive) x 2 (nos…