Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Min, Juhong"'
Learning to assemble geometric shapes into a larger target structure is a pivotal task in various practical applications. In this work, we tackle this problem by establishing local correspondences between point clouds of part shapes in both coarse- a
Externí odkaz:
http://arxiv.org/abs/2407.10542
This paper addresses the task of video question answering (videoQA) via a decomposed multi-stage, modular reasoning framework. Previous modular methods have shown promise with a single planning stage ungrounded in visual content. However, through a s
Externí odkaz:
http://arxiv.org/abs/2404.06511
Recent studies show that leveraging the match-wise relationships within the 4D correlation map yields significant improvements in establishing semantic correspondences - but at the cost of increased computation and latency. In this work, we focus on
Externí odkaz:
http://arxiv.org/abs/2311.04336
Human vision possesses a special type of visual processing systems called peripheral vision. Partitioning the entire visual field into multiple contour regions based on the distance to the center of our gaze, the peripheral vision provides us the abi
Externí odkaz:
http://arxiv.org/abs/2206.06801
Establishing correspondences between images remains a challenging task, especially under large appearance changes due to different viewpoints or intra-class variations. In this work, we introduce a strong semantic image matching learner, dubbed Trans
Externí odkaz:
http://arxiv.org/abs/2205.11634
Despite advances in feature representation, leveraging geometric relations is crucial for establishing reliable visual correspondences under large variations of images. In this work we introduce a Hough transform perspective on convolutional matching
Externí odkaz:
http://arxiv.org/abs/2109.05221
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 representatio
Externí odkaz:
http://arxiv.org/abs/2108.09666
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-g
Externí odkaz:
http://arxiv.org/abs/2104.01538
Autor:
Min, Juhong, Cho, Minsu
Despite advances in feature representation, leveraging geometric relations is crucial for establishing reliable visual correspondences under large variations of images. In this work we introduce a Hough transform perspective on convolutional matching
Externí odkaz:
http://arxiv.org/abs/2103.16831
Feature representation plays a crucial role in visual correspondence, and recent methods for image matching resort to deeply stacked convolutional layers. These models, however, are both monolithic and static in the sense that they typically use a sp
Externí odkaz:
http://arxiv.org/abs/2007.10587