8 accepted papers
Scene Graph Generation (SGG) paradigms predominantly model relationships as static, unidirectional mappings ($s \to o$), effectively treating objects as passive recipients of actions. This formulation suffers from an inherent \textit{unidirectional bias}, violating the physical reality that visual i…
Semi-supervised learning (SSL) has garnered significant attention due to its ability to leverage limited labeled data and a large amount of unlabeled data to improve model generalization performance. Recent approaches achieve impressive successes by combining ideas from both consistency regularizati…
Modeling label correlations has always played a pivotal role in multi-label image classification (MLC), attracting significant attention from researchers. However, recent studies have overemphasized co-occurrence relationships among labels, which can lead to overfitting risk on this overemphasis, re…
The scarcity of annotated medical imaging data has driven significant progress in semi-supervised learning to alleviate reliance on expensive expert labeling. While foundational vision models such as the Segment Anything Model (SAM) exhibit robust generalization in generic segmentation tasks, their…
Single-view clothed human reconstruction has emerged as a prominent research focus, yet reconstructing the occluded backside of the human body remains a significant challenge due to viewpoint limitations and occlusion. In this paper, we propose a novel single-view clothed human reconstruction method…
X-ray 3D reconstruction has achieved superior performance in medical imaging with traditional and deep learning methods. However, when sparse-view X-ray projections are used to minimize patient exposure to radiation, these methods tend to overfit and produce blurring. To overcome this problem, we pr…
Most existing reflection removal methods pay too much attention to the transmission layer and ignore the mutual complementary mechanisms between the transmission layer and the reflection layer. To make full use of the complementarity and distinction between the reflection and transmission layers in…
Polyphone disambiguation is the core of grapheme-to-phoneme(G2P) module for the Chinese speech synthesis system. However, there is a lack of datasets and only one public for polyphone disambiguation. Moreover, due to the double long-tail distribution of polyphones, the ratio of pronunciation data fo…