10 accepted papers
Scene Graph Generation (SGG) connects visual perception with structured reasoning, but is limited by scarce annotations and the long-tailed distribution of relational predicates. Training-free methods based on vision-language models (VLMs) reduce supervision requirements, yet often rely on flat grap…
Aligning the decision-making process of deep learning models with that of experienced sonographers is essential for ultrasound-based reliable disease diagnosis. Although existing methods have made significant progress in this aspect, their alignments are primarily associational rather than causal, l
RGB-based category-level 6D pose estimation methods have faced significant challenges in achieving real-time performance, primarily due to the design of two-stage pipeline. To address this issue, we propose a novel end-to-end pipeline named EPCPE. In detail, we first extract implicit rotation featur…
We propose a novel three-stage Transformer-based methodology for entire cytopathological whole slide image (WSI) classification. The key idea is to leverage Transformer to extract the fine-grained lesion-level features and then progressively aggregate them into intermediate-grained patch-level featu…
A fundamental challenge in deep metric learning is the generalization capability of the feature embedding network model since the embedding network learned on training classes need to be evaluated on new test classes. To address this challenge, in this paper, we introduce a new method called coded…
Autonomous exploration in unknown environments is a fundamental task of Unmanned Aerial Vehicles (UAVs). To choose exploration goals wisely, we propose an information-driven exploration strategy by applying the fast marching method to exploration of UAVs. A frontier point detection algorithm is desi
Fully automated cervical cancer grading on the level of Whole Slide Images (WSI) is a challenge task. As WSIs are in gigapixel resolution, it is impossible to train a deep classification neural network with the entire WSIs as inputs. To bypass this problem, we propose a two-stage learning framework.…
Unsupervised visual representation learning aims to learn general features from unlabelled data. Early methods design intra-image pretext tasks as learning targets and can be achieved with low computational overhead but unsatisfactory performance. Recent methods introduce contrastive learning and ac…
The microscopic urine sediment examination is a crucial part in the evaluation of renal and urinary tract diseases. Recently, there are emerging CNNs-based detectors to detect the urine sediment particles in an end-to-end manner. However, it is not very compatible to transfer CNNs-based detector dir…
Segmentation of optic disc (OD) and optic cup (OC) is critical in automated fundus image analysis system. Existing state-of-the-arts focus on designing deep neural networks with one or multiple dense prediction branches. Such kind of designs ignore connections among prediction branches and their lea…