15 accepted papers
Long-Tailed Class-Incremental Learning (LT-CIL) remains a fundamental challenge due to biased gradient updates caused by highly imbalanced data distributions and the inherent stability-plasticity dilemma. These factors jointly degrade tail-class performance and exacerbate catastrophic forgetting. To…
The development of Reasoning Large Language Models (RLLMs) has significantly improved multi-step reasoning capabilities, but it has also made hallucination problems more frequent and harder to eliminate. While existing approaches address hallucination through external knowledge integration, model pa…
Knowledge graphs (KGs) are ubiquitous in numerous real-world applications, and watermarking facilitates protecting intellectual property and preventing potential harm from AI-generated content. Existing watermarking methods mainly focus on static plain text or image data, while they can hardly be ap…
Robust robotic grasping in cluttered environments presents a significant challenge, as existing methods often neglect the complex interactions between the gripper, objects, and obstacles, leading to collisions and grasping failures. To address this, we propose a framework that integrates collision a
In ultra-high resolution image segmentation task for robotic platforms like UAVs and autonomous vehicles, existing paradigms process a downsampled input image through a deep network and the original high-resolution image through a shallow network, then fusing their features for final segmentation. A
Multi-view clustering aims to improve the clustering performance by leveraging information from multiple views. Most existing works assume that all views are complete. However, samples in real-world scenarios cannot be always observed in all views, leading to the challenging problem of Incomplete Mu…
Ultra-high resolution image segmentation poses a formidable challenge for UAVs with limited computation resources. Moreover, with multiple deployed tasks (e.g., mapping, localization, and decision making), the demand for a memory efficient model becomes more urgent. This letter delves into the intri
Domain generalization (DG) is a fundamental yet very challenging research topic in machine learning. The existing arts mainly focus on learning domain-invariant features with limited source domains in a static model. Unfortunately, there is a lack of training-free mechanism to adjust the model when…
A novel siamese autoencoder visual odometry system named SAEVO is proposed in this paper. SAEVO can jointly estimate the 6-DoF pose and the depth using deep neural networks trained with monocular clips only. The main idea of the proposed method is an unsupervised deep learning scheme that combines s…
Learning 3D object reconstruction from a single RGB image is a fundamental and extremely challenging problem for robots. As acquiring labeled 3D shape representations for real-world data is time-consuming and expensive, synthetic image-shape pairs are widely used for 3D reconstruction. However, the…
Ultra-high resolution image segmentation has raised increasing interests in recent years due to its realistic applications. In this paper, we innovate the widely used high-resolution image segmentation pipeline, in which an ultra-high resolution image is partitioned into regular patches for local se…
HD map reconstruction is crucial for autonomous driving. LiDAR-based methods are limited due to the deployed expensive sensors and time-consuming computation. Camera-based methods usually need to separately perform road segmentation and view transformation, which often causes distortion and the abse…
Batch Normalization (BN) techniques have been proposed to reduce the so-called Internal Covariate Shift (ICS) by attempting to keep the distributions of layer outputs unchanged. Experiments have shown their effectiveness on training deep neural networks. However, since only the first two moments are…
Curvilinear structures are frequently observed in various images in different forms, such as blood vessels or neuronal boundaries in biomedical images. In this paper, we propose a novel curvilinear structure segmentation approach using context-aware spatio-recurrent networks. Instead of directly seg…
In this paper, we propose a novel iterative multi-task framework to complete the segmentation mask of an occluded vehicle and recover the appearance of its invisible parts. In particular, firstly, to improve the quality of the segmentation completion, we present two coupled discriminators that intro…