9 accepted papers
The ability to adapt to varying observation lengths is crucial for human trajectory prediction tasks, particularly in scenarios with limited observation lengths or missing data. Existing approaches mainly focus on introducing novel architectures or additional structural components, which substantial…
Colonoscopy is the gold standard for detecting colorectal lesions and is critical for early screening and prevention of colorectal cancer. However, accurate polyp segmentation remains a challenging task due to the diverse morphology, varying sizes and indistinct boundaries of polyps. To address thes…
Large Language Models (LLMs) often generate hallucinations, producing outputs that are contextually inaccurate or factually incorrect. We introduce HICD, a novel method designed to induce hallucinations for contrastive decoding to mitigate hallucinations. Unlike existing contrastive decoding methods…
Algorithm of outlier removal for learning-based 3D point cloud registration is usually regarded as a classification problem. The core for this to be successful is to learn the discriminative inlier/outlier feature representations. This letter proposes a two-stage efficient network (MuSCLe-Reg) with
Trajectory prediction is a critical technique for autonomous driving, robot navigation and intelligent surveillance. The recurrent neural networks (RNNs) models propagate temporal information through hidden states and have been widely applied in adapting to varying input lengths and generating multi
In semi-supervised medical image segmentation, appropriately merging labeled and unlabeled data before network training instead of using them separately can effectively reduce knowledge loss, mitigate distribution discrepancies and promote efficient knowledge transfer to unlabeled data. However, exi…
Pedestrian's future trajectory prediction is a key challenge in ego-centric view of autonomous driving system. Most of the current methods are flawed in capturing subtle change features in a lightweight model size. To solve this problem, we propose a multimodal forward generation transformer network
In this paper, we propose a novel and efficient method for knowledge distillation, which is structurally simple and requires negligible computation overhead. Our method includes three modules. The first module is the calibrated mask, which avoids the teacher model’s incorrect representation to distu…
Image inpainting has made huge strides benefiting from the advantages of convolutional neural networks (CNNs) in understanding high-level semantics. Recently, some studies have applied transformers to the visual field to solve the problem that the convolution kernel cannot attend to longdistance inf…