Lei Chen

Chang Shu, Ziming Chen, Lei Chen et al.

Since context modeling is critical for estimating depth from a single image, researchers put tremendous effort into obtaining global context. Many global manipulations are designed for traditional CNN-based architectures to overcome the locality of convolutions. Attention mechanisms or transformers originally designed for capturing long-range dependencies might be a better choice, but usually complicates architectures and could lead to a decrease in inference speed. In this work, we propose a pure transformer architecture called SideRT that can attain excellent predictions in real-time. In order to capture better global context, Cross-Scale Attention (CSA) and Multi-Scale Refinement (MSR) modules are designed to work collaboratively to fuse features of different scales efficiently. CSA modules focus on fusing features of high semantic similarities, while MSR modules aim to fuse features at corresponding positions. These two modules contain a few learnable parameters without convolutions, based on which a lightweight yet effective model is built. This architecture achieves state-of-the-art performances in real-time (51.3 FPS) and becomes much faster with a reasonable performance drop on a smaller backbone Swin-T (83.1 FPS). Furthermore, its performance surpasses the previous state-of-the-art by a large margin, improving AbsRel metric 6.9% on KITTI and 9.7% on NYU. To the best of our knowledge, this is the first work to show that transformer-based networks can attain state-of-the-art performance in real-time in the single image depth estimation field. Code will be made available soon.

Yubo Wang, Haoyang Li, Fei Teng et al.

Text classification is a fundamental task in data mining, pivotal to various applications such as tabular understanding and recommendation. Although neural network-based models, such as CNN and BERT, have demonstrated remarkable performance in text classification, their effectiveness heavily relies on abundant labeled training data. This dependency makes these models less effective in dynamic few-shot text classification, where labeled data is scarce, and new target labels frequently appear based on application needs. Recently, large language models (LLMs) have shown promise due to their extensive pretraining and contextual understanding ability. Current approaches provide LLMs with text inputs, candidate labels, and additional side information (e.g., descriptions) to classify texts. However, their effectiveness is hindered by the increased input size and the noise introduced through side information processing. To address these limitations, we propose a graph-based online retrieval-augmented generation framework, namely GORAG, for dynamic few-shot text classification. Rather than treating each input independently, GORAG constructs and maintains a weighted graph by extracting side information across all target texts. In this graph, text keywords and labels are represented as nodes, with edges indicating the correlations between them. To model these correlations, GORAG employs an edge weighting mechanism to prioritize the importance and reliability of extracted information and dynamically retrieves relevant context using a minimum-cost spanning tree tailored for each text input. Empirical evaluations demonstrate that GORAG outperforms existing approaches by providing more comprehensive and precise contextual information.

Yang Chen, Zongqing Lu, Xuechen Zhang et al.

Recent end-to-end deep neural networks for disparity regression have achieved the state-of-the-art performance. However, many well-acknowledged specific properties of disparity estimation are omitted in these deep learning algorithms. Especially, matching cost volume, one of the most important procedure, is treated as a normal intermediate feature for the following softargmin regression, lacking explicit constraints compared with those traditional algorithms. In this paper, inspired by previous canonical definition of cost volume, we propose the noise-sampling cross entropy loss function to regularize the cost volume produced by deep neural networks to be unimodal and coherent. Extensive experiments validate that the proposed noise-sampling cross entropy loss can not only help neural networks learn more informative cost volume, but also lead to better stereo matching performance compared with several representative algorithms.

Congcong Jin, Jihua Zhu, Yaochen Li et al.

Recently, the low rank and sparse (LRS) matrix decomposition has been introduced as an effective mean to solve the multi-view registration. It views each available relative motion as a block element to reconstruct one matrix so as to approximate the low rank matrix, where global motions can be recovered for multi-view registration. However, this approach is sensitive to the sparsity of the reconstructed matrix and it treats all block elements equally in spite of their varied reliability. Therefore, this paper proposes an effective approach for multi-view registration by the weighted LRS decomposition. Based on the anti-symmetry property of relative motions, it firstly proposes a completion strategy to reduce the sparsity of the reconstructed matrix. The reduced sparsity of reconstructed matrix can improve the robustness of LRS decomposition. Then, it proposes the weighted LRS decomposition, where each block element is assigned with one estimated weight to denote its reliability. By introducing the weight, more accurate registration results can be recovered from the estimated low rank matrix with good efficiency. Experimental results tested on public data sets illustrate the superiority of the proposed approach over the state-of-the-art approaches on robustness, accuracy, and efficiency.