Alibaba Group (China)

Object detection in High-Resolution Wide (HRW) shots, or gigapixel images, presents unique challenges due to extreme object sparsity and vast scale variations. State-of-the-art methods like SparseFormer have pioneered sparse processing by selectively focusing on important regions, yet they apply a uniform computational model to all selected regions, overlooking their intrinsic complexity differences. This leads to a suboptimal trade-off between performance and efficiency. In this paper, we introduce GigaMoE, a novel backbone architecture that pioneers adaptive computation for this domain by replacing the standard Feed-Forward Networks (FFNs) with a Mixture-of-Experts (MoE) module. Our architecture first employs a shared expert to provide a robust feature baseline for all selected regions. Upon this foundation, our core innovation---a novel Sparsity-Guided Routing mechanism---insightfully repurposes importance scores from the sparse backbone to provide a "computational bonus,'' dynamically engaging a variable number of specialized experts based on content complexity. The entire system is trained efficiently via a loss-free load-balancing technique, eliminating the need for cumbersome auxiliary losses. Extensive experiments show that GigaMoE sets a new state-of-the-art on the PANDA benchmark, improving detection accuracy by 1.1% over SparseFormer while simultaneously reducing the computational cost (FLOPs) by a remarkable 32.3%.

The integration of renewable energy in modern power systems has increased the stochasticity of load patterns. Probabilistic load forecasting (PLF) acts as a solution to capture the uncertainty of load data. Especially, neural network (NN)-based models can be flexibly adapted for PLF. However, the black-box characteristic of NN makes it difficult for us to understand where the uncertainty comes from, thereby reducing the reliability of the forecast. This paper provides a model-agnostic NN explanation framework for PLF, attributing the uncertainty quantification of the probabilistic forecast to the uncertainty of the input feature to evaluate where the uncertainty in the forecast comes from. Unlike traditional deep learning explanation techniques that examine how the information of the feature affects the result, our explanation focuses on exploring how the uncertainty of the feature affects the result. In this way, we can know the uncertainty and error sources of PLF and further understand its mechanism, which can help reduce the adverse effects of data uncertainty on forecasts. The codes can be found in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/hkuedl/Explanation-for-Probabilistic-Load-Forecasting</uri>.

Esophageal varices (EV) represent a critical complication of portal hypertension, affecting approximately 60% of cirrhosis patients with a significant bleeding risk of ∼ 30%. While traditionally diagnosed through invasive endoscopy, non-contrast computed tomography (NCCT) presents a potential non-invasive alternative that has yet to be fully utilized in clinical practice. We present Multi-Organ-COhesion Network++ (MOON++), a novel multimodal framework that enhances EV assessment through comprehensive analysis of NCCT scans. Inspired by clinical evidence correlating organ volumetric relationships with liver disease severity, MOON++ synthesizes imaging characteristics of the esophagus, liver, and spleen through multimodal learning. We evaluated our approach using 1631 patients, those with endoscopically confirmed EV were classified into four severity grades. Validation in 239 patient cases and independent testing in 289 cases demonstrate superior performance compared to conventional single organ methods, achieving an AUC of 0.894 versus 0.803 for the severe grade EV classification (G3 versus < G3) and 0.921 versus 0.793 for the differentiation of moderate to severe grades ( ≥ G2 versus < G2). We conducted a reader study involving experienced radiologists to further validate the performance of MOON++. To our knowledge, MOON++ represents the first comprehensive multi-organ NCCT analysis framework incorporating clinical knowledge priors for EV assessment, potentially offering a promising non-invasive diagnostic alternative. Code is available at https://github.com/StevenHaojc/MOON.

Abstract Recently, the reasoning capabilities of Large Reasoning Models (LRMs), such as DeepSeek-R1, have witnessed significant advancements through computationally intensive “slow thinking” processes. These models have demonstrated impressive performance across a variety of complex reasoning tasks. However, despite their remarkable success, LRMs come with substantial computational demands that pose considerable challenges in terms of resource consumption, scalability, and accessibility. In contrast, Small Reasoning Models (SRMs), which are often distilled from larger models, offer a more efficient alternative while still achieving competitive performance. Beyond their efficiency, SRMs frequently exhibit distinct capabilities and cognitive trajectories compared with their larger counterparts, making them particularly interesting from both practical and theoretical perspectives. In this work, we provide a timely and comprehensive survey of recently published research focused on SRMs. We first review the current landscape of SRMs. Then, we analyze diverse training paradigms and inference techniques tailored to enhance the reasoning capabilities of SRMs. Furthermore, we offer an extensive review of domain-specific applications where SRMs have been effectively leveraged. Finally, we discuss promising future research directions that aim to bridge existing gaps. By consolidating recent advances, this survey serves as an essential reference for researchers and practitioners interested in leveraging or developing SRMs to unlock advanced reasoning functionalities with improved efficiency.

Face Recognition (FR) technology has made significant strides with the emergence of deep learning. Typically, most existing FR models are built upon Convolutional Neural Networks (CNN) and take RGB face images as the model's input. In this work, we take a closer look at existing FR paradigms from high-efficiency, security, and precision perspectives, and identify the following three problems: (i) CNN frameworks are vulnerable in capturing global facial features and modeling the correlations between local facial features. (ii) Selecting RGB face images as the model's input greatly degrades the model's inference efficiency, increasing the extra computation costs. (iii) In the real-world FR system that operates on RGB face images, the integrity of user privacy may be compromised if hackers successfully penetrate and gain access to the input of this model. To solve these three issues, we propose two novel FR frameworks, i.e., TransFace and TransFace++, which successfully explore the feasibility of applying ViTs and image bytes to FR tasks, respectively. Firstly, as revealed from our observations, we find that ViTs perform vulnerably when applied to FR scenarios with extremely large datasets. We investigate the reasons for this phenomenon and discover that the existing data augmentation approaches and hard sample mining strategies are incompatible with ViTs-based FR backbone due to the lack of tailored consideration on preserving face structural information and leveraging each local token information. To remedy these problems, we first propose a superior FR model called TransFace, which contains a patch-level data augmentation strategy named Dominant Patch Amplitude Perturbation (DPAP) and a hard sample mining strategy named Entropy-guided Hard Sample Mining (EHSM). Furthermore, to improve inference efficiency and user privacy protection, we investigate the intrinsic property of image bytes and propose a superior FR model termed TransFace++. The proposed model is trained directly on image bytes, presenting a novel approach to address the aforementioned issues. Specifically, considering the importance of local correlations in bytes, an image bytes compression strategy named Topology-based Image Bytes Compression (TIBC) is introduced to extract prominent features from the raw bytes and integrate these features with byte embeddings, effectively mitigating information loss during the bytes mapping process. Moreover, to strengthen the model's perception on geometric information encoded in image bytes, a novel cross-attention module named Structure Information-guided Cross-Attention (SICA) is designed to inject structure information into byte tokens for information interaction, significantly improving the model's generalization ability. Experiments on popular face benchmarks demonstrate the superiority of our TransFace and TransFace++.