Learning sign language is not only a gateway to linguistic competence but also to cultural participation, self-expression, and meaningful interaction within the Deaf community. Recent advances in sign language education technologies have made notable progress in supporting vocabulary acquisition and sentence-level translation. However, dialogue, as one critical component of natural language use, remains largely absent from existing systems. We identify two underlying challenges that contribute to this gap: the lack of recognition robustness to viewpoint variation, which constrains expressive freedom during signing; and the limited semantic modeling capabilities needed to support discourse-level interpretation and interaction. To address these issues, we propose a learning-centered system that integrates pose-based multi-view augmentation and a multi-agent language modeling workflow. This system supports free-form input across diverse signing perspectives and provides structured feedback across word, sentence, and dialogue levels. Built as a modular and deployable platform, the system demonstrates strong recognition performance and learner engagement across varied input conditions. Through this integration of spatial robustness and semantic scaffolding, our work advances the design of sign language learning technologies toward more interactive, expressive, and pedagogically grounded experiences. However, the current system remains limited by its focus on successive (non-continuous) signing and by its moderate vocabulary and restricted pedagogical scope. Future work will therefore extend lexical coverage, incorporate more natural continuous signing, and investigate richer educational functions. • A View-invariant Pose-based Isolated Auslan Sign Recognition. • A Multi-agent Workflow Providing Transparent Dialogue Support. • An Interactive, Learner-paced Design for Auslan Learning System.

3D cars are commonly used in self-driving systems, virtual/augmented reality, and games. However, existing 3D car datasets are either synthetic or low-quality, limiting their applications in practical scenarios and presenting a significant gap toward high-quality real-world 3D car datasets. In this paper, we propose the first large-scale 3D real car dataset, termed 3DRealCar, offering three distinctive features. (1) \textbf{High-Volume}: 2,500 cars are meticulously scanned by smartphones, obtaining car images and point clouds with real-world dimensions; (2) \textbf{High-Quality}: Each car is captured in an average of 200 dense, high-resolution 360-degree RGB-D views, enabling high-fidelity 3D reconstruction; (3) \textbf{High-Diversity}: The dataset contains various cars from over 100 brands, collected under three distinct lighting conditions, including reflective, standard, and dark. Additionally, we offer detailed car parsing maps for each instance to promote research in car parsing tasks. Moreover, we remove background point clouds and standardize the car orientation to a unified axis for the reconstruction only on cars and controllable rendering without background. We benchmark 3D reconstruction results with state-of-the-art methods across different lighting conditions in 3DRealCar. Extensive experiments demonstrate that the standard lighting condition part of 3DRealCar can be used to produce a large number of high-quality 3D cars, improving various 2D and 3D tasks related to cars. Notably, our dataset brings insight into the fact that recent 3D reconstruction methods face challenges in reconstructing high-quality 3D cars under reflective and dark lighting conditions. \textcolor{red}{\href{https://xiaobiaodu.github.io/3drealcar/}{Our dataset is here.}}

Retina images provide a noninvasive view of the central nervous system and microvasculature, making it essential for clinical applications. Changes in the retina often indicate both ophthalmic and systemic diseases, aiding in diagnosis and early intervention. While deep learning algorithms have advanced retina image analysis, a comprehensive review of related datasets, tasks, and benchmarking is still lacking. In this survey, we systematically categorize existing retina image datasets based on their available data modalities, and review the tasks these datasets support in multimodal retina image analysis. We also explain key evaluation metrics used in various retina image analysis benchmarks. By thoroughly examining current datasets and methods, we highlight the challenges and limitations in existing benchmarks and discuss potential research topics in the field. We hope this work will guide future retina analysis methods and promote the shared use of existing data across different tasks.

Effective communication between the deaf community and hearing individuals facilitates social inclusion, equal opportunities, and the dignity of vulnerable populations. However, existing region-specific sign language systems are constrained by limited training datasets and narrow topic domains, rendering them ineffective for bridging the linguistic gaps between sign languages and spoken languages. Auslan, as the sign language specific to Australia, still lacks a reliable bidirectional translation tool for effective communication. To address these challenges, we propose AuslanWeb, a web-based system for bidirectional translation of both isolated and successive sign language. For the former, AuslanWeb achieves high-precision mapping between isolated signs (glosses) and spoken language words or phrases through a multimodal recognition system and a versatile Auslan dictionary. For the latter, it leverages the advanced contextual understanding and text generation capabilities of Large Language Models (LLMs) to support bidirectional translation between successive sign language videos and long-form spoken language. By integrating linguistic structure with advanced AI capabilities, AuslanWeb overcomes the limitations of dataset dependency and enhances the scalability of sign language translation systems. The effectiveness of the system is further validated through user feedback, receiving consistent praise from Auslan experts, Australian deaf individuals, and volunteers. The demo video of AuslanWeb is provided here.

Existing audio-visual segmentation datasets typically focus on short-trimmed videos with only one pixel-map annotation for a per-second video clip. In contrast, for untrimmed videos, the sound duration, start- and end-sounding time positions, and visual deformation of audible objects vary significantly. Therefore, we observed that current AVS models trained on trimmed videos might struggle to segment sounding objects in long videos. To investigate the feasibility of grounding audible objects in videos along both temporal and spatial dimensions, we introduce the Long-Untrimmed Audio-Visual Segmentation dataset (LU-AVS), which includes precise frame-level annotations of sounding emission times and provides exhaustive mask annotations for all frames. Considering that pixel-level annotations are difficult to achieve in some complex scenes, we also provide the bounding boxes to indicate the sounding regions. Specifically, LU-AVS contains 10M mask annotations across 6.6K videos, and 11M bounding box annotations across 7K videos. Compared with the existing datasets, LU-AVS videos are on average 4~8 times longer, with the silent duration being 3~15 times greater. Furthermore, we try our best to adapt some baseline models that were originally designed for audio-visual-relevant tasks to examine the challenges of our newly curated LU-AVS. Through comprehensive evaluation, we demonstrate the challenges of LU-AVS compared to the ones containing trimmed videos. Therefore, LU-AVS provides an ideal yet challenging platform for evaluating audio-visual segmentation and localization on untrimmed long videos. The dataset is publicly available at: https://yenanliu.github.io/LU-AVS/.

Isolated Sign Language Recognition (ISLR) focuses on identifying individual sign language glosses. Considering the diversity of sign languages across geographical regions, developing region-specific ISLR datasets is crucial for supporting communication and research. Auslan, as a sign language specific to Australia, still lacks a dedicated large-scale word-level dataset for the ISLR task. To fill this gap, we curate \underline{\textbf{the first}} large-scale Multi-view Multi-modal Word-Level Australian Sign Language recognition dataset, dubbed MM-WLAuslan. Compared to other publicly available datasets, MM-WLAuslan exhibits three significant advantages: (1) the largest amount of data, (2) the most extensive vocabulary, and (3) the most diverse of multi-modal camera views. Specifically, we record 282K+ sign videos covering 3,215 commonly used Auslan glosses presented by 73 signers in a studio environment.Moreover, our filming system includes two different types of cameras, i.e., three Kinect-V2 cameras and a RealSense camera. We position cameras hemispherically around the front half of the model and simultaneously record videos using all four cameras. Furthermore, we benchmark results with state-of-the-art methods for various multi-modal ISLR settings on MM-WLAuslan, including multi-view, cross-camera, and cross-view. Experiment results indicate that MM-WLAuslan is a challenging ISLR dataset, and we hope this dataset will contribute to the development of Auslan and the advancement of sign languages worldwide. All datasets and benchmarks are available at MM-WLAuslan.

Retinal vessel segmentation is generally grounded in image-based datasets collected with bench-top devices. The static images naturally lose the dynamic characteristics of retina fluctuation, resulting in diminished dataset richness, and the usage of bench-top devices further restricts dataset scalability due to its limited accessibility. Considering these limitations, we introduce the first video-based retinal dataset by employing handheld devices for data acquisition. The dataset comprises 635 smartphone-based fundus videos collected from four different clinics, involving 415 patients from 50 to 75 years old. It delivers comprehensive and precise annotations of retinal structures in both spatial and temporal dimensions, aiming to advance the landscape of vasculature segmentation. Specifically, the dataset provides three levels of spatial annotations: binary vessel masks for overall retinal structure delineation, general vein-artery masks for distinguishing the vein and artery, and fine-grained vein-artery masks for further characterizing the granularities of each artery and vein. In addition, the dataset offers temporal annotations that capture the vessel pulsation characteristics, assisting in detecting ocular diseases that require fine-grained recognition of hemodynamic fluctuation. In application, our dataset exhibits a significant domain shift with respect to data captured by bench-top devices, thus posing great challenges to existing methods. Thanks to rich annotations and data scales, our dataset potentially paves the path for more advanced retinal analysis and accurate disease diagnosis. In the experiments, we provide evaluation metrics and benchmark results on our dataset, reflecting both the potential and challenges it offers for vessel segmentation tasks. We hope this challenging dataset would significantly contribute to the development of eye disease diagnosis and early prevention.

Sign language translation (SLT) aims to convert a continuous sign language video clip into a spoken language. Considering different geographic regions generally have their own native sign languages, it is valuable to establish corresponding SLT datasets to support related communication and research. Auslan, as a sign language specific to Australia, still lacks a dedicated large-scale dataset for SLT.To fill this gap, we curate an Australian Sign Language translation dataset, dubbed Auslan-Daily, which is collected from the Auslan educational TV series and Auslan TV programs. The former involves daily communications among multiple signers in the wild, while the latter comprises sign language videos for up-to-date news, weather forecasts, and documentaries. In particular, Auslan-Daily has two main features: (1) the topics are diverse and signed by multiple signers, and (2) the scenes in our dataset are more complex, e.g., captured in various environments, gesture interference during multi-signers' interactions and various camera positions. With a collection of more than 45 hours of high-quality Auslan video materials, we invite Auslan experts to align different fine-grained visual and language pairs, including video $\leftrightarrow$ fingerspelling, video $\leftrightarrow$ gloss, and video $\leftrightarrow$ sentence. As a result, Auslan-Daily contains multi-grained annotations that can be utilized to accomplish various fundamental sign language tasks, such as signer detection, sign spotting, fingerspelling detection, isolated sign language recognition, sign language translation and alignment. Moreover, we benchmark results with state-of-the-art models for each task in Auslan-Daily. Experiments indicate that Auslan-Daily is a highly challenging SLT dataset, and we hope this dataset will contribute to the development of Auslan and the advancement of sign languages worldwide in a broader context. All datasets and benchmarks are available at Auslan-Daily.