Transparent object perception is indispensable for numerous robotic tasks. However, accurately segmenting and estimating the depth of transparent objects remain challenging due to complex optical properties. Existing methods primarily delve into only one task using extra inputs or specialized sensors, neglecting the valuable interactions among tasks and the subsequent refinement process, leading to suboptimal and blurry predictions. To address these issues, we propose a monocular framework, which is the first to excel in both segmentation and depth estimation of transparent objects, with only a single-image input. Specifically, we devise a novel semantic and geometric fusion module, effectively integrating the multi-scale information between tasks. In addition, drawing inspiration from human perception of objects, we further incorporate an iterative strategy, which progressively refines initial features for clearer results. Experiments on two challenging synthetic and real-world datasets demonstrate that our model surpasses state-of-the-art monocular, stereo, and multi-view methods by a large margin of about 38.8%-46.2% with only a single RGB input. Codes and models are publicly available at https://github.com/L-J-Yuan/MODEST.

Deep-learning-based object detection has gained widespread application in surface defect inspection, with anchor-based detectors achieving remarkable success by utilizing dense anchors to align with defects. Determining the optimal anchor configuration, i.e., sizes and aspect ratios of anchor boxes, remains a critical challenge, particularly when addressing defects with significant shape variations. While previous studies have predominantly focused on developing more efficient network architectures and learning strategies, the problem of anchor configuration determination has not been thoroughly explored. To address this gap, this paper proposes the IoU-Aware Clustering (IAC) algorithm, which autonomously learns suitable anchor configurations by extracting shape priors from diverse defects. IAC takes the training bounding boxes as potential clustering centers and selects a subset that aligns with the shape distribution of the training samples. The algorithm involves only a single hyper-parameter, the anchor number k, making it highly adaptable to various scenarios. Experimental results demonstrate that IAC can effectively generate anchor configurations tailored to defect shapes, significantly improving the mean Average Precision (mAP) by 6.9% and 14.4% on two industrial defect datasets with substantial shape variations.

Video content comprehension is essential for various applications, ranging from video analysis to interactive systems. Despite advancements in large-scale vision-language models (VLMs), these models often struggle to capture the nuanced, spatiotemporal details essential for thorough video analysis. To address this gap, we introduce Video-CoT, a groundbreaking dataset designed to enhance spatiotemporal understanding using Chain-of-Thought(CoT) methodologies. Video-CoT contains 192,000 fine-grained spatiotemporal question-answer pairs and 23,000 high-quality CoT-annotated samples, providing a solid foundation for evaluating spatiotemporal understanding in video comprehension. Addition- ally, we provide a comprehensive benchmark for assessing these tasks, with each task featuring 750 images and tailored evaluation metrics. Our extensive experiments reveal that current VLMs face significant challenges in achieving satisfactory performance, high- lighting the difficulties of effective spatiotemporal understanding. Overall, the Video-CoT dataset and benchmark open new avenues for research in multimedia understanding and support future innovations in intelligent systems requiring advanced video analysis capabilities. By making these resources publicly available, we aim to encourage further exploration in this critical area. Project website: https://video-cot.github.io/ .