VLA models have emerged as a powerful paradigm for transferring semantic knowledge from web-scale data to physical robotic control.However, current single-frame architectures suffer from intrinsic limitations: temporal myopia that discards historical dynamics, reasoning gaps between high-level instructions and low-level motor commands, and inference inefficiency due to autoregressive scalar decoding.In this work, we propose MIRTH, a unified framework designed to address these challenges.MIRTH augments a pretrained VLA backbone with three key innovations: (1) dualscale temporal memory hubs that compress long-term scene evolution and short-term motion trends into compact embeddings; (2) latent reasoning tokens optimized via a mutualinformation objective carving out a semantic plan space to align multimodal context with action trajectories; and (3) a parallel action decoding scheme that replaces autoregressive generation with vector-wise prediction to maximize control throughput.Extensive evaluations on the LIBERO simulation benchmark and a real-world LeRobot platform demonstrate that MIRTH achieves state-of-the-art performance and exhibiting emergent error recovery capabilities.The codes and collected datasets are released at http://github.com/kiva12138/mirth.

Depression, a pervasive mental health condition, affects millions globally, challenging early and accurate diagnosis due to its subtle and varied manifestations. Recognizing the critical link between emotional dysregulation and depressive symptoms, our research introduces a pioneering training paradigm that integrates sentiment analysis with depression detection. This approach is motivated by the potential of sentiment data to enrich models with a deeper understanding of emotional states, crucial for identifying depressive patterns. To leverage the nuanced sentiment information without compromising the pretrained model’s integrity, we employ deep prefix tuning. This novel technique allows for targeted model refinement, ensuring that the valuable pretrained structures are not overshadowed by the sparse and specific nature of depression-related data. The empirical results demonstrate superior performance across standard benchmarks, setting a new precedent for multimodal depression detection.

Depression is a pervasive mental health disorder that remains frequently undiagnosed and untreated due to societal barriers and the subjective nature of its symptoms. Leveraging recent advances in large language models (LLMs), we propose a novel depression detection pipeline that generates emotion prompts tailored to individual data, enhancing detection accuracy. Our approach integrates cross-modality fusion via cross attention mechanisms to combine depressive and emotional features, creating a comprehensive representation of depression indicators. Evaluated on the E-DAIC and EATD datasets, our method outperforms state-of-the-art techniques, demonstrating its potential for more precise emotion-based depression detection.

Recently, foundation models have been introduced demonstrating various tasks in the field of computer vision. These models such as Segment Anything Model (SAM) are generalized models trained using huge datasets. Currently, ongoing research focuses on exploring the effective utilization of these generalized models for Specific domains, such as medical imaging. However, in medical imaging, the lack of training samples due to privacy concerns and other factors presents a major challenge for applying these generalized models to medical image segmentation task. To address this issue, the effective fine tuning of these models is crucial to ensure their optimal utilization. In this study, we propose to combine a complementary Convolutional Neural Network (CNN) along with the standard SAM network for medical image segmentation. To reduce the burden of fine tuning large foundation model and implement cost-efficient training scheme, we focus only on fine-tuning the additional CNN network and SAM decoder part. This strategy significantly reduces training time and achieves competitive results on publicly available dataset. The code is available at ">https://github.com/11yxk/SAM-LST .

Generating high-quality stitched images with natural structures is a challenging task in computer vision. In this paper, we succeed in preserving both local and global geometric structures for wide parallax images, while reducing artifacts and distortions. A projective invariant, Characteristic Number, is used to match co-planar local sub-regions for input images. The homography between these well-matched sub-regions produces consistent line and point pairs, suppressing artifacts in overlapping areas. We explore and introduce global collinear structures into an objective function to specify and balance the desired characters for image warping, which can preserve both local and global structures while alleviating distortions. We also develop comprehensive measures for stitching quality to quantify the collinearity of points and the discrepancy of matched line pairs by considering the sensitivity to linear structures for human vision. Extensive experiments demonstrate the superior performance of the proposed method over the state-of-the-art by presenting sharp textures and preserving prominent natural structures in stitched images. Especially, our method not only exhibits lower errors but also the least divergence across all test images. Code is available at https://github.com/dut-media-lab/Image-Stitching.