Video-based Large Language Models (Video-LLMs) have witnessed substantial advancements in recent years, propelled by the advancement in multi-modal LLMs. Although these models have demonstrated proficiency in providing the overall description of videos, they struggle with fine-grained understanding, particularly in aspects such as visual dynamics and video details inquiries. To tackle these shortcomings, we find that fine-tuning Video-LLMs on self-supervised fragment tasks, greatly improve their fine-grained video understanding abilities. Hence we propose two key contributions: (1) Self-Supervised Fragment Fine-Tuning (SF2T), a novel effortless fine-tuning method, employs the rich inherent characteristics of videos for training, while unlocking more fine-grained understanding ability of Video-LLMs. Moreover, it relieves researchers from labor-intensive annotations and smartly circumvents the limitations of natural language, which often fails to capture the complex spatiotemporal variations in videos; (2) A novel benchmark dataset, namely FineVidBench, for rigorously assessing Video-LLMs’ performance at both the scene and fragment levels, offering a comprehensive evaluation of their capabilities. We assessed multiple models and validated the effectiveness of SF2T on them. Experimental results reveal that our approach improves their ability to capture and interpret spatiotemporal details.

Segmentation of brain structures from MRI is crucial for evaluating brain morphology, yet existing CNN and transformer-based methods struggle to delineate complex structures accurately. While current diffusion models have shown promise in image segmentation, they are inadequate when applied directly to brain MRI due to neglecting anatomical information. To address this, we propose Collaborative Anatomy Diffusion (CA-Diff), a framework integrating spatial anatomical features to enhance segmentation accuracy of the diffusion model. Specifically, we introduce distance field as an auxiliary anatomical condition to provide global spatial context, alongside a collaborative diffusion process to model its joint distribution with anatomical structures, enabling effective utilization of anatomical features for segmentation. Furthermore, we introduce a consistency loss to refine relationships between the distance field and anatomical structures and design a time adapted channel attention module to enhance the U-Net feature fusion procedure. Extensive experiments show that CA-Diff outperforms state-of-the-art (SOTA) methods.

Automatic Breast Ultrasound (ABUS) image segmentation is of great significance for breast cancer diagnosis and treatment. However, similar to most medical datasets, ABUS image datasets are often small-scale and seriously imbalanced, which makes ABUS image segmentation become a challenge. To solve this problem, we propose the Residual Swin Transformer Unet with Consistency Regularization (RSTUnet-CR) which can make full use of non-lesion and unlabeled images for high-precision tumor segmentation on ABUS images. We design a consistency-regularization decoder to reconstruct the input image, which can learn well from non-lesion and unlabeled data. The reconstruction task makes the model more suitable for the imbalanced medical image datasets. In addition, observing that the ABUS images have global semantic correlation, we establish long-distance dependence of images by the residual Swin Transformer block to improve segmentation performance. We evaluate our method on the ABUS dataset collected from 256 subjects and demonstrate the superiority of the proposed method over other state-of-the-art methods in this imbalanced dataset.