Yi Zhu

ZhiChao Yan, Hui Xue, Yi Zhu et al.

Accurate segmentation of lesions in pancreatic endoscopic ultrasound (EUS) images is crucial for effective diagnosis and treatment. However, the collection of enough crisp EUS images for effective diagnosis is arduous. Recently, domain adaptation (DA) has been employed to address these challenges by leveraging related knowledge from other domains. Most DA methods only focus on multi-view representations of the same organ, which makes it still tough to clearly depict the tumor lesion area with limited semantic information. Although transferring homogeneous similarity from different organs could benefit the issue, there is a lack of relevant work due to the enormous domain gap between them. To address these challenges, we propose the Cross-Organ Tumor Segmentation Networks (COTS-Nets), consisting of a universal network and an auxiliary network. The universal network utilizes boundary loss to learn common boundary information of different tumors, enabling accurate delineation of tumors in EUS despite limited and low-quality data. Simultaneously, we incorporate consistency loss in the universal network to align the prediction of pancreatic EUS with tumor boundaries from other organs to mitigate the domain gap. To further reduce the cross-organ domain gap, the auxiliary network integrates multi-scale features from different organs, aiding the universal network in acquiring domain-invariant knowledge. Systematic experiments demonstrate that COTS-Nets significantly improves the accuracy of pancreatic cancer diagnosis. Additionally, we developed the Pancreatic Cancer Endoscopic Ultrasound (PCEUS) dataset, comprising 501 pathologically confirmed pancreatic EUS images, to facilitate model development.

Sixiao Zheng, Zimian Peng, Yanpeng Zhou et al.

Controllable image-to-video (I2V) generation transforms a reference image into a coherent video guided by user-specified control signals. In content creation workflows, precise and simultaneous control over camera motion, object motion, and lighting direction enhances both accuracy and flexibility. However, existing approaches typically treat these control signals separately, largely due to the scarcity of datasets with high-quality joint annotations and mismatched control spaces across modalities. We present VidCRAFT3, a unified and flexible I2V framework that supports both independent and joint control over camera motion, object motion, and lighting direction by integrating three core components. Image2Cloud reconstructs a 3D point cloud from the reference image to enable precise camera motion control. ObjMotionNet encodes sparse object trajectories into multi-scale optical flow features to guide object motion. The Spatial Triple-Attention Transformer integrates lighting direction embeddings via parallel cross-attention. To address the scarcity of jointly annotated data, we curate the VideoLightingDirection (VLD) dataset of synthetic static-scene video clips with per-frame lighting-direction labels, and adopt a three-stage training strategy that enables robust learning without fully joint annotations. Extensive experiments show that VidCRAFT3 outperforms existing methods in control precision and visual coherence. Code and data will be released. Project page: https://sixiaozheng.github.io/VidCRAFT3/.

Yuxin Ren, Zihan Zhong, Xingjian Shi et al.

It has been commonly observed that a teacher model with superior performance does not necessarily result in a stronger student, highlighting a discrepancy between current teacher training practices and effective knowledge transfer. In order to enhance the guidance of the teacher training process, we introduce the concept of distillation influence to determine the impact of distillation from each training sample on the student's generalization ability. In this paper, we propose Learning Good Teacher Matters (LGTM), an efficient training technique for incorporating distillation influence into the teacher's learning process. By prioritizing samples that are likely to enhance the student's generalization ability, our LGTM outperforms 10 common knowledge distillation baselines on 6 text classification tasks in the GLUE benchmark.