Xuyang Zou

Tao Feng, Tingfa Xu, Haolin Qin et al.

Visual object tracking in real-world scenarios presents numerous challenges including occlusion, interference from similar objects and complex backgrounds-all of which limit the effectiveness of RGB-based trackers. Multispectral imagery, which captures pixel-level spectral reflectance, enhances target discriminability. However, the availability of multispectral tracking datasets remains limited. To bridge this gap, we introduce MSITrack, the largest and most diverse multispectral single object tracking dataset to date. MSITrack offers the following key features: (i) More Challenging Attributes-including interference from similar objects and similarity in color and texture between targets and backgrounds in natural scenarios, along with a wide range of real-world tracking challenges; (ii) Richer and More Natural Scenes-spanning 55 object categories and 300 distinct natural scenes, MSITrack far exceeds the scope of existing benchmarks. Many of these scenes and categories are introduced to the multispectral tracking domain for the first time; (iii) Larger Scale-300 videos comprising over 129k frames of multispectral imagery. To ensure annotation precision, each frame has undergone meticulous processing, manual labeling and multi-stage verification. Extensive evaluations using representative trackers demonstrate that the multispectral data in MSITrack significantly improves performance over RGB-only baselines, highlighting its potential to drive future advancements in the field. The MSITrack dataset is publicly available at: https://github.com/Fengtao191/MSITrack.

Zihan Liu, Jiayi Wen, Junru Wu et al.

In this paper, we introduce PrivDFS, a distributed feature-sharing framework for input-private inference in image classification. A single holistic intermediate representation in split inference gives diffusion-based Data Reconstruction Attacks (DRAs) sufficient signal to reconstruct the input with high fidelity. PrivDFS restructures this vulnerability by fragmenting the representation and processing the fragments independently across a majority-honest set of servers. As a result, each branch observes only an incomplete and reconstruction-insufficient view of the input. To realize this, PrivDFS employs learnable binary masks that partition the intermediate representation into sparse and largely non-overlapping feature shares, each processed by a separate server, while a lightweight fusion module aggregates their predictions on the client. This design preserves full task accuracy when all branches are combined, yet sharply limits the reconstructive power available to any individual server. PrivDFS applies seamlessly to both ResNet-based CNNs and Vision Transformers. Across CIFAR-10/100, CelebA, and ImageNet-1K, PrivDFS induces a pronounced collapse in DRA performance, e.g., on CIFAR-10, PSNR drops from 23.25 -> 12.72 and SSIM from 0.963 -> 0.260, while maintaining accuracy within 1% of non-private split inference. These results establish structural feature partitioning as a practical and architecture-agnostic approach to reducing reconstructive leakage in cloud-based vision inference.