Shehbaz Tariq

Shehbaz Tariq, Brian Estadimas Arfeto, Chaoning Zhang et al.

In light of the diminishing returns of traditional methods for enhancing transmission rates, the domain of semantic communication presents promising new frontiers. Focusing on image transmission, this paper explores the application of foundation models, particularly the Segment Anything Model (SAM) developed by Meta AI Research, to improve semantic communication. SAM is a promptable image segmentation model that has gained attention for its ability to perform zero-shot segmentation tasks without explicit training or domain-specific knowledge. By employing SAM's segmentation capability and lightweight neural network architecture for semantic coding, we propose a practical approach to semantic communication. We demonstrate that this approach retains critical semantic features, achieving higher image reconstruction quality and reducing communication overhead. This practical solution eliminates the resource-intensive stage of training a segmentation model and can be applied to any semantic coding architecture, paving the way for real-world applications.

Chaoning Zhang, Yu Qiao, Shehbaz Tariq et al.

In contrast to the human vision that mainly depends on the shape for recognizing the objects, deep image recognition models are widely known to be biased toward texture. Recently, Meta research team has released the first foundation model for image segmentation, termed segment anything model (SAM), which has attracted significant attention. In this work, we understand SAM from the perspective of texture \textit{v.s.} shape. Different from label-oriented recognition tasks, the SAM is trained to predict a mask for covering the object shape based on a promt. With this said, it seems self-evident that the SAM is biased towards shape. In this work, however, we reveal an interesting finding: the SAM is strongly biased towards texture-like dense features rather than shape. This intriguing finding is supported by a novel setup where we disentangle texture and shape cues and design texture-shape cue conflict for mask prediction.

Junaid ur Rehman, Shehbaz Tariq, Symeon Chatzinotas

We consider a general and practical scenario of quantum key distribution (QKD) over an unknown, stationary, unital qubit channel. Furthermore, due to practical limitations, e.g., relative movement and rotation of communicating parties, a global shared reference frame cannot be established. This scenario can routinely appear in satellite QKD. We propose two methods to overcome the physical qubit noise and the lack of shared reference frame. The first proposed approach involves constructing the Pauli transfer matrix (PTM) description of the channel, which we achieve without requiring a shared reference frame, by absorbing the lack of shared reference frame in the channel definition. This is followed by the identification of singular vectors of PTM as the Bloch vectors for optimal signal states. In the optimized local bases, the resulting correlations are equivalent, up to outcome relabeling, to those of a Pauli channel, allowing us to show the optimality of the BB84 and six-state QKD protocols under these conditions. The second approach, called the sequential basis matching (SBM) involves sequentially identifying the channel-optimized local bases that enable QKD. We show that both of these approaches result in the same effective key exchange rate for QKD.

Brian E. Arfeto, Shehbaz Tariq, Uman Khalid et al.

We introduce a prototyping testbed, GenSC-6G, developed to generate a comprehensive dataset that supports the integration of generative artificial intelligence (AI), quantum computing, and semantic communication for emerging sixth-generation (6G) applications. The GenSC-6G dataset is designed with noise-augmented synthetic data optimized for semantic decoding, classification, and localization tasks, significantly enhancing flexibility for diverse AI-driven communication applications. This adaptable prototype supports seamless modifications across baseline models, communication modules, and goal-oriented decoders. Case studies demonstrate its application in lightweight classification, semantic upsampling, and edge-based language inference under noise conditions. The GenSC-6G dataset serves as a scalable and robust resource for developing goal-oriented communication systems tailored to the growing demands of 6G networks.