7 papers
UNITY: Attention Flow Networks for Adaptive Conditioning in Diffusion
Aryan Das, Koushik Biswas, Moloud Abdar +1
We introduce UNITY, a Universal-to-Specialized adapter for efficient and scalable composite conditioning in diffusion based image generation. Unlike prior methods that train separa…
ATR-Bench: A Federated Learning Benchmark for Adaptation, Trust, and Reasoning
Tajamul Ashraf, Mohammed Mohsen Peerzada, Moloud Abdar +5
Federated Learning (FL) has emerged as a promising paradigm for collaborative model training while preserving data privacy across decentralized participants. As FL adoption grows,…
FOCUS: Bridging Fine-Grained Recognition and Open-World Discovery across Domains
Vaibhav Rathore, Divyam Gupta, Moloud Abdar +2
We introduce the first unified framework for *Fine-Grained Domain-Generalized Generalized Category Discovery* (FG-DG-GCD), bringing open-world recognition closer to real-world depl…
QTrack: Query-Driven Reasoning for Multi-modal MOT
Tajamul Ashraf, Tavaheed Tariq, Sonia Yadav +4
Multi-object tracking (MOT) has traditionally focused on estimating trajectories of all objects in a video, without selectively reasoning about user-specified targets under semanti…
GroundedSurg: A Multi-Procedure Benchmark for Language-Conditioned Surgical Tool Segmentation
Tajamul Ashraf, Abrar Ul Riyaz, Wasif Tak +4
Clinically reliable perception of surgical scenes is essential for advancing intelligent, context-aware intraoperative assistance such as instrument handoff guidance, collision avo…
FedMVP: Federated Multimodal Visual Prompt Tuning for Vision-Language Models
Mainak Singha, Subhankar Roy, Sarthak Mehrotra +4
In federated learning, textual prompt tuning adapts Vision-Language Models (e.g., CLIP) by tuning lightweight input tokens (or prompts) on local client data, while keeping network…