5 papers
Rethinking Prompt Design for Inference-time Scaling in Text-to-Visual Generation
Subin Kim, Sangwoo Mo, Mamshad Nayeem Rizve +4
Achieving precise alignment between user intent and generated visuals remains a central challenge in text-to-visual generation, as a single attempt often fails to produce the desir…
CompSlider: Compositional Slider for Disentangled Multiple-Attribute Image Generation
Zixin Zhu, Kevin Duarte, Mamshad Nayeem Rizve +3
In text-to-image (T2I) generation, achieving fine-grained control over attributes - such as age or smile - remains challenging, even with detailed text prompts. Slider-based method…
Unified Alignment Protocol: Making Sense of the Unlabeled Data in New Domains
Sabbir Ahmed, Mamshad Nayeem Rizve, Abdullah Al Arafat +4
Semi-Supervised Federated Learning (SSFL) is gaining popularity over conventional Federated Learning in many real-world applications. Due to the practical limitation of limited lab…
FinePseudo: Improving Pseudo-Labelling through Temporal-Alignablity for Semi-Supervised Fine-Grained Action Recognition
Ishan Rajendrakumar Dave, Mamshad Nayeem Rizve, Mubarak Shah
Real-life applications of action recognition often require a fine-grained understanding of subtle movements, e.g., in sports analytics, user interactions in AR/VR, and surgical vid…
GAReT: Cross-view Video Geolocalization with Adapters and Auto-Regressive Transformers
Manu S Pillai, Mamshad Nayeem Rizve, Mubarak Shah
Cross-view video geo-localization (CVGL) aims to derive GPS trajectories from street-view videos by aligning them with aerial-view images. Despite their promising performance, curr…