2.7k citations · 3.5k across the 19 of their papers we have counts for
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SALD: Self-Referenced Advantage Learning for Diffusion Models
Aryan Das, Surjo Dey, Koushik Biswas +4
Recent work on language-model adaptation has shown that single models can obtain informative training signals by evaluating their behavior in demonstrationor feedback-augmented con…
ProCAP: Probabilistic Cross-Attentive Prompt Learning for Vision-Language Models
Hiwa Azeez Abbas, Fatemeh Daneshfar, Moloud Abdar
Pre-trained vision-language models such as CLIP can recognize new categories via prompting, but they often struggle when labeled data are scarce or the test distribution shifts. Pr…
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…
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 +5
Category-level multi-object trackers estimate trajectories for all detected instances, whereas some applications require propagating only targets specified by a user. We study offl…
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…