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20242026
most citedX-Former: Unifying Contrastive and Reconstruction Learning for MLLMs

1 citations · 1 across the 10 of their papers we have counts for

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cs.CV2026

ReasonCLIP-58M: Visually Grounded Commonsense Reasoning Supervision for CLIP

Sicheng Zhang, Muzammal Naseer, Binzhu Xie +5

CLIP and its variants are widely adopted visual backbones in multimodal systems, but their pretraining remains dominated by descriptive image-text alignment. As downstream applicat…

cs.CV2026

TIGeR: A Unified Framework for Time, Images and Geo-location Retrieval

David G. Shatwell, Sirnam Swetha, Mubarak Shah

Many real-world applications in digital forensics, urban monitoring, and environmental analysis require jointly reasoning about visual appearance, location, and time. Beyond standa…

cs.CV2025

StretchySnake: Flexible SSM Training Unlocks Action Recognition Across Spatio-Temporal Scales

Nyle Siddiqui, Rohit Gupta, Sirnam Swetha +1

State space models (SSMs) have emerged as a competitive alternative to transformers in various tasks. Their linear complexity and hidden-state recurrence make them particularly att…

cs.CV2025

Safe-LLaVA: A Privacy-Preserving Vision-Language Dataset and Benchmark for Biometric Safety

Younggun Kim, Sirnam Swetha, Fazil Kagdi +1

Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in vision-language tasks. However, these models often infer and reveal sensitive biometric attrib…

cs.CV2025

Cross-View Open-Vocabulary Object Detection in Aerial Imagery

Jyoti Kini, Rohit Gupta, Mubarak Shah

Traditional object detection models are typically trained on a fixed set of classes, limiting their flexibility and making it costly to incorporate new categories. Open-vocabulary…

cs.CV2025

The Telephone Game: Evaluating Semantic Drift in Unified Models

Sabbir Mollah, Rohit Gupta, Sirnam Swetha +3

Unified models (UMs) combine visual understanding (I2T) and generation (T2I) in a single framework. We focus on T2I and I2T, where cross-consistency---what a model understands, it…