7 papers
When Trackers Fail: VLM-Guided Verification and Recovery for Robust Video Object Segmentation
Valay Bundele, Susmit Agrawal, Mehran Hosseinzadeh +2
Recent trackers built on foundation models such as the Segment Anything Model (SAM) achieve strong performance by propagating object masks through memory. However, this strength is…
Moving Forward with Video Saliency: A New Dataset and Benchmark where Motion Matters
Susmit Agrawal, Rebecca Wanner, Juliane Verwiebe +3
Video saliency prediction is inherently harder to model than static image saliency due to the additional temporal dimension. Video saliency benchmarks rest on the premise that pred…
DeepGaze3.5-VL: Modeling Scanpaths via Autoregressive Token Prediction
Susmit Agrawal, Matthias Bethge, Matthias Kümmerer
Understanding human visual attention on a scene over time has applications in domains such as interface design and inferring cognitive states. Modeling visual scanpaths has histori…
Raising the Ceiling: Better Empirical Fixation Densities for Saliency Benchmarking
Susmit Agrawal, Jannis Hollman, Matthias Kümmerer
Empirical fixation densities, spatial distributions estimated from human eye-tracking data, are foundational to saliency benchmarking. They directly shape benchmark conclusions, le…
Memory-Integrated Reconfigurable Adapters: A Unified Framework for Settings with Multiple Tasks
Susmit Agrawal, Krishn Vishwas Kher, Saksham Mittal +2
Organisms constantly pivot between tasks such as evading predators, foraging, traversing rugged terrain, and socializing, often within milliseconds. Remarkably, they preserve knowl…
Walking the Web of Concept-Class Relationships in Incrementally Trained Interpretable Models
Susmit Agrawal, Deepika Vemuri, Sri Siddarth Chakaravarthy P +1
Concept-based methods have emerged as a promising direction to develop interpretable neural networks in standard supervised settings. However, most works that study them in increme…
Analyzing Memorization in Large Language Models through the Lens of Model Attribution
Tarun Ram Menta, Susmit Agrawal, Chirag Agarwal
Large Language Models (LLMs) are prevalent in modern applications but often memorize training data, leading to privacy breaches and copyright issues. Existing research has mainly f…