16 citations · 18 across the 8 of their papers we have counts for
10 papers
VidParse: Online Parsing of Egocentric Procedures Like a Pro
Anubhav Gupta, Archit Kambhamettu, Vatsal Agarwal +2
Translating continuous, noisy egocentric video streams into discrete, temporally ordered action steps is fraught with visual challenges. Heavy ego-motion, transient occlusions, and…
MMOU: A Massive Multi-Task Omni Understanding and Reasoning Benchmark for Long and Complex Real-World Videos
Arushi Goel, Sreyan Ghosh, Vatsal Agarwal +16
Multimodal Large Language Models (MLLMs) have shown strong performance in visual and audio understanding when evaluated in isolation. However, their ability to jointly reason over…
Going Down Memory Lane: Scaling Tokens for Video Stream Understanding with Dynamic KV-Cache Memory
Vatsal Agarwal, Saksham Suri, Matthew Gwilliam +2
Streaming video understanding requires models to robustly encode, store, and retrieve information from a continuous video stream to support accurate video question answering (VQA).…
Towards Multimodal Understanding via Stable Diffusion as a Task-Aware Feature Extractor
Vatsal Agarwal, Matthew Gwilliam, Gefen Kohavi +3
Recent advances in multimodal large language models (MLLMs) have enabled image-based question-answering capabilities. However, a key limitation is the use of CLIP as the visual enc…
LEIA: Latent View-invariant Embeddings for Implicit 3D Articulation
Archana Swaminathan, Anubhav Gupta, Kamal Gupta +3
Neural Radiance Fields (NeRFs) have revolutionized the reconstruction of static scenes and objects in 3D, offering unprecedented quality. However, extending NeRFs to model dynamic…
Do text-free diffusion models learn discriminative visual representations?
Soumik Mukhopadhyay, Matthew Gwilliam, Yosuke Yamaguchi +6
While many unsupervised learning models focus on one family of tasks, either generative or discriminative, we explore the possibility of a unified representation learner: a model w…
Diffusion Models Beat GANs on Image Classification
Soumik Mukhopadhyay, Matthew Gwilliam, Vatsal Agarwal +5
While many unsupervised learning models focus on one family of tasks, either generative or discriminative, we explore the possibility of a unified representation learner: a model w…
A Frequency Perspective of Adversarial Robustness
Shishira R Maiya, Max Ehrlich, Vatsal Agarwal +3
Adversarial examples pose a unique challenge for deep learning systems. Despite recent advances in both attacks and defenses, there is still a lack of clarity and consensus in the…
Weakly Supervised Lesion Co-segmentation on CT Scans
Vatsal Agarwal, Youbao Tang, Jing Xiao +1
Lesion segmentation in medical imaging serves as an effective tool for assessing tumor sizes and monitoring changes in growth. However, not only is manual lesion segmentation time-…
Weakly-Supervised Lesion Segmentation on CT Scans using Co-Segmentation
Vatsal Agarwal, Youbao Tang, Jing Xiao +1
Lesion segmentation on computed tomography (CT) scans is an important step for precisely monitoring changes in lesion/tumor growth. This task, however, is very challenging since ma…