61 citations · 83 across the 10 of their papers we have counts for
13 papers
Semantic Action Graph: A Shared Representation for Agent Grounding and Human Interpretation of Sports Highlights
Tica Lin, Deepak Chandran, Gauri Jagatap +4
Generative agents are increasingly used to select and narrate video highlights, but they typically operate over unstructured or frame-level representations. Their output is consequ…
Video2Reaction: Training Foundation Video Models to Predict Audience Reaction
Sidong Zhang, Trang Nguyen, Shiv Shankar +4
We introduce Video2Reaction, a multimodal dataset that maps short movie segments to the induced emotional reactions of viewers in the wild, as expressed through social media commen…
Video2Reaction: Mapping Video to Audience Reaction Distribution in the Wild
Trang Nguyen, Sidong Zhang, Shiv Shankar +4
Understanding and forecasting audience reactions to video content are crucial for improving content creation, recommendation systems, and media analysis. To enable audience reactio…
OnPoint: Offline-to-Online Multi-Level Distillation for Point-Supervised Online Temporal Action Localization
Sakib Reza, Gauri Jagatap, Mohsen Moghaddam +2
Temporal Action Localization (TAL) typically relies on segment annotations or offline access to full videos, limiting scalability and online use. We introduce Point-Supervised Onli…
Towards Mitigating Hallucinations in Large Vision-Language Models by Refining Textual Embeddings
Aakriti Agrawal, Gouthaman KV, Rohith Aralikatti +6
Hallucinations in Large Vision-Language Models (LVLMs) remain a persistent challenge, often stemming from inadequate integration of visual information during multimodal reasoning.…
Moment Sampling in Video LLMs for Long-Form Video QA
Mustafa Chasmai, Gauri Jagatap, Gouthaman KV +3
Recent advancements in video large language models (Video LLMs) have significantly advanced the field of video question answering (VideoQA). While existing methods perform well on…
Can deepfakes be created by novice users?
Pulak Mehta, Gauri Jagatap, Kevin Gallagher +5
Recent advancements in machine learning and computer vision have led to the proliferation of Deepfakes. As technology democratizes over time, there is an increasing fear that novic…
Adversarial Token Attacks on Vision Transformers
Ameya Joshi, Gauri Jagatap, Chinmay Hegde
Vision transformers rely on a patch token based self attention mechanism, in contrast to convolutional networks. We investigate fundamental differences between these two families o…
Provable Compressed Sensing with Generative Priors via Langevin Dynamics
Thanh V. Nguyen, Gauri Jagatap, Chinmay Hegde
Deep generative models have emerged as a powerful class of priors for signals in various inverse problems such as compressed sensing, phase retrieval and super-resolution. Here, we…
Adversarially Robust Learning via Entropic Regularization
Gauri Jagatap, Ameya Joshi, Animesh Basak Chowdhury +2
In this paper we propose a new family of algorithms, ATENT, for training adversarially robust deep neural networks. We formulate a new loss function that is equipped with an additi…
Algorithmic Guarantees for Inverse Imaging with Untrained Network Priors
Gauri Jagatap, Chinmay Hegde
Deep neural networks as image priors have been recently introduced for problems such as denoising, super-resolution and inpainting with promising performance gains over hand-crafte…
Learning ReLU Networks via Alternating Minimization
Gauri Jagatap, Chinmay Hegde
We propose and analyze a new family of algorithms for training neural networks with ReLU activations. Our algorithms are based on the technique of alternating minimization: estimat…
Sample-Efficient Algorithms for Recovering Structured Signals from Magnitude-Only Measurements
Gauri Jagatap, Chinmay Hegde
We consider the problem of recovering a signal , from magnitude-only measurements for $i…