160 citations · 162 across the 14 of their papers we have counts for
14 papers · 1 filter
Attend Locally, Remember Linearly: Linear Attention as Cross-Frame Memory for Autoregressive Video Diffusion
Kunyang Li, Mubarak Shah, Yuzhang Shang
Autoregressive (AR) video diffusion is a powerful paradigm for streaming and interactive video generation. However, its reliance on softmax self-attention leads to quadratic comput…
Aero-World: Action-Conditioned Aerial Video Generation from Inertial Controls
Abdul Mohaimen Al Radi, Kunyang Li, Yuzhang Shang +2
Foundation video models produce visually impressive results, but their use in embodied AI remains limited because they are primarily trained on natural language rather than low-lev…
Privacy Beyond Pixels: Latent Anonymization for Privacy-Preserving Video Understanding
Joseph Fioresi, Ishan Rajendrakumar Dave, Mubarak Shah
We introduce a novel formulation of visual privacy preservation for video foundation models that operates entirely in the latent space. While spatio-temporal features learned by fo…
From Play to Replay: Composed Video Retrieval for Temporally Fine-Grained Videos
Animesh Gupta, Jay Parmar, Ishan Rajendrakumar Dave +1
Composed Video Retrieval (CoVR) retrieves a target video given a query video and a modification text describing the intended change. Existing CoVR benchmarks emphasize appearance s…
ALBAR: Adversarial Learning approach to mitigate Biases in Action Recognition
Joseph Fioresi, Ishan Rajendrakumar Dave, Mubarak Shah
Bias in machine learning models can lead to unfair decision making, and while it has been well-studied in the image and text domains, it remains underexplored in action recognition…
FinePseudo: Improving Pseudo-Labelling through Temporal-Alignablity for Semi-Supervised Fine-Grained Action Recognition
Ishan Rajendrakumar Dave, Mamshad Nayeem Rizve, Mubarak Shah
Real-life applications of action recognition often require a fine-grained understanding of subtle movements, e.g., in sports analytics, user interactions in AR/VR, and surgical vid…
Sync from the Sea: Retrieving Alignable Videos from Large-Scale Datasets
Ishan Rajendrakumar Dave, Fabian Caba Heilbron, Mubarak Shah +1
Temporal video alignment aims to synchronize the key events like object interactions or action phase transitions in two videos. Such methods could benefit various video editing, pr…
GAReT: Cross-view Video Geolocalization with Adapters and Auto-Regressive Transformers
Manu S Pillai, Mamshad Nayeem Rizve, Mubarak Shah
Cross-view video geo-localization (CVGL) aims to derive GPS trajectories from street-view videos by aligning them with aerial-view images. Despite their promising performance, curr…
Open Vocabulary Multi-Label Video Classification
Rohit Gupta, Mamshad Nayeem Rizve, Jayakrishnan Unnikrishnan +5
Pre-trained vision-language models (VLMs) have enabled significant progress in open vocabulary computer vision tasks such as image classification, object detection and image segmen…
VidLA: Video-Language Alignment at Scale
Mamshad Nayeem Rizve, Fan Fei, Jayakrishnan Unnikrishnan +5
In this paper, we propose VidLA, an approach for video-language alignment at scale. There are two major limitations of previous video-language alignment approaches. First, they do…
CodaMal: Contrastive Domain Adaptation for Malaria Detection in Low-Cost Microscopes
Ishan Rajendrakumar Dave, Tristan de Blegiers, Chen Chen +1
Malaria is a major health issue worldwide, and its diagnosis requires scalable solutions that can work effectively with low-cost microscopes (LCM). Deep learning-based methods have…
No More Shortcuts: Realizing the Potential of Temporal Self-Supervision
Ishan Rajendrakumar Dave, Simon Jenni, Mubarak Shah
Self-supervised approaches for video have shown impressive results in video understanding tasks. However, unlike early works that leverage temporal self-supervision, current state-…
DVANet: Disentangling View and Action Features for Multi-View Action Recognition
Nyle Siddiqui, Praveen Tirupattur, Mubarak Shah
In this work, we present a novel approach to multi-view action recognition where we guide learned action representations to be separated from view-relevant information in a video.…
TCLR: Temporal Contrastive Learning for Video Representation
Ishan Dave, Rohit Gupta, Mamshad Nayeem Rizve +1
Contrastive learning has nearly closed the gap between supervised and self-supervised learning of image representations, and has also been explored for videos. However, prior work…