2 citations · 6 across the 8 of their papers we have counts for
8 papers
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…
X-Former: Unifying Contrastive and Reconstruction Learning for MLLMs
Sirnam Swetha, Jinyu Yang, Tal Neiman +5
Recent advancements in Multimodal Large Language Models (MLLMs) have revolutionized the field of vision-language understanding by integrating visual perception capabilities into La…
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…
CDFSL-V: Cross-Domain Few-Shot Learning for Videos
Sarinda Samarasinghe, Mamshad Nayeem Rizve, Navid Kardan +1
Few-shot video action recognition is an effective approach to recognizing new categories with only a few labeled examples, thereby reducing the challenges associated with collectin…
Preserving Modality Structure Improves Multi-Modal Learning
Swetha Sirnam, Mamshad Nayeem Rizve, Nina Shvetsova +2
Self-supervised learning on large-scale multi-modal datasets allows learning semantically meaningful embeddings in a joint multi-modal representation space without relying on human…
TimeBalance: Temporally-Invariant and Temporally-Distinctive Video Representations for Semi-Supervised Action Recognition
Ishan Rajendrakumar Dave, Mamshad Nayeem Rizve, Chen Chen +1
Semi-Supervised Learning can be more beneficial for the video domain compared to images because of its higher annotation cost and dimensionality. Besides, any video understanding t…