160 citations · 255 across the 14 of their papers we have counts for
14 papers
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…
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…
Is ImageNet worth 1 video? Learning strong image encoders from 1 long unlabelled video
Shashanka Venkataramanan, Mamshad Nayeem Rizve, João Carreira +2
Self-supervised learning has unlocked the potential of scaling up pretraining to billions of images, since annotation is unnecessary. But are we making the best use of data? How mo…
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…
Towards Realistic Semi-Supervised Learning
Mamshad Nayeem Rizve, Navid Kardan, Mubarak Shah
Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature…
OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning
Mamshad Nayeem Rizve, Navid Kardan, Salman Khan +2
Semi-supervised learning (SSL) is one of the dominant approaches to address the annotation bottleneck of supervised learning. Recent SSL methods can effectively leverage a large re…
UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning
Nazmul Karim, Mamshad Nayeem Rizve, Nazanin Rahnavard +2
Supervised deep learning methods require a large repository of annotated data; hence, label noise is inevitable. Training with such noisy data negatively impacts the generalization…
Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning
Mamshad Nayeem Rizve, Salman Khan, Fahad Shahbaz Khan +1
In many real-world problems, collecting a large number of labeled samples is infeasible. Few-shot learning (FSL) is the dominant approach to address this issue, where the objective…
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…
In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning
Mamshad Nayeem Rizve, Kevin Duarte, Yogesh S Rawat +1
The recent research in semi-supervised learning (SSL) is mostly dominated by consistency regularization based methods which achieve strong performance. However, they heavily rely o…
Gabriella: An Online System for Real-Time Activity Detection in Untrimmed Security Videos
Mamshad Nayeem Rizve, Ugur Demir, Praveen Tirupattur +5
Activity detection in security videos is a difficult problem due to multiple factors such as large field of view, presence of multiple activities, varying scales and viewpoints, an…