1.8k citations · 2.3k across the 36 of their papers we have counts for
16 papers · 1 filter
Ensemble Modeling for Multimodal Visual Action Recognition
Jyoti Kini, Sarah Fleischer, Ishan Dave +1
In this work, we propose an ensemble modeling approach for multimodal action recognition. We independently train individual modality models using a variant of focal loss tailored t…
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…
TeD-SPAD: Temporal Distinctiveness for Self-supervised Privacy-preservation for video Anomaly Detection
Joseph Fioresi, Ishan Rajendrakumar Dave, Mubarak Shah
Video anomaly detection (VAD) without human monitoring is a complex computer vision task that can have a positive impact on society if implemented successfully. While recent advanc…
Reverse Stable Diffusion: What prompt was used to generate this image?
Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu +1
Text-to-image diffusion models have recently attracted the interest of many researchers, and inverting the diffusion process can play an important role in better understanding the…
Foundational Models Defining a New Era in Vision: A Survey and Outlook
Muhammad Awais, Muzammal Naseer, Salman Khan +5
Vision systems to see and reason about the compositional nature of visual scenes are fundamental to understanding our world. The complex relations between objects and their locatio…
Frequency Domain Adversarial Training for Robust Volumetric Medical Segmentation
Asif Hanif, Muzammal Naseer, Salman Khan +2
It is imperative to ensure the robustness of deep learning models in critical applications such as, healthcare. While recent advances in deep learning have improved the performance…
Video-FocalNets: Spatio-Temporal Focal Modulation for Video Action Recognition
Syed Talal Wasim, Muhammad Uzair Khattak, Muzammal Naseer +3
Recent video recognition models utilize Transformer models for long-range spatio-temporal context modeling. Video transformer designs are based on self-attention that can model glo…
Exploiting the Brain's Network Structure for Automatic Identification of ADHD Subjects
Soumyabrata Dey, Ravishankar Rao, Mubarak Shah
Attention Deficit Hyperactive Disorder (ADHD) is a common behavioral problem affecting children. In this work, we investigate the automatic classification of ADHD subjects using th…
Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectors
Nicolae-Catalin Ristea, Florinel-Alin Croitoru, Radu Tudor Ionescu +3
We propose an efficient abnormal event detection model based on a lightweight masked auto-encoder (AE) applied at the video frame level. The novelty of the proposed model is threef…
Learning Situation Hyper-Graphs for Video Question Answering
Aisha Urooj Khan, Hilde Kuehne, Bo Wu +5
Answering questions about complex situations in videos requires not only capturing the presence of actors, objects, and their relations but also the evolution of these relationship…
Former: Unified etrieval and eranking Transformer for Place Recognition
Sijie Zhu, Linjie Yang, Chen Chen +3
Visual Place Recognition (VPR) estimates the location of query images by matching them with images in a reference database. Conventional methods generally adopt aggregated CNN feat…
Vita-CLIP: Video and text adaptive CLIP via Multimodal Prompting
Syed Talal Wasim, Muzammal Naseer, Salman Khan +2
Adopting contrastive image-text pretrained models like CLIP towards video classification has gained attention due to its cost-effectiveness and competitive performance. However, re…
Video Instance Segmentation in an Open-World
Omkar Thawakar, Sanath Narayan, Hisham Cholakkal +5
Existing video instance segmentation (VIS) approaches generally follow a closed-world assumption, where only seen category instances are identified and spatio-temporally segmented…
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…
3D Mitochondria Instance Segmentation with Spatio-Temporal Transformers
Omkar Thawakar, Rao Muhammad Anwer, Jorma Laaksonen +3
Accurate 3D mitochondria instance segmentation in electron microscopy (EM) is a challenging problem and serves as a prerequisite to empirically analyze their distributions and morp…