70 citations · 299 across the 35 of their papers we have counts for
36 papers
Open-Vocabulary Temporal Action Localization using Multimodal Guidance
Akshita Gupta, Aditya Arora, Sanath Narayan +3
Open-Vocabulary Temporal Action Localization (OVTAL) enables a model to recognize any desired action category in videos without the need to explicitly curate training data for all…
Progressive Semantic-Guided Vision Transformer for Zero-Shot Learning
Shiming Chen, Wenjin Hou, Salman Khan +1
Zero-shot learning (ZSL) recognizes the unseen classes by conducting visual-semantic interactions to transfer semantic knowledge from seen classes to unseen ones, supported by sema…
Efficient Video Object Segmentation via Modulated Cross-Attention Memory
Abdelrahman Shaker, Syed Talal Wasim, Martin Danelljan +3
Recently, transformer-based approaches have shown promising results for semi-supervised video object segmentation. However, these approaches typically struggle on long videos due t…
Cal-DETR: Calibrated Detection Transformer
Muhammad Akhtar Munir, Salman Khan, Muhammad Haris Khan +2
Albeit revealing impressive predictive performance for several computer vision tasks, deep neural networks (DNNs) are prone to making overconfident predictions. This limits the ado…
How Good is Google Bard's Visual Understanding? An Empirical Study on Open Challenges
Haotong Qin, Ge-Peng Ji, Salman Khan +3
Google's Bard has emerged as a formidable competitor to OpenAI's ChatGPT in the field of conversational AI. Notably, Bard has recently been updated to handle visual inputs alongsid…
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…
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
Muhammad Maaz, Hanoona Rasheed, Salman Khan +1
Conversation agents fueled by Large Language Models (LLMs) are providing a new way to interact with visual data. While there have been initial attempts for image-based conversation…
Modulate Your Spectrum in Self-Supervised Learning
Xi Weng, Yunhao Ni, Tengwei Song +5
Whitening loss offers a theoretical guarantee against feature collapse in self-supervised learning (SSL) with joint embedding architectures. Typically, it involves a hard whitening…
Remote Sensing Change Detection With Transformers Trained from Scratch
Mubashir Noman, Mustansar Fiaz, Hisham Cholakkal +4
Current transformer-based change detection (CD) approaches either employ a pre-trained model trained on large-scale image classification ImageNet dataset or rely on first pre-train…
Gated Multi-Resolution Transfer Network for Burst Restoration and Enhancement
Nancy Mehta, Akshay Dudhane, Subrahmanyam Murala +3
Burst image processing is becoming increasingly popular in recent years. However, it is a challenging task since individual burst images undergo multiple degradations and often hav…
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…
Bridging Precision and Confidence: A Train-Time Loss for Calibrating Object Detection
Muhammad Akhtar Munir, Muhammad Haris Khan, Salman Khan +1
Deep neural networks (DNNs) have enabled astounding progress in several vision-based problems. Despite showing high predictive accuracy, recently, several works have revealed that…
SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications
Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed +3
Self-attention has become a defacto choice for capturing global context in various vision applications. However, its quadratic computational complexity with respect to image resolu…
Guidance Through Surrogate: Towards a Generic Diagnostic Attack
Muzammal Naseer, Salman Khan, Fatih Porikli +1
Adversarial training is an effective approach to make deep neural networks robust against adversarial attacks. Recently, different adversarial training defenses are proposed that n…
UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation
Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed +3
Owing to the success of transformer models, recent works study their applicability in 3D medical segmentation tasks. Within the transformer models, the self-attention mechanism is…
Fine-tuned CLIP Models are Efficient Video Learners
Hanoona Rasheed, Muhammad Uzair Khattak, Muhammad Maaz +2
Large-scale multi-modal training with image-text pairs imparts strong generalization to CLIP model. Since training on a similar scale for videos is infeasible, recent approaches fo…
Person Image Synthesis via Denoising Diffusion Model
Ankan Kumar Bhunia, Salman Khan, Hisham Cholakkal +4
The pose-guided person image generation task requires synthesizing photorealistic images of humans in arbitrary poses. The existing approaches use generative adversarial networks t…
An Investigation into Whitening Loss for Self-supervised Learning
Xi Weng, Lei Huang, Lei Zhao +3
A desirable objective in self-supervised learning (SSL) is to avoid feature collapse. Whitening loss guarantees collapse avoidance by minimizing the distance between embeddings of…
MaPLe: Multi-modal Prompt Learning
Muhammad Uzair Khattak, Hanoona Rasheed, Muhammad Maaz +2
Pre-trained vision-language (V-L) models such as CLIP have shown excellent generalization ability to downstream tasks. However, they are sensitive to the choice of input text promp…
Transformers in Remote Sensing: A Survey
Abdulaziz Amer Aleissaee, Amandeep Kumar, Rao Muhammad Anwer +4
Deep learning-based algorithms have seen a massive popularity in different areas of remote sensing image analysis over the past decade. Recently, transformers-based architectures,…
AVisT: A Benchmark for Visual Object Tracking in Adverse Visibility
Mubashir Noman, Wafa Al Ghallabi, Daniya Najiha +7
One of the key factors behind the recent success in visual tracking is the availability of dedicated benchmarks. While being greatly benefiting to the tracking research, existing b…
3D Vision with Transformers: A Survey
Jean Lahoud, Jiale Cao, Fahad Shahbaz Khan +4
The success of the transformer architecture in natural language processing has recently triggered attention in the computer vision field. The transformer has been used as a replace…
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…