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20162026
most citedTransformers in Vision: A Survey

3.6k citations · 10.7k across the 127 of their papers we have counts for

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133 papers · 1 filter

cs.CV2026★ 3 cited

HorizonNet for visual terrain navigation

Bertil Grelsson, Andreas Robinson, Michael Felsberg +1

This paper investigates the problem of position estimation of unmanned surface vessels (USVs) operating in coastal areas or in the archipelago. We propose a position estimation met…

cs.CV2026

Gaussian-JEPA: Joint-Embedding Predictive Learning for 3D Gaussian Splats

Bin Ren, Qi Ma, Yue Li +7

3D Gaussian Splatting (3DGS) represents 3D content with anisotropic primitives that jointly encode geometry and appearance. Fixed-budget encoders consume sampled observations of Ga…

cs.CV2023★ 5 cited

Improving Underwater Visual Tracking With a Large Scale Dataset and Image Enhancement

Basit Alawode, Fayaz Ali Dharejo, Mehnaz Ummar +6

This paper presents a new dataset and general tracker enhancement method for Underwater Visual Object Tracking (UVOT). Despite its significance, underwater tracking has remained un…

cs.CV2023★ 18 cited

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…

cs.CV2023★ 4 cited

Self-regulating Prompts: Foundational Model Adaptation without Forgetting

Muhammad Uzair Khattak, Syed Talal Wasim, Muzammal Naseer +3

Prompt learning has emerged as an efficient alternative for fine-tuning foundational models, such as CLIP, for various downstream tasks. Conventionally trained using the task-speci…

cs.CV2023★ 1 cited

Towards Realistic Zero-Shot Classification via Self Structural Semantic Alignment

Sheng Zhang, Muzammal Naseer, Guangyi Chen +4

Large-scale pre-trained Vision Language Models (VLMs) have proven effective for zero-shot classification. Despite the success, most traditional VLMs-based methods are restricted by…

cs.CV2023★ 68 cited

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…

cs.CV2023★ 2 cited

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…

cs.CV2023★ 35 cited

PromptIR: Prompting for All-in-One Blind Image Restoration

Vaishnav Potlapalli, Syed Waqas Zamir, Salman Khan +1

Image restoration involves recovering a high-quality clean image from its degraded version. Deep learning-based methods have significantly improved image restoration performance, h…

cs.CV2023★ 7 cited

DFormer: Diffusion-guided Transformer for Universal Image Segmentation

Hefeng Wang, Jiale Cao, Rao Muhammad Anwer +3

This paper introduces an approach, named DFormer, for universal image segmentation. The proposed DFormer views universal image segmentation task as a denoising process using a diff…

cs.CV2023★ 1 cited

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…

cs.CV2023★ 16 cited

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…

cs.CV2023★ 22 cited

Salient Mask-Guided Vision Transformer for Fine-Grained Classification

Dmitry Demidov, Muhammad Hamza Sharif, Aliakbar Abdurahimov +2

Fine-grained visual classification (FGVC) is a challenging computer vision problem, where the task is to automatically recognise objects from subordinate categories. One of its mai…

cs.CV2023

Discriminative Co-Saliency and Background Mining Transformer for Co-Salient Object Detection

Long Li, Junwei Han, Ni Zhang +5

Most previous co-salient object detection works mainly focus on extracting co-salient cues via mining the consistency relations across images while ignoring explicit exploration of…

cs.CV2023

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…

cs.CV2023★ 2 cited

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…

cs.CV2023★ 2 cited

Boosting Adversarial Transferability using Dynamic Cues

Muzammal Naseer, Ahmad Mahmood, Salman Khan +1

The transferability of adversarial perturbations between image models has been extensively studied. In this case, an attack is generated from a known surrogate \eg, the ImageNet tr…

cs.CV2023

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…

cs.CV2023★ 2 cited

Burstormer: Burst Image Restoration and Enhancement Transformer

Akshay Dudhane, Syed Waqas Zamir, Salman Khan +2

On a shutter press, modern handheld cameras capture multiple images in rapid succession and merge them to generate a single image. However, individual frames in a burst are misalig…

cs.CV2023

Generative Multiplane Neural Radiance for 3D-Aware Image Generation

Amandeep Kumar, Ankan Kumar Bhunia, Sanath Narayan +5

We present a method to efficiently generate 3D-aware high-resolution images that are view-consistent across multiple target views. The proposed multiplane neural radiance model, na…

cs.CV2023

3D-Aware Multi-Class Image-to-Image Translation with NeRFs

Senmao Li, Joost van de Weijer, Yaxing Wang +3

Recent advances in 3D-aware generative models (3D-aware GANs) combined with Neural Radiance Fields (NeRF) have achieved impressive results. However no prior works investigate 3D-aw…

cs.CV2023

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…

cs.CV2023★ 12 cited

StyleDiffusion: Prompt-Embedding Inversion for Text-Based Editing

Senmao Li, Joost van de Weijer, Taihang Hu +5

A significant research effort is focused on exploiting the amazing capacities of pretrained diffusion models for the editing of images.They either finetune the model, or invert the…

cs.CV2023★ 9 cited

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…