research
∙
09/19/2023
Unsupervised Landmark Discovery Using Consistency Guided Bottleneck
We study a challenging problem of unsupervised discovery of object landm...
share
research
∙
08/11/2023
Towards Instance-adaptive Inference for Federated Learning
Federated learning (FL) is a distributed learning paradigm that enables ...
research
∙
08/11/2023
Diverse Data Augmentation with Diffusions for Effective Test-time Prompt Tuning
Benefiting from prompt tuning, recent years have witnessed the promising...
research
∙
07/27/2023
How Good is Google Bard's Visual Understanding? An Empirical Study on Open Challenges
Google's Bard has emerged as a formidable competitor to OpenAI's ChatGPT...
research
∙
07/14/2023
Frequency Domain Adversarial Training for Robust Volumetric Medical Segmentation
It is imperative to ensure the robustness of deep learning models in cri...
research
∙
07/13/2023
Video-FocalNets: Spatio-Temporal Focal Modulation for Video Action Recognition
Recent video recognition models utilize Transformer models for long-rang...
research
∙
06/22/2023
PromptIR: Prompting for All-in-One Blind Image Restoration
Image restoration involves recovering a high-quality clean image from it...
research
∙
06/15/2023
Learnable Weight Initialization for Volumetric Medical Image Segmentation
Hybrid volumetric medical image segmentation models, combining the advan...
research
∙
06/13/2023
XrayGPT: Chest Radiographs Summarization using Medical Vision-Language Models
The latest breakthroughs in large vision-language models, such as Bard a...
research
∙
06/08/2023
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
Conversation agents fueled by Large Language Models (LLMs) are providing...
research
∙
05/26/2023
Modulate Your Spectrum in Self-Supervised Learning
Whitening loss provides theoretical guarantee in avoiding feature collap...
research
∙
04/30/2023
Discriminative Co-Saliency and Background Mining Transformer for Co-Salient Object Detection
Most previous co-salient object detection works mainly focus on extracti...
research
∙
04/13/2023
Remote Sensing Change Detection With Transformers Trained from Scratch
Current transformer-based change detection (CD) approaches either employ...
research
∙
04/13/2023
Gated Multi-Resolution Transfer Network for Burst Restoration and Enhancement
Burst image processing is becoming increasingly popular in recent years....
research
∙
04/06/2023
Vita-CLIP: Video and text adaptive CLIP via Multimodal Prompting
Adopting contrastive image-text pretrained models like CLIP towards vide...
research
∙
04/03/2023
Video Instance Segmentation in an Open-World
Existing video instance segmentation (VIS) approaches generally follow a...
research
∙
03/25/2023
Bridging Precision and Confidence: A Train-Time Loss for Calibrating Object Detection
Deep neural networks (DNNs) have enabled astounding progress in several ...
research
∙
02/23/2023
Boosting Adversarial Transferability using Dynamic Cues
The transferability of adversarial perturbations between image models ha...
research
∙
12/30/2022
Guidance Through Surrogate: Towards a Generic Diagnostic Attack
Adversarial training is an effective approach to make deep neural networ...
research
∙
12/11/2022
PromptCAL: Contrastive Affinity Learning via Auxiliary Prompts for Generalized Novel Category Discovery
Although existing semi-supervised learning models achieve remarkable suc...
research
∙
12/06/2022
Fine-tuned CLIP Models are Efficient Video Learners
Large-scale multi-modal training with image-text pairs imparts strong ge...
research
∙
11/22/2022
Person Image Synthesis via Denoising Diffusion Model
The pose-guided person image generation task requires synthesizing photo...
research
∙
10/07/2022
An Investigation into Whitening Loss for Self-supervised Learning
A desirable objective in self-supervised learning (SSL) is to avoid feat...
research
∙
10/06/2022
MaPLe: Multi-modal Prompt Learning
Pre-trained vision-language (V-L) models such as CLIP have shown excelle...
research
∙
10/06/2022
CLIP model is an Efficient Continual Learner
The continual learning setting aims to learn new tasks over time without...
research
∙
10/04/2022
ROAD-R: The Autonomous Driving Dataset with Logical Requirements
Neural networks have proven to be very powerful at computer vision tasks...
research
∙
09/12/2022
Identification of Cognitive Workload during Surgical Tasks with Multimodal Deep Learning
The operating room (OR) is a dynamic and complex environment consisting ...
research
∙
09/12/2022
Situation Awareness for Automated Surgical Check-listing in AI-Assisted Operating Room
Nowadays, there are more surgical procedures that are being performed us...
research
∙
09/02/2022
Transformers in Remote Sensing: A Survey
Deep learning-based algorithms have seen a massive popularity in differe...
research
∙
08/14/2022
AVisT: A Benchmark for Visual Object Tracking in Adverse Visibility
One of the key factors behind the recent success in visual tracking is t...
research
∙
07/25/2022
Self-Distilled Vision Transformer for Domain Generalization
In recent past, several domain generalization (DG) methods have been pro...
research
∙
07/18/2022
Adversarial Pixel Restoration as a Pretext Task for Transferable Perturbations
Transferable adversarial attacks optimize adversaries from a pretrained ...
research
∙
07/07/2022
Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection
Existing open-vocabulary object detectors typically enlarge their vocabu...
research
∙
07/05/2022
OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning
Semi-supervised learning (SSL) is one of the dominant approaches to addr...
research
∙
06/21/2022
EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications
In the pursuit of achieving ever-increasing accuracy, large and complex ...
research
∙
04/22/2022
Self-Supervised Video Object Segmentation via Cutout Prediction and Tagging
We propose a novel self-supervised Video Object Segmentation (VOS) appro...
research
∙
03/28/2022
Energy-based Latent Aligner for Incremental Learning
Deep learning models tend to forget their earlier knowledge while increm...
research
∙
03/24/2022
Video Instance Segmentation via Multi-scale Spatio-temporal Split Attention Transformer
State-of-the-art transformer-based video instance segmentation (VIS) app...
research
∙
01/24/2022
Transformers in Medical Imaging: A Survey
Following unprecedented success on the natural language tasks, Transform...
research
∙
01/10/2022
Vision in adverse weather: Augmentation using CycleGANs with various object detectors for robust perception in autonomous racing
In an autonomous driving system, perception - identification of features...
research
∙
01/07/2022
A Novel Incremental Learning Driven Instance Segmentation Framework to Recognize Highly Cluttered Instances of the Contraband Items
Screening cluttered and occluded contraband items from baggage X-ray sca...
research
∙
12/09/2021
Spatio-temporal Relation Modeling for Few-shot Action Recognition
We propose a novel few-shot action recognition framework, STRM, which en...
research
∙
12/06/2021
DoodleFormer: Creative Sketch Drawing with Transformers
Creative sketching or doodling is an expressive activity, where imaginat...
research
∙
12/02/2021
Self-supervised Video Transformer
In this paper, we propose self-supervised training for video transformer...
research
∙
12/02/2021
OW-DETR: Open-world Detection Transformer
Open-world object detection (OWOD) is a challenging computer vision prob...
research
∙
10/27/2021
International Workshop on Continual Semi-Supervised Learning: Introduction, Benchmarks and Baselines
The aim of this paper is to formalize a new continual semi-supervised le...
research
∙
08/22/2021
Tensor Pooling Driven Instance Segmentation Framework for Baggage Threat Recognition
Automated systems designed for screening contraband items from the X-ray...
research
∙
08/20/2021
Discriminative Region-based Multi-Label Zero-Shot Learning
Multi-label zero-shot learning (ZSL) is a more realistic counter-part of...
research
∙
07/23/2021
Learning Discriminative Representations for Multi-Label Image Recognition
Multi-label recognition is a fundamental, and yet is a challenging task ...
research
∙
07/15/2021
