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Found 16 papers, 12 papers with code
FaceXFormer: A Unified Transformer for Facial Analysis
Unlike these conventional methods, our FaceXformer leverages a transformer-based encoder-decoder architecture where each task is treated as a learnable token, enabling the integration of multiple tasks within a single framework.
PosSAM: Panoptic Open-vocabulary Segment Anything
In this paper, we introduce an open-vocabulary panoptic segmentation model that effectively unifies the strengths of the Segment Anything Model (SAM) with the vision-language CLIP model in an end-to-end framework.
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Open Vocabulary Panoptic Segmentation
Open Vocabulary Semantic Segmentation
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Entropic Open-set Active Learning
Active Learning (AL) aims to enhance the performance of deep models by selecting the most informative samples for annotation from a pool of unlabeled data.
Mask-free OVIS: Open-Vocabulary Instance Segmentation without Manual Mask Annotations
In summary, an OV method learns task-specific information using strong supervision from base annotations and novel category information using weak supervision from image-captions pairs.
Open-Set Automatic Target Recognition
Automatic Target Recognition (ATR) is a category of computer vision algorithms which attempts to recognize targets on data obtained from different sensors.
Towards Online Domain Adaptive Object Detection
To the best of our knowledge, this is the first work to address online and offline adaptation settings for object detection.
Instance Relation Graph Guided Source-Free Domain Adaptive Object Detection
The Source-Free Domain Adaptation (SFDA) setting aims to alleviate these concerns by adapting a source-trained model for the target domain without requiring access to the source data.
Target and Task specific Source-Free Domain Adaptive Image Segmentation
In task-specific adaptation, we exploit the enhanced pseudo-labels using a student-teacher framework to effectively learn segmentation on the target domain.
On-the-Fly Test-time Adaptation for Medical Image Segmentation
During test-time, the model takes in just the new test image and generates a domain code to adapt the features of source model according to the test data.
ST-MTL: Spatio-Temporal Multitask Learning Model to Predict Scanpath While Tracking Instruments in Robotic Surgery
We generate the task-aware saliency maps and scanpath of the instruments on the dataset of the MICCAI 2017 robotic instrument segmentation challenge.
Meta-UDA: Unsupervised Domain Adaptive Thermal Object Detection using Meta-Learning
Though thermal cameras are widely used for military applications and increasingly for commercial applications, there is a lack of robust algorithms to robustly exploit the thermal imagery due to the limited availability of labeled thermal data.
Image Fusion Transformer
Furthermore, we show the effectiveness of the proposed ST fusion strategy with an ablation analysis.
Unsupervised Domain Adaptation of Object Detectors: A Survey
Recent advances in deep learning have led to the development of accurate and efficient models for various computer vision applications such as classification, segmentation, and detection.
Brain Tumor Segmentation and Survival Prediction using 3D Attention UNet
For survival prediction, we extract some novel radiomic features based on geometry, location, the shape of the segmented tumor and combine them with clinical information to estimate the survival duration for each patient.
MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection
Existing approaches for unsupervised domain adaptive object detection perform feature alignment via adversarial training.
AP-MTL: Attention Pruned Multi-task Learning Model for Real-time Instrument Detection and Segmentation in Robot-assisted Surgery
Training a real-time robotic system for the detection and segmentation of high-resolution images provides a challenging problem with the limited computational resource.
