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Found 27 papers, 16 papers with code
VDOR: A Video-based Dataset for Object Removal via Sequence Consistency
Object removal, as a sub-task of image inpainting, has garnered significant attention in recent years.
PGP-SAM: Prototype-Guided Prompt Learning for Efficient Few-Shot Medical Image Segmentation
The Segment Anything Model (SAM) has demonstrated strong and versatile segmentation capabilities, along with intuitive prompt-based interactions.
From Simple to Professional: A Combinatorial Controllable Image Captioning Agent
The Controllable Image Captioning Agent (CapAgent) is an innovative system designed to bridge the gap between user simplicity and professional-level outputs in image captioning tasks.
Detailed Object Description with Controllable Dimensions
Dimension Tailor can not only improve the quality of object details but also offer flexibility in including or excluding specific dimensions based on user preferences.
Polyp-E: Benchmarking the Robustness of Deep Segmentation Models via Polyp Editing
To benchmark the model robustness, we focus on evaluating the robustness of segmentation models on the polyps with various attributes (e. g. location and size) and healthy samples.
Evaluating Attribute Comprehension in Large Vision-Language Models
Currently, large vision-language models have gained promising progress on many downstream tasks.
Efficient Face Super-Resolution via Wavelet-based Feature Enhancement Network
Previous methods typically employ an encoder-decoder structure to extract facial structural features, where the direct downsampling inevitably introduces distortions, especially to high-frequency features such as edges.
A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge
We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge.
Benchmarking Segmentation Models with Mask-Preserved Attribute Editing
We evaluate a broad variety of semantic segmentation models, spanning from conventional close-set models to recent open-vocabulary large models on their robustness to different types of variations.
Vision-language Assisted Attribute Learning
Attribute labeling at large scale is typically incomplete and partial, posing significant challenges to model optimization.
Ariadne's Thread:Using Text Prompts to Improve Segmentation of Infected Areas from Chest X-ray images
Segmentation of the infected areas of the lung is essential for quantifying the severity of lung disease like pulmonary infections.
Super-Resolution Information Enhancement For Crowd Counting
As the proposed method requires SR labels, we further propose a Super-Resolution Crowd Counting dataset (SR-Crowd).
Graph Convolution Based Cross-Network Multi-Scale Feature Fusion for Deep Vessel Segmentation
To tackle the challenges caused by the sparsity and anisotropy of vessels, a higher percentage of graph nodes are distributed in areas that potentially contain vessels while a higher percentage of edges follow the orientation of potential nearbyvessels.
On-the-Fly Category Discovery
Our code is available at https://github. com/PRIS-CV/On-the-fly-Category-Discovery.
Multi-head Uncertainty Inference for Adversarial Attack Detection
We adopt a multi-head architecture with multiple prediction heads (i. e., classifiers) to obtain predictions from different depths in the DNNs and introduce shallow information for the UI.
Learning Invariant Visual Representations for Compositional Zero-Shot Learning
Compositional Zero-Shot Learning (CZSL) aims to recognize novel compositions using knowledge learned from seen attribute-object compositions in the training set.
Domain Generalization via Frequency-domain-based Feature Disentanglement and Interaction
Adaptation to out-of-distribution data is a meta-challenge for all statistical learning algorithms that strongly rely on the i. i. d.
Symmetry-Enhanced Attention Network for Acute Ischemic Infarct Segmentation with Non-Contrast CT Images
In this paper, we propose a symmetry enhanced attention network (SEAN) for acute ischemic infarct segmentation.
Cross-layer Navigation Convolutional Neural Network for Fine-grained Visual Classification
Then, attention mechanisms are used after feature fusion to extract spatial and channel information while linking the high-level semantic information and the low-level texture features, which can better locate the discriminative regions for the FGVC.
DF^2AM: Dual-level Feature Fusion and Affinity Modeling for RGB-Infrared Cross-modality Person Re-identification
Meanwhile, to further mining the relationships between global features from person images, we propose an Affinities Modeling (AM) module to obtain the optimal intra- and inter-modality image matching.
Cross-Modality Person Re-identification
Person Re-Identification
Fine-Grained Visual Classification via Simultaneously Learning of Multi-regional Multi-grained Features
Finally, we can obtain multiple discriminative regions on high-level feature channels and obtain multiple more minute regions within these discriminative regions on middle-level feature channels.
Context-Aware Refinement Network Incorporating Structural Connectivity Prior for Brain Midline Delineation
Nevertheless, there are still great challenges with brain midline delineation, such as the largely deformed midline caused by the mass effect and the possible morphological failure that the predicted midline is not a connected curve.
Segmentation-based Method combined with Dynamic Programming for Brain Midline Delineation
The midline related pathological image features are crucial for evaluating the severity of brain compression caused by stroke or traumatic brain injury (TBI).
FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation
Modern approaches for semantic segmentation usually employ dilated convolutions in the backbone to extract high-resolution feature maps, which brings heavy computation complexity and memory footprint.
Ranked #42 on
Semantic Segmentation
on PASCAL Context
Visual relationship detection with deep structural ranking
In this paper, we propose a novel framework, called Deep Structural Ranking, for visual relationship detection.
A Unified Multiplicative Framework for Attribute Learning
Attributes are mid-level semantic properties of objects.
