Search Results for author:
Found 35 papers, 15 papers with code
Feature Representation Matters: End-to-End Learning for Reference-based Image Super-resolution
To this end, we engaged neural texture transfer to swap texture features between the low-resolution image and the high-resolution reference image.
APC: Adaptive Patch Contrast for Weakly Supervised Semantic Segmentation
Weakly Supervised Semantic Segmentation (WSSS) using only image-level labels has gained significant attention due to its cost-effectiveness.
Contrastive Learning
Weakly supervised Semantic Segmentation
+1
Frozen CLIP: A Strong Backbone for Weakly Supervised Semantic Segmentation
Specifically, the frozen CLIP model is applied as the backbone for semantic feature extraction, and a new decoder is designed to interpret extracted semantic features for final prediction.
Towards the Uncharted: Density-Descending Feature Perturbation for Semi-supervised Semantic Segmentation
Inspired by the low-density separation assumption in semi-supervised learning, our key insight is that feature density can shed a light on the most promising direction for the segmentation classifier to explore, which is the regions with lower density.
Continual Segmentation with Disentangled Objectness Learning and Class Recognition
Based on these findings, we propose CoMasTRe by disentangling continual segmentation into two stages: forgetting-resistant continual objectness learning and well-researched continual classification.
SFC: Shared Feature Calibration in Weakly Supervised Semantic Segmentation
Specifically, we leverage the class prototypes that carry positive shared features and propose a Multi-Scaled Distribution-Weighted (MSDW) consistency loss for narrowing the gap between the CAMs generated through classifier weights and class prototypes during training.
PSDPM: Prototype-based Secondary Discriminative Pixels Mining for Weakly Supervised Semantic Segmentation
Specifically we introduce a Foreground Pixel Estimation Module (FPEM) for estimating potential foreground pixels based on the correlations between primary and secondary discriminative pixels and the semantic segmentation results of baseline methods.
Top-K Pooling with Patch Contrastive Learning for Weakly-Supervised Semantic Segmentation
In this paper, we introduce a novel ViT-based WSSS method named top-K pooling with patch contrastive learning (TKP-PCL), which employs a top-K pooling layer to alleviate the limitations of previous max pooling selection.
Contrastive Learning
Weakly supervised Semantic Segmentation
+1
GPT-Prompt Controlled Diffusion for Weakly-Supervised Semantic Segmentation
In this process, the existing images and image-level labels provide the necessary control information, where GPT is employed to enrich the prompts, leading to the generation of diverse backgrounds.
Synchronize Feature Extracting and Matching: A Single Branch Framework for 3D Object Tracking
In this paper, we forsake the conventional Siamese paradigm and propose a novel single-branch framework, SyncTrack, synchronizing the feature extracting and matching to avoid forwarding encoder twice for template and search region as well as introducing extra parameters of matching network.
Trajectory Poisson multi-Bernoulli mixture filter for traffic monitoring using a drone
This paper proposes a multi-object tracking (MOT) algorithm for traffic monitoring using a drone equipped with optical and thermal cameras.
Weight-guided class complementing for long-tailed image recognition
In this paper, we propose a Weight-Guided Class Complementing framework to address this issue.
Ranked #11 on
Long-tail Learning
on CIFAR-10-LT (ρ=100)
FastRecon: Few-shot Industrial Anomaly Detection via Fast Feature Reconstruction
In this paper, we propose a few-shot anomaly detection strategy that works in a low-data regime and can generalize across products at no cost.
Hunting Sparsity: Density-Guided Contrastive Learning for Semi-Supervised Semantic Segmentation
Inspired by density-based unsupervised clustering, we propose to leverage feature density to locate sparse regions within feature clusters defined by label and pseudo labels.
Fully and Weakly Supervised Referring Expression Segmentation with End-to-End Learning
To validate our framework on a weakly-supervised setting, we annotated three RES benchmark datasets (RefCOCO, RefCOCO+ and RefCOCOg) with click annotations. Our method is simple but surprisingly effective, outperforming all previous state-of-the-art RES methods on fully- and weakly-supervised settings by a large margin.
Democracy Does Matter: Comprehensive Feature Mining for Co-Salient Object Detection
To achieve this, we design a democratic prototype generation module to generate democratic response maps, covering sufficient co-salient regions and thereby involving more shared attributes of co-salient objects.
Ranked #3 on
Co-Salient Object Detection
on CoCA
Towards Simple and Accurate Human Pose Estimation with Stair Network
In this paper, we focus on tackling the precise keypoint coordinates regression task.
Dynamic Feature Regularized Loss for Weakly Supervised Semantic Segmentation
In this paper, we propose a new regularized loss which utilizes both shallow and deep features that are dynamically updated in order to aggregate sufficient information to represent the relationship of different pixels.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Fast Pixel-Matching for Video Object Segmentation
Most mask-propagation based models are faster but with relatively low performance due to failure to adapt to object appearance variation.
Affinity Attention Graph Neural Network for Weakly Supervised Semantic Segmentation
More importantly, our approach can be readily applied to bounding box supervised instance segmentation task or other weakly supervised semantic segmentation tasks, with state-of-the-art or comparable performance among almot all weakly supervised tasks on PASCAL VOC or COCO dataset.
Box-supervised Instance Segmentation
Graph Neural Network
+4
Discriminative Triad Matching and Reconstruction for Weakly Referring Expression Grounding
In this paper, we are tackling the weakly-supervised referring expression grounding task, for the localization of a referent object in an image according to a query sentence, where the mapping between image regions and queries are not available during the training stage.
Self-Guided and Cross-Guided Learning for Few-Shot Segmentation
Specifically, through making an initial prediction for the annotated support image, the covered and uncovered foreground regions are encoded to the primary and auxiliary support vectors using masked GAP, respectively.
Ranked #65 on
Few-Shot Semantic Segmentation
on COCO-20i (1-shot)
Iterative Shrinking for Referring Expression Grounding Using Deep Reinforcement Learning
In this paper, we are tackling the proposal-free referring expression grounding task, aiming at localizing the target object according to a query sentence, without relying on off-the-shelf object proposals.
Structure-Consistent Weakly Supervised Salient Object Detection with Local Saliency Coherence
Since scribble labels fail to offer detailed salient regions, we propose a local coherence loss to propagate the labels to unlabeled regions based on image features and pixel distance, so as to predict integral salient regions with complete object structures.
Robust Generative Adversarial Network
Generative adversarial networks (GANs) are powerful generative models, but usually suffer from instability and generalization problem which may lead to poor generations.
Fast Template Matching and Update for Video Object Tracking and Segmentation
Specifically, the reinforcement learning agent learns to decide whether to update the target template according to the quality of the predicted result.
Reliability Does Matter: An End-to-End Weakly Supervised Semantic Segmentation Approach
Such reliable regions are then directly served as ground-truth labels for the parallel segmentation branch, where a newly designed dense energy loss function is adopted for optimization.
Ranked #22 on
Semantic Segmentation
on PASCAL VOC 2012 val
Progressive Sample Mining and Representation Learning for One-Shot Person Re-identification with Adversarial Samples
To tackle this problem, we propose to iteratively guess pseudo labels for the unlabeled image samples, which are later used to update the re-identification model together with the labelled samples.
Adaptive ROI Generation for Video Object Segmentation Using Reinforcement Learning
In this paper, we aim to tackle the task of semi-supervised video object segmentation across a sequence of frames where only the ground-truth segmentation of the first frame is provided.
Segmentation Mask Guided End-to-End Person Search
Person search aims to search for a target person among multiple images recorded by multiple surveillance cameras, which faces various challenges from both pedestrian detection and person re-identification.
Single Image based Head Pose Estimation with Spherical Parameterization and 3D Morphing
Specifically, the rectangular coordinates of only four non-coplanar feature points from a predefined 3D facial model as well as the corresponding ones automatically/ manually extracted from a 2D face image are first normalized to exclude the effect of external factors (i. e., scale factor and translation parameters).
Generative Adversarial Classifier for Handwriting Characters Super-Resolution
Generative Adversarial Networks (GAN) receive great attentions recently due to its excellent performance in image generation, transformation, and super-resolution.
Correlation Filter Selection for Visual Tracking Using Reinforcement Learning
In this paper, we propose a novel approach to address the correlation filter update problem.
Image Ordinal Classification and Understanding: Grid Dropout with Masking Label
Finally we demonstrate the effectiveness of both approaches by visualizing the Class Activation Map (CAM) and discover that grid dropout is more aware of the whole facial areas and more robust than neuron dropout for small training dataset.
IAN: The Individual Aggregation Network for Person Search
In addition, to relieve the negative effect caused by varying visual appearances of the same individual, IAN introduces a novel center loss that can increase the intra-class compactness of feature representations.
