Search Results for author:
Found 105 papers, 53 papers with code
Content-Consistent Matching for Domain Adaptive Semantic Segmentation
The target of CCM is to acquire those synthetic images that share similar distribution with the real ones in the target domain, so that the domain gap can be naturally alleviated by employing the content-consistent synthetic images for training.
Ranked #11 on
Semantic Segmentation
on GTAV-to-Cityscapes Labels
Unified Frequency-Assisted Transformer Framework for Detecting and Grounding Multi-Modal Manipulation
Moreover, to address the semantic conflicts between image and frequency domains, the forgery-aware mutual module is developed to further enable the effective interaction of disparate image and frequency features, resulting in aligned and comprehensive visual forgery representations.
StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data
The remarkable multimodal capabilities demonstrated by OpenAI's GPT-4 have sparked significant interest in the development of multimodal Large Language Models (LLMs).
CTP: Towards Vision-Language Continual Pretraining via Compatible Momentum Contrast and Topology Preservation
Regarding the growing nature of real-world data, such an offline training paradigm on ever-expanding data is unsustainable, because models lack the continual learning ability to accumulate knowledge constantly.
CLE Diffusion: Controllable Light Enhancement Diffusion Model
Low light enhancement has gained increasing importance with the rapid development of visual creation and editing.
Disentangled Pre-training for Image Matting
The pre-training task is designed in a similar manner as image matting, where random trimap and alpha matte are generated to achieve an image disentanglement objective.
Progressive Semantic-Visual Mutual Adaption for Generalized Zero-Shot Learning
Generalized Zero-Shot Learning (GZSL) identifies unseen categories by knowledge transferred from the seen domain, relying on the intrinsic interactions between visual and semantic information.
Global Knowledge Calibration for Fast Open-Vocabulary Segmentation
Recent advancements in pre-trained vision-language models, such as CLIP, have enabled the segmentation of arbitrary concepts solely from textual inputs, a process commonly referred to as open-vocabulary semantic segmentation (OVS).
Knowledge Distillation
Open Vocabulary Semantic Segmentation
+3
SLCA: Slow Learner with Classifier Alignment for Continual Learning on a Pre-trained Model
The goal of continual learning is to improve the performance of recognition models in learning sequentially arrived data.
Adversarially Masking Synthetic To Mimic Real: Adaptive Noise Injection for Point Cloud Segmentation Adaptation
With the help of adversarial training, the masking module can learn to generate source masks to mimic the pattern of irregular target noise, thereby narrowing the domain gap.
Learning To Segment Every Referring Object Point by Point
Extensive experiments show that our model achieves 52. 06% in terms of accuracy (versus 58. 93% in fully supervised setting) on RefCOCO+@testA, when only using 1% of the mask annotations.
Learning on Gradients: Generalized Artifacts Representation for GAN-Generated Images Detection
The key of fake image detection is to develop a generalized representation to describe the artifacts produced by generation models.
Mask Matching Transformer for Few-Shot Segmentation
Typical methods follow the paradigm to firstly learn prototypical features from support images and then match query features in pixel-level to obtain segmentation results.
Mining Unseen Classes via Regional Objectness: A Simple Baseline for Incremental Segmentation
Our MicroSeg is based on the assumption that background regions with strong objectness possibly belong to those concepts in the historical or future stages.
Class-Incremental Semantic Segmentation
Continual Learning
+1
VMFormer: End-to-End Video Matting with Transformer
In this paper, we propose VMFormer: a transformer-based end-to-end method for video matting.
Instance As Identity: A Generic Online Paradigm for Video Instance Segmentation
In this work, we propose a new online VIS paradigm named Instance As Identity (IAI), which models temporal information for both detection and tracking in an efficient way.
SiRi: A Simple Selective Retraining Mechanism for Transformer-based Visual Grounding
Particularly, SiRi conveys a significant principle to the research of visual grounding, i. e., a better initialized vision-language encoder would help the model converge to a better local minimum, advancing the performance accordingly.
Entity-Graph Enhanced Cross-Modal Pretraining for Instance-level Product Retrieval
Our goal in this research is to study a more realistic environment in which we can conduct weakly-supervised multi-modal instance-level product retrieval for fine-grained product categories.
FisheyeEX: Polar Outpainting for Extending the FoV of Fisheye Lens
For the distortion synthesis, we propose a spiral distortion-aware perception module, in which the learning path keeps consistent with the distortion prior of the fisheye image.
Cylin-Painting: Seamless 360° Panoramic Image Outpainting and Beyond with Cylinder-Style Convolutions
Motivated by this analysis, we present a Cylin-Painting framework that involves meaningful collaborations between inpainting and outpainting and efficiently fuses the different arrangements, with a view to leveraging their complementary benefits on a consistent and seamless cylinder.
L2G: A Simple Local-to-Global Knowledge Transfer Framework for Weakly Supervised Semantic Segmentation
Our framework conducts the global network to learn the captured rich object detail knowledge from a global view and thereby produces high-quality attention maps that can be directly used as pseudo annotations for semantic segmentation networks.
Transfer Learning
Weakly supervised Semantic Segmentation
+1
Scalable Video Object Segmentation with Identification Mechanism
This paper delves into the challenges of achieving scalable and effective multi-object modeling for semi-supervised Video Object Segmentation (VOS).
Semantic Segmentation
Semi-Supervised Video Object Segmentation
+1
Large-Scale Video Panoptic Segmentation in the Wild: A Benchmark
In contrast, our large-scale VIdeo Panoptic Segmentation in the Wild (VIPSeg) dataset provides 3, 536 videos and 84, 750 frames with pixel-level panoptic annotations, covering a wide range of real-world scenarios and categories.
Clicking Matters:Towards Interactive Human Parsing
In this work, we focus on Interactive Human Parsing (IHP), which aims to segment a human image into multiple human body parts with guidance from users' interactions.
M5Product: Self-harmonized Contrastive Learning for E-commercial Multi-modal Pretraining
Despite the potential of multi-modal pre-training to learn highly discriminative feature representations from complementary data modalities, current progress is being slowed by the lack of large-scale modality-diverse datasets.
Understanding and Accelerating Neural Architecture Search with Training-Free and Theory-Grounded Metrics
This work targets designing a principled and unified training-free framework for Neural Architecture Search (NAS), with high performance, low cost, and in-depth interpretation.
Generating Superpixels for High-resolution Images with Decoupled Patch Calibration
Superpixel segmentation has recently seen important progress benefiting from the advances in differentiable deep learning.
Product1M: Towards Weakly Supervised Instance-Level Product Retrieval via Cross-modal Pretraining
In this paper, we investigate a more realistic setting that aims to perform weakly-supervised multi-modal instance-level product retrieval among fine-grained product categories.
LayerCAM: Exploring Hierarchical Class Activation Maps for Localization
To evaluate the quality of the class activation maps produced by LayerCAM, we apply them to weakly-supervised object localization and semantic segmentation.
ReGO: Reference-Guided Outpainting for Scenery Image
We aim to tackle the challenging yet practical scenery image outpainting task in this work.
Automated Deepfake Detection
In this paper, we propose to utilize Automated Machine Learning to adaptively search a neural architecture for deepfake detection.
VSPW: A Large-scale Dataset for Video Scene Parsing in the Wild
To the best of our knowledge, our VSPW is the first attempt to tackle the challenging video scene parsing task in the wild by considering diverse scenarios.
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.
Domain Consensus Clustering for Universal Domain Adaptation
To better exploit the intrinsic structure of the target domain, we propose Domain Consensus Clustering (DCC), which exploits the domain consensus knowledge to discover discriminative clusters on both common samples and private ones.
Ranked #3 on
Partial Domain Adaptation
on Office-31
Few-Shot Segmentation via Cycle-Consistent Transformer
Directly performing cross-attention may aggregate these features from support to query and bias the query features.
Ranked #51 on
Few-Shot Semantic Segmentation
on COCO-20i (5-shot)
Associating Objects with Transformers for Video Object Segmentation
The state-of-the-art methods learn to decode features with a single positive object and thus have to match and segment each target separately under multi-object scenarios, consuming multiple times computing resources.
One-shot visual object segmentation
Semantic Segmentation
+1
Rethinking Cross-modal Interaction from a Top-down Perspective for Referring Video Object Segmentation
Referring video object segmentation (RVOS) aims to segment video objects with the guidance of natural language reference.
One-shot visual object segmentation
Referring Video Object Segmentation
+2
Cross-Modal Progressive Comprehension for Referring Segmentation
In this paper, we propose a Cross-Modal Progressive Comprehension (CMPC) scheme to effectively mimic human behaviors and implement it as a CMPC-I (Image) module and a CMPC-V (Video) module to improve referring image and video segmentation models.
Ranked #7 on
Referring Expression Segmentation
on J-HMDB
Decoupled Spatial Temporal Graphs for Generic Visual Grounding
Visual grounding is a long-lasting problem in vision-language understanding due to its diversity and complexity.
AINet: Association Implantation for Superpixel Segmentation
However, simply applying a series of convolution operations with limited receptive fields can only implicitly perceive the relations between the pixel and its surrounding grids.
Towards Complete Scene and Regular Shape for Distortion Rectification by Curve-Aware Extrapolation
To our knowledge, we are the first to tackle the challenging rectification via outpainting, and our curve-aware strategy can reach a rectification construction with complete content and regular shape.
Consistent Structural Relation Learning for Zero-Shot Segmentation
Concretely, by exploring the pair-wise and list-wise structures, we impose the relations of generated visual features to be consistent with their counterparts in the semantic word embedding space.
Delving Deep into Label Smoothing
Experiments demonstrate that based on the same classification models, the proposed approach can effectively improve the classification performance on CIFAR-100, ImageNet, and fine-grained datasets.
Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation
The conventional solution to this task is to minimize the discrepancy between source and target to enable effective knowledge transfer.
Ranked #24 on
Synthetic-to-Real Translation
on SYNTHIA-to-Cityscapes
LID 2020: The Learning from Imperfect Data Challenge Results
The purpose of the Learning from Imperfect Data (LID) workshop is to inspire and facilitate the research in developing novel approaches that would harness the imperfect data and improve the data-efficiency during training.
Collaborative Video Object Segmentation by Multi-Scale Foreground-Background Integration
This paper investigates the principles of embedding learning to tackle the challenging semi-supervised video object segmentation.
One-shot visual object segmentation
Semantic Segmentation
+1
Referring Image Segmentation via Cross-Modal Progressive Comprehension
In addition to the CMPC module, we further leverage a simple yet effective TGFE module to integrate the reasoned multimodal features from different levels with the guidance of textual information.
Ranked #11 on
Referring Expression Segmentation
on RefCOCO testB
DONet: Dual Objective Networks for Skin Lesion Segmentation
Skin lesion segmentation is a crucial step in the computer-aided diagnosis of dermoscopic images.
Inter-Image Communication for Weakly Supervised Localization
We learn a feature center for each category and realize the global feature consistency by forcing the object features to approach class-specific centers.
Sketch-Guided Scenery Image Outpainting
In this work, we take the image outpainting one step forward by allowing users to harvest personal custom outpainting results using sketches as the guidance.
Rethinking Localization Map: Towards Accurate Object Perception with Self-Enhancement Maps
To fulfill the direct evaluation, we annotate pixel-level object masks on the ILSVRC validation set.
Focus Longer to See Better:Recursively Refined Attention for Fine-Grained Image Classification
In this paper, we tried to focus on these marginal differences to extract more representative features.
Omni-supervised Facial Expression Recognition via Distilled Data
However, the performance of the current state-of-the-art facial expression recognition (FER) approaches is directly related to the labeled data for training.
Facial Expression Recognition
Facial Expression Recognition (FER)
The 1st Agriculture-Vision Challenge: Methods and Results
The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation task associated with our challenge dataset.
Referring Image Segmentation by Generative Adversarial Learning
To this end, we propose to train the referring image segmentation model in a generative adversarial fashion, which well addresses the distribution similarity problem.
VehicleNet: Learning Robust Visual Representation for Vehicle Re-identification
This stage relaxes the full alignment between the training and testing domains, as it is agnostic to the target vehicle domain.
Ranked #1 on
Vehicle Re-Identification
on VehicleID
Alleviating Semantic-level Shift: A Semi-supervised Domain Adaptation Method for Semantic Segmentation
A key challenge of this task is how to alleviate the data distribution discrepancy between the source and target domains, i. e. reducing domain shift.
Memory Aggregation Networks for Efficient Interactive Video Object Segmentation
Interactive video object segmentation (iVOS) aims at efficiently harvesting high-quality segmentation masks of the target object in a video with user interactions.
Ranked #5 on
Interactive Video Object Segmentation
on DAVIS 2017
(AUC-J metric)
Interactive Video Object Segmentation
Semantic Segmentation
+1
Laplacian Denoising Autoencoder
This can be naturally generalized to span multiple scales with a Laplacian pyramid representation of the input data.
Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation
We consider the problem of unsupervised domain adaptation for semantic segmentation by easing the domain shift between the source domain (synthetic data) and the target domain (real data) in this work.
Ranked #8 on
Semantic Segmentation
on DensePASS
Collaborative Video Object Segmentation by Foreground-Background Integration
This paper investigates the principles of embedding learning to tackle the challenging semi-supervised video object segmentation.
One-shot visual object segmentation
Semantic Segmentation
+1
University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization
To our knowledge, University-1652 is the first drone-based geo-localization dataset and enables two new tasks, i. e., drone-view target localization and drone navigation.
Ranked #3 on
Image-Based Localization
on cvusa
AlignSeg: Feature-Aligned Segmentation Networks
Aggregating features in terms of different convolutional blocks or contextual embeddings has been proven to be an effective way to strengthen feature representations for semantic segmentation.
Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis
To encourage research in computer vision for agriculture, we present Agriculture-Vision: a large-scale aerial farmland image dataset for semantic segmentation of agricultural patterns.
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 #21 on
Semantic Segmentation
on PASCAL VOC 2012 val
Image Classification
Weakly supervised Semantic Segmentation
+1
Self-Correction for Human Parsing
To tackle the problem of learning with label noises, this work introduces a purification strategy, called Self-Correction for Human Parsing (SCHP), to progressively promote the reliability of the supervised labels as well as the learned models.
Ranked #2 on
Human Part Segmentation
on CIHP
SPGNet: Semantic Prediction Guidance for Scene Parsing
The multi-scale context module refers to the operations to aggregate feature responses from a large spatial extent, while the single-stage encoder-decoder structure encodes the high-level semantic information in the encoder path and recovers the boundary information in the decoder path.
CCNet: Criss-Cross Attention for Semantic Segmentation
Compared with the non-local block, the proposed recurrent criss-cross attention module requires 11x less GPU memory usage.
Ranked #7 on
Semantic Segmentation
on FoodSeg103
(using extra training data)
Self-similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-identification
Upon our SSG, we further introduce a clustering-guided semisupervised approach named SSG ++ to conduct the one-shot domain adaption in an open set setting (i. e. the number of independent identities from the target domain is unknown).
A Simple Non-i.i.d. Sampling Approach for Efficient Training and Better Generalization
While training on samples drawn from independent and identical distribution has been a de facto paradigm for optimizing image classification networks, humans learn new concepts in an easy-to-hard manner and on the selected examples progressively.
STA: Spatial-Temporal Attention for Large-Scale Video-based Person Re-Identification
Thus, a more robust clip-level feature representation can be generated according to a weighted sum operation guided by the mined 2-D attention score matrix.
Large-Scale Person Re-Identification
Video-Based Person Re-Identification
Weakly Supervised Scene Parsing with Point-based Distance Metric Learning
Semantic scene parsing is suffering from the fact that pixel-level annotations are hard to be collected.
Self-Erasing Network for Integral Object Attention
To test the quality of the generated attention maps, we employ the mined object regions as heuristic cues for learning semantic segmentation models.
SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation
In this way, the possibilities embedded in the produced similarity maps can be adapted to guide the process of segmenting objects.
Ranked #86 on
Few-Shot Semantic Segmentation
on PASCAL-5i (5-Shot)
Decoupled Classification Refinement: Hard False Positive Suppression for Object Detection
In particular, DCR places a separate classification network in parallel with the localization network (base detector).
Devil in the Details: Towards Accurate Single and Multiple Human Parsing
Human parsing has received considerable interest due to its wide application potentials.
Ranked #2 on
Person Re-Identification
on Market-1501-C
Self-produced Guidance for Weakly-supervised Object Localization
A stagewise approach is proposed to incorporate high confident object regions to learn the SPG masks.
Ranked #1 on
Weakly-Supervised Object Localization
on ILSVRC 2016
TS2C: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection
This work provides a simple approach to discover tight object bounding boxes with only image-level supervision, called Tight box mining with Surrounding Segmentation Context (TS2C).
Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi-Supervised Semantic Segmentation
Despite remarkable progress, weakly supervised segmentation methods are still inferior to their fully supervised counterparts.
Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation
It can produce dense and reliable object localization maps and effectively benefit both weakly- and semi- supervised semantic segmentation.
Object Localization
Semi-Supervised Semantic Segmentation
+1
Adversarial Complementary Learning for Weakly Supervised Object Localization
With such an adversarial learning, the two parallel-classifiers are forced to leverage complementary object regions for classification and can finally generate integral object localization together.
Ranked #2 on
Weakly-Supervised Object Localization
on ILSVRC 2016
General Classification
Weakly-Supervised Object Localization
Horizontal Pyramid Matching for Person Re-identification
Despite the remarkable recent progress, person re-identification (Re-ID) approaches are still suffering from the failure cases where the discriminative body parts are missing.
Ranked #55 on
Person Re-Identification
on DukeMTMC-reID
Left-Right Comparative Recurrent Model for Stereo Matching
Left-right consistency check is an effective way to enhance the disparity estimation by referring to the information from the opposite view.
Revisiting RCNN: On Awakening the Classification Power of Faster RCNN
Recent region-based object detectors are usually built with separate classification and localization branches on top of shared feature extraction networks.
Transferable Semi-supervised Semantic Segmentation
The performance of deep learning based semantic segmentation models heavily depends on sufficient data with careful annotations.
Learning to Segment Human by Watching YouTube
An intuition on human segmentation is that when a human is moving in a video, the video-context (e. g., appearance and motion clues) may potentially infer reasonable mask information for the whole human body.
Regional Interactive Image Segmentation Networks
The interactive image segmentation model allows users to iteratively add new inputs for refinement until a satisfactory result is finally obtained.
Ranked #9 on
Interactive Segmentation
on SBD
(NoC@85 metric)
Self-explanatory Deep Salient Object Detection
Through visualizing the differences, we can interpret the capability of different deep neural networks based saliency detection models and demonstrate that our proposed model indeed uses more reasonable structure for salient object detection.
Perceptual Generative Adversarial Networks for Small Object Detection
In this work, we address the small object detection problem by developing a single architecture that internally lifts representations of small objects to "super-resolved" ones, achieving similar characteristics as large objects and thus more discriminative for detection.
Multiple-Human Parsing in the Wild
To address the multi-human parsing problem, we introduce a new multi-human parsing (MHP) dataset and a novel multi-human parsing model named MH-Parser.
Ranked #3 on
Multi-Human Parsing
on MHP v2.0
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.
Deep Self-Taught Learning for Weakly Supervised Object Localization
To overcome this issue, we propose a deep self-taught learning approach, which makes the detector learn the object-level features reliable for acquiring tight positive samples and afterwards re-train itself based on them.
Weakly Supervised Object Detection
Weakly-Supervised Object Localization
Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach
We investigate a principle way to progressively mine discriminative object regions using classification networks to address the weakly-supervised semantic segmentation problems.
Bottom-Up Top-Down Cues for Weakly-Supervised Semantic Segmentation
We focus on the following three aspects of EM: (i) initialization; (ii) latent posterior estimation (E-step) and (iii) the parameter update (M-step).
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Attentive Contexts for Object Detection
We provide preliminary answers to these questions through developing a novel Attention to Context Convolution Neural Network (AC-CNN) based object detection model.
Instance-Aware Hashing for Multi-Label Image Retrieval
The instance-aware representations not only bring advantages to semantic hashing, but also can be used in category-aware hashing, in which an image is represented by multiple pieces of hash codes and each piece of code corresponds to a category.
Deep Learning with S-shaped Rectified Linear Activation Units
Rectified linear activation units are important components for state-of-the-art deep convolutional networks.
Towards Computational Baby Learning: A Weakly-Supervised Approach for Object Detection
Then the concept detector can be fine-tuned based on these new instances.
Reversible Recursive Instance-level Object Segmentation
By being reversible, the proposal refinement sub-network adaptively determines an optimal number of refinement iterations required for each proposal during both training and testing.
STC: A Simple to Complex Framework for Weakly-supervised Semantic Segmentation
Then, a better network called Enhanced-DCNN is learned with supervision from the predicted segmentation masks of simple images based on the Initial-DCNN as well as the image-level annotations.
Proposal-free Network for Instance-level Object Segmentation
Instance-level object segmentation is an important yet under-explored task.
Modality-dependent Cross-media Retrieval
Specifically, by jointly optimizing the correlation between images and text and the linear regression from one modal space (image or text) to the semantic space, two couples of mappings are learned to project images and text from their original feature spaces into two common latent subspaces (one for I2T and the other for T2I).
Computational Baby Learning
Then the concept detector can be fine-tuned based on these new instances.
CNN: Single-label to Multi-label
Convolutional Neural Network (CNN) has demonstrated promising performance in single-label image classification tasks.
Proximal Iteratively Reweighted Algorithm with Multiple Splitting for Nonconvex Sparsity Optimization
This paper proposes the Proximal Iteratively REweighted (PIRE) algorithm for solving a general problem, which involves a large body of nonconvex sparse and structured sparse related problems.
