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Found 33 papers, 10 papers with code
PhraseClick: Toward Achieving Flexible Interactive Segmentation by Phrase and Click
We propose to employ phrase expressions as another interaction input to infer the attributes of target object.
A Closer Look at Few-shot Image Generation
Informed by our analysis and to slow down the diversity degradation of the target generator during adaptation, our second contribution proposes to apply mutual information (MI) maximization to retain the source domain's rich multi-level diversity information in the target domain generator.
Instance-Specific Feature Propagation for Referring Segmentation
In this work, we propose a novel framework that simultaneously detects the target-of-interest via feature propagation and generates a fine-grained segmentation mask.
Coarse-to-Fine Feature Mining for Video Semantic Segmentation
To address this problem, we propose a Coarse-to-Fine Feature Mining (CFFM) technique to learn a unified presentation of static contexts and motional contexts.
Flow-Guided Sparse Transformer for Video Deblurring
Exploiting similar and sharper scene patches in spatio-temporal neighborhoods is critical for video deblurring.
Time-Aware Neighbor Sampling for Temporal Graph Networks
In this work, we propose the TNS (Time-aware Neighbor Sampling) method: TNS learns from temporal information to provide an adaptive receptive neighborhood for every node at any time.
Structure-Aware Label Smoothing for Graph Neural Networks
Representing a label distribution as a one-hot vector is a common practice in training node classification models.
Directed Graph Contrastive Learning
However, it is still in its infancy with two concerns: 1) changing the graph structure through data augmentation to generate contrastive views may mislead the message passing scheme, as such graph changing action deprives the intrinsic graph structural information, especially the directional structure in directed graphs; 2) since GCL usually uses predefined contrastive views with hand-picking parameters, it does not take full advantage of the contrastive information provided by data augmentation, resulting in incomplete structure information for models learning.
Adaptive Data Augmentation on Temporal Graphs
To address this issue, our idea is to transform the temporal graphs using data augmentation (DA) with adaptive magnitudes, so as to effectively augment the input features and preserve the essential semantic information.
LONG-TAILED RECOGNITION BY LEARNING FROM LATENT CATEGORIES
In this work, we argue that there are common latent features between the head and tailed classes that can be used to give better feature representation.
Meta Navigator: Search for a Good Adaptation Policy for Few-shot Learning
Inspired by the recent success in Automated Machine Learning literature (AutoML), in this paper, we present Meta Navigator, a framework that attempts to solve the aforementioned limitation in few-shot learning by seeking a higher-level strategy and proffer to automate the selection from various few-shot learning designs.
Calibrating Class Activation Maps for Long-Tailed Visual Recognition
First, we present a Class Activation Map Calibration (CAMC) module to improve the learning and prediction of network classifiers, by enforcing network prediction based on important image regions.
Few-shot Segmentation with Optimal Transport Matching and Message Flow
We address the challenging task of few-shot segmentation in this work.
Vision-Language Transformer and Query Generation for Referring Segmentation
We introduce transformer and multi-head attention to build a network with an encoder-decoder attention mechanism architecture that "queries" the given image with the language expression.
Ranked #1 on
Referring Expression Segmentation
on RefCOCOg-val
Few-Shot Segmentation with Global and Local Contrastive Learning
To this end, we first propose a prior extractor to learn the query information from the unlabeled images with our proposed global-local contrastive learning.
M2IOSR: Maximal Mutual Information Open Set Recognition
In this work, we aim to address the challenging task of open set recognition (OSR).
Improving Video Instance Segmentation via Temporal Pyramid Routing
Moreover, our approach is a plug-and-play module and can be easily applied to existing instance segmentation methods.
Recovering the Unbiased Scene Graphs from the Biased Ones
Given input images, scene graph generation (SGG) aims to produce comprehensive, graphical representations describing visual relationships among salient objects.
Ranked #1 on
Unbiased Scene Graph Generation
on Visual Genome
Knowledge-aware Deep Framework for Collaborative Skin Lesion Segmentation and Melanoma Recognition
In this paper, we propose a novel knowledge-aware deep framework that incorporates some clinical knowledge into collaborative learning of two important melanoma diagnosis tasks, i. e., skin lesion segmentation and melanoma recognition.
MINE: Towards Continuous Depth MPI with NeRF for Novel View Synthesis
In this paper, we propose MINE to perform novel view synthesis and depth estimation via dense 3D reconstruction from a single image.
Towards Enhancing Fine-grained Details for Image Matting
In this paper, we argue that recovering these microscopic details relies on low-level but high-definition texture features.
Discovering Human Interactions With Large-Vocabulary Objects via Query and Multi-Scale Detection
In this work, we study the problem of human-object interaction (HOI) detection with large vocabulary object categories.
Interaction via Bi-Directional Graph of Semantic Region Affinity for Scene Parsing
In this work, we treat each respective region in an image as a whole, and capture the structure topology as well as the affinity among different regions.
Prototypical Matching and Open Set Rejection for Zero-Shot Semantic Segmentation
In this work, we present zero-shot semantic segmentation, which aims to identify not only the seen classes contained in training but also the novel classes that have never been seen.
A Unified 3D Human Motion Synthesis Model via Conditional Variational Auto-Encoder
Notably, by considering this problem as a conditional generation process, we estimate a parametric distribution of the missing regions based on the input conditions, from which to sample and synthesize the full motion series.
Else-Net: Elastic Semantic Network for Continual Action Recognition From Skeleton Data
This makes online continual action recognition a challenging task.
Object 6D Pose Estimation with Non-local Attention
In this paper, we address the challenging task of estimating 6D object pose from a single RGB image.
Bi-directional Dermoscopic Feature Learning and Multi-scale Consistent Decision Fusion for Skin Lesion Segmentation
Accurate segmentation of skin lesion from dermoscopic images is a crucial part of computer-aided diagnosis of melanoma.
Semantic Correlation Promoted Shape-Variant Context for Segmentation
In this way, the proposed network aggregates the context information of a pixel from its semantic-correlated region instead of a predefined fixed region.
Ranked #9 on
Semantic Segmentation
on COCO-Stuff test
Boundary-Aware Feature Propagation for Scene Segmentation
Furthermore, we propose a boundary-aware feature propagation (BFP) module to harvest and propagate the local features within their regions isolated by the learned boundaries in the UAG-structured image.
Ranked #28 on
Semantic Segmentation
on PASCAL Context
Toward Achieving Robust Low-Level and High-Level Scene Parsing
Furthermore, we introduce a “dense skip” architecture to retain a rich set of low-level information from the pre-trained CNN, which is essential to improve the low-level parsing performance.
Feature Boosting Network For 3D Pose Estimation
Learning a set of features that are reliable and discriminatively representative of the pose of a hand (or body) part is difficult due to the ambiguities, texture and illumination variation, and self-occlusion in the real application of 3D pose estimation.
Context Contrasted Feature and Gated Multi-Scale Aggregation for Scene Segmentation
In this paper, we first propose a novel context contrasted local feature that not only leverages the informative context but also spotlights the local information in contrast to the context.
Ranked #12 on
Semantic Segmentation
on COCO-Stuff test
