Search Results for author:
Found 20 papers, 13 papers with code
Fixing Localization Errors to Improve Image Classification
This way, in an effort to fix localization errors, our loss provides an extra supervisory signal that helps the model to better discriminate between similar classes.
Object Segmentation by Mining Cross-Modal Semantics
In this paper, we propose a novel approach by mining the Cross-Modal Semantics to guide the fusion and decoding of multimodal features, with the aim of controlling the modal contribution based on relative entropy.
Indiscernible Object Counting in Underwater Scenes
We further advance the frontier of this field by systematically studying a new challenge named indiscernible object counting (IOC), the goal of which is to count objects that are blended with respect to their surroundings.
Mining Relations among Cross-Frame Affinities for Video Semantic Segmentation
The essence of video semantic segmentation (VSS) is how to leverage temporal information for prediction.
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.
SwinIR: Image Restoration Using Swin Transformer
In particular, the deep feature extraction module is composed of several residual Swin Transformer blocks (RSTB), each of which has several Swin Transformer layers together with a residual connection.
Ranked #2 on
Color Image Denoising
on urban100 sigma15
Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution
Extensive experiments on synthetic and real images show that the proposed MANet not only performs favorably for both spatially variant and invariant kernel estimation, but also leads to state-of-the-art blind SR performance when combined with non-blind SR methods.
Boosting Few-shot Semantic Segmentation with Transformers
Due to the fact that fully supervised semantic segmentation methods require sufficient fully-labeled data to work well and can not generalize to unseen classes, few-shot segmentation has attracted lots of research attention.
Vision Transformers with Hierarchical Attention
This paper tackles the low-efficiency flaw of the vision transformer caused by the high computational/space complexity in Multi-Head Self-Attention (MHSA).
Boosting Crowd Counting with Transformers
This indicates that global scene context is essential, despite the seemingly bottom-up nature of the problem.
Task Switching Network for Multi-Task Learning
Multiple tasks are performed by switching between them, performing one task at a time.
CompositeTasking: Understanding Images by Spatial Composition of Tasks
Learning to perform spatially distributed tasks is motivated by the frequent availability of only sparse labels across tasks, and the desire for a compact multi-tasking network.
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.
Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentation
Moreover, our approach ranked 1st place in the Weakly-Supervised Semantic Segmentation Track of CVPR2020 Learning from Imperfect Data Challenge.
Object Localization
Weakly supervised Semantic Segmentation
+1
Edge Guided GANs with Contrastive Learning for Semantic Image Synthesis
We propose a novel ECGAN for the challenging semantic image synthesis task.
Fine-grained Recognition: Accounting for Subtle Differences between Similar Classes
The main requisite for fine-grained recognition task is to focus on subtle discriminative details that make the subordinate classes different from each other.
Ranked #14 on
Fine-Grained Image Classification
on Stanford Dogs
Towards Partial Supervision for Generic Object Counting in Natural Scenes
Our RLC framework further reduces the annotation cost arising from large numbers of object categories in a dataset by only using lower-count supervision for a subset of categories and class-labels for the remaining ones.
Image Classification
Image-level Supervised Instance Segmentation
+2
iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images
Compared to existing small-scale aerial image based instance segmentation datasets, iSAID contains 15$\times$ the number of object categories and 5$\times$ the number of instances.
Ranked #1 on
Object Detection
on iSAID
Object Counting and Instance Segmentation with Image-level Supervision
Moreover, our approach improves state-of-the-art image-level supervised instance segmentation with a relative gain of 17. 8% in terms of average best overlap, on the PASCAL VOC 2012 dataset.
Ranked #1 on
Object Counting
on COCO count-test
Image-level Supervised Instance Segmentation
Object Counting
+1
Graph Embedding with Rich Information through Heterogeneous Network
In this paper, we proposed a novel and general framework of representation learning for graph with rich text information through constructing a bipartite heterogeneous network.
