Search Results for author:
Found 23 papers, 14 papers with code
Universal Fine-grained Visual Categorization by Concept Guided Learning
The discriminative concepts is utilized to guide the fine-grained representation learning.
Ranked #4 on
Fine-Grained Image Classification
on CUB-200-2011
Fine-Grained Image Classification
Fine-Grained Visual Categorization
+4
Parameter-Efficient Fine-Tuning via Selective Discrete Cosine Transform
In the era of large language models, parameter-efficient fine-tuning (PEFT) has been extensively studied.
Learning Spectral-Decomposed Tokens for Domain Generalized Semantic Segmentation
In this paper, we present a novel Spectral-dEcomposed Token (SET) learning framework to advance the frontier.
Modeling Weather Uncertainty for Multi-weather Co-Presence Estimation
In this paper, we start with solid revisit of the physics definition of weather and how it can be described as a continuous machine learning and computer vision task.
Learning Generalized Segmentation for Foggy-scenes by Bi-directional Wavelet Guidance
We argue that an ideal segmentation model that can be well generalized to foggy-scenes need to simultaneously enhance the content, de-correlate the urban-scene style and de-correlate the fog style.
Constraint Latent Space Matters: An Anti-anomalous Waveform Transformation Solution from Photoplethysmography to Arterial Blood Pressure
Arterial blood pressure (ABP) holds substantial promise for proactive cardiovascular health management.
GOOD: Towards Domain Generalized Orientated Object Detection
In this paper, we propose the task of domain generalized oriented object detection, which intends to explore the generalization of oriented object detectors on arbitrary unseen target domains.
Cross-Level Multi-Instance Distillation for Self-Supervised Fine-Grained Visual Categorization
To comprehensively learn the relation between informative patches and fine-grained semantics, the multi-instance knowledge distillation is implemented on both the region/image crop pairs from the teacher and student net, and the region-image crops inside the teacher / student net, which we term as intra-level multi-instance distillation and inter-level multi-instance distillation.
Fine-Grained Visual Categorization
Knowledge Distillation
+2
Interactive Learning of Intrinsic and Extrinsic Properties for All-day Semantic Segmentation
In this paper, in contrast to existing methods, we tackle this challenge from the perspective of image formulation itself, where the image appearance is determined by both intrinsic (e. g., semantic category, structure) and extrinsic (e. g., lighting) properties.
Ranked #1 on
Semantic Segmentation
on Mapillary val
Learning Content-enhanced Mask Transformer for Domain Generalized Urban-Scene Segmentation
Unlike domain gap challenges, USSS is unique in that the semantic categories are often similar in different urban scenes, while the styles can vary significantly due to changes in urban landscapes, weather conditions, lighting, and other factors.
Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications
Recently, Meta AI Research approaches a general, promptable Segment Anything Model (SAM) pre-trained on an unprecedentedly large segmentation dataset (SA-1B).
All grains, one scheme (AGOS): Learning multigrain instance representation for aerial scene classification
Finally, our SSF module allows our framework to learn the same scene scheme from multigrain instance representations and fuses them, so that the entire framework is optimized as a whole.
Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection
The laborious and time-consuming manual annotation has become a real bottleneck in various practical scenarios.
All Grains, One Scheme (AGOS): Learning Multi-grain Instance Representation for Aerial Scene Classification
Finally, our SSF allows our framework to learn the same scene scheme from multi-grain instance representations and fuses them, so that the entire framework is optimized as a whole.
Ranked #1 on
Scene Recognition
on AID
Label-efficient Hybrid-supervised Learning for Medical Image Segmentation
Due to the lack of expertise for medical image annotation, the investigation of label-efficient methodology for medical image segmentation becomes a heated topic.
Joint Semantic Mining for Weakly Supervised RGB-D Salient Object Detection
As a by-product, a CapS dataset is constructed by augmenting existing benchmark training set with additional image tags and captions.
Local semantic enhanced convnet for aerial scene recognition
Our LSE-Net consists of a context enhanced convolutional feature extractor, a local semantic perception module and a classification layer.
Ranked #2 on
Scene Recognition
on AID
Learning Calibrated Medical Image Segmentation via Multi-Rater Agreement Modeling
To our knowledge, our work is the first in producing calibrated predictions under different expertise levels for medical image segmentation.
Calibrated RGB-D Salient Object Detection
Complex backgrounds and similar appearances between objects and their surroundings are generally recognized as challenging scenarios in Salient Object Detection (SOD).
Ranked #3 on
Object Detection
on PKU-DDD17-Car
Temporal Cue Guided Video Highlight Detection With Low-Rank Audio-Visual Fusion
Video highlight detection plays an increasingly important role in social media content filtering, however, it remains highly challenging to develop automated video highlight detection methods because of the lack of temporal annotations (i. e., where the highlight moments are in long videos) for supervised learning.
A multiple-instance densely-connected ConvNet for aerial scene classification
It regards aerial scene classification as a multiple-instance learning problem so that local semantics can be further investigated.
Ranked #3 on
Scene Recognition
on AID
Multiple instance dense connected convolution neural network for aerial image scene classification
While the current convolution neural network tends to extract global features and global semantic information in a scene, the geo-spatial objects can be located at anywhere in an aerial image scene and their spatial arrangement tends to be more complicated.
Building change detection based on multi-scale filtering and grid partition
Exhaustive experiments indicate that the proposed method can detect building change types directly and outperform the current multi-index learning method.
