Search Results for author:
Found 65 papers, 34 papers with code
SD-ReID: View-aware Stable Diffusion for Aerial-Ground Person Re-Identification
Furthermore, we propose the View-Refine Decoder (VRD) to obtain additional controllable conditions to generate missing cross-view features.
LATex: Leveraging Attribute-based Text Knowledge for Aerial-Ground Person Re-Identification
Then, with these features, we propose a Prompted Attribute Classifier Group (PACG) to generate person attribute predictions and obtain the encoded representations of predicted attributes.
IDEA: Inverted Text with Cooperative Deformable Aggregation for Multi-modal Object Re-Identification
Extensive experiments on three multi-modal object ReID benchmarks demonstrate the effectiveness of our proposed method.
The Devil is in Temporal Token: High Quality Video Reasoning Segmentation
Existing methods for Video Reasoning Segmentation rely heavily on a single special token to represent the object in the keyframe or the entire video, inadequately capturing spatial complexity and inter-frame motion.
Ranked #1 on
Referring Video Object Segmentation
on ReVOS
AVS-Mamba: Exploring Temporal and Multi-modal Mamba for Audio-Visual Segmentation
To perform multi-modal fusion, we propose the Modality Aggregation Decoder, leveraging the Vision-to-Audio Fusion Block to integrate visual features into audio features across both frame and temporal levels.
Towards Open-Vocabulary Remote Sensing Image Semantic Segmentation
In this work, we introduce Open-Vocabulary Remote Sensing Image Semantic Segmentation (OVRSISS), which aims to segment arbitrary semantic classes in remote sensing images.
Unity is Strength: Unifying Convolutional and Transformeral Features for Better Person Re-Identification
We propose a novel fusion framework called FusionReID to unify the strengths of CNNs and Transformers for image-based person ReID.
MambaPro: Multi-Modal Object Re-Identification with Mamba Aggregation and Synergistic Prompt
Furthermore, current multi-modal aggregation methods have obvious limitations in dealing with long sequences from different modalities.
DeMo: Decoupled Feature-Based Mixture of Experts for Multi-Modal Object Re-Identification
To address these issues, we propose a novel feature learning framework called DeMo for multi-modal object ReID, which adaptively balances decoupled features using a mixture of experts.
Large Language Models for Lossless Image Compression: Next-Pixel Prediction in Language Space is All You Need
We have recently witnessed that ``Intelligence" and `` Compression" are the two sides of the same coin, where the language large model (LLM) with unprecedented intelligence is a general-purpose lossless compressor for various data modalities.
Test-time adaptation for image compression with distribution regularization
Current test- or compression-time adaptation image compression (TTA-IC) approaches, which leverage both latent and decoder refinements as a two-step adaptation scheme, have potentially enhanced the rate-distortion (R-D) performance of learned image compression models on cross-domain compression tasks, \textit{e. g.,} from natural to screen content images.
When Video Coding Meets Multimodal Large Language Models: A Unified Paradigm for Video Coding
Existing codecs are designed to eliminate intrinsic redundancies to create a compact representation for compression.
Multi-Scale and Detail-Enhanced Segment Anything Model for Salient Object Detection
Finally, we propose a Detail Enhancement Module (DEM) to incorporate SAM with fine-grained details.
MarvelOVD: Marrying Object Recognition and Vision-Language Models for Robust Open-Vocabulary Object Detection
Learning from pseudo-labels that generated with VLMs~(Vision Language Models) has been shown as a promising solution to assist open vocabulary detection (OVD) in recent studies.
Asymmetric Mask Scheme for Self-Supervised Real Image Denoising
In recent years, self-supervised denoising methods have gained significant success and become critically important in the field of image restoration.
MAS-SAM: Segment Any Marine Animal with Aggregated Features
In underwater scenes, it exhibits substantial performance degradation due to the light scattering and absorption.
Ranked #1 on
Image Segmentation
on RMAS
Other Tokens Matter: Exploring Global and Local Features of Vision Transformers for Object Re-Identification
In this work, we first explore the influence of global and local features of ViT and then further propose a novel Global-Local Transformer (GLTrans) for high-performance object Re-ID.
CompGS: Efficient 3D Scene Representation via Compressed Gaussian Splatting
Gaussian splatting, renowned for its exceptional rendering quality and efficiency, has emerged as a prominent technique in 3D scene representation.
Fantastic Animals and Where to Find Them: Segment Any Marine Animal with Dual SAM
To this end, we first introduce a dual structure with SAM's paradigm to enhance feature learning of marine images.
Sigma: Siamese Mamba Network for Multi-Modal Semantic Segmentation
Multi-modal semantic segmentation significantly enhances AI agents' perception and scene understanding, especially under adverse conditions like low-light or overexposed environments.
Ranked #4 on
Thermal Image Segmentation
on PST900
Magic Tokens: Select Diverse Tokens for Multi-modal Object Re-Identification
To address above issues, we propose a novel learning framework named \textbf{EDITOR} to select diverse tokens from vision Transformers for multi-modal object ReID.
TF-CLIP: Learning Text-free CLIP for Video-based Person Re-Identification
Technically, TMC allows the frame-level memories in a sequence to communicate with each other, and to extract temporal information based on the relations within the sequence.
TOP-ReID: Multi-spectral Object Re-Identification with Token Permutation
In addition, most of current Transformer-based ReID methods only utilize the global feature of class tokens to achieve the holistic retrieval, ignoring the local discriminative ones.
Part Representation Learning with Teacher-Student Decoder for Occluded Person Re-identification
In addition, existing occluded person ReID benchmarks utilize occluded samples as queries, which will amplify the role of alleviating occlusion interference and underestimate the impact of the feature absence issue.
SRSNetwork: Siamese Reconstruction-Segmentation Networks based on Dynamic-Parameter Convolution
In this paper, we present a high-performance deep neural network for weak target image segmentation, including medical image segmentation and infrared image segmentation.
TransY-Net:Learning Fully Transformer Networks for Change Detection of Remote Sensing Images
To relieve these issues, in this work we propose a novel Transformer-based learning framework named TransY-Net for remote sensing image CD, which improves the feature extraction from a global view and combines multi-level visual features in a pyramid manner.
Video-based Person Re-identification with Long Short-Term Representation Learning
Meanwhile, to extract short-term representations, we propose a Bi-direction Motion Estimator (BME), in which reciprocal motion information is efficiently extracted from consecutive frames.
Representation Learning
Video-Based Person Re-Identification
Geometric Prior Based Deep Human Point Cloud Geometry Compression
The emergence of digital avatars has raised an exponential increase in the demand for human point clouds with realistic and intricate details.
Deeply-Coupled Convolution-Transformer with Spatial-temporal Complementary Learning for Video-based Person Re-identification
Further, in spatial, we propose a Complementary Content Attention (CCA) to take advantages of the coupled structure and guide independent features for spatial complementary learning.
Learning Progressive Modality-shared Transformers for Effective Visible-Infrared Person Re-identification
Visible-Infrared Person Re-Identification (VI-ReID) is a challenging retrieval task under complex modality changes.
Fully Transformer Network for Change Detection of Remote Sensing Images
Then, we introduce a pyramid structure to aggregate multi-level visual features from Transformers for feature enhancement.
Distortion-Aware Loop Filtering of Intra 360^o Video Coding with Equirectangular Projection
In this paper, we propose a distortion-aware loop filtering model to improve the performance of intra coding for 360$^o$ videos projected via equirectangular projection (ERP) format.
URetinex-Net: Retinex-Based Deep Unfolding Network for Low-Light Image Enhancement
Retinex model-based methods have shown to be effective in layer-wise manipulation with well-designed priors for low-light image enhancement.
Mind Your Clever Neighbours: Unsupervised Person Re-identification via Adaptive Clustering Relationship Modeling
To generate high-quality pseudo-labels and mitigate the impact of clustering errors, we propose a novel clustering relationship modeling framework for unsupervised person Re-ID.
Global and Local Texture Randomization for Synthetic-to-Real Semantic Segmentation
In this work, we propose two simple yet effective texture randomization mechanisms, Global Texture Randomization (GTR) and Local Texture Randomization (LTR), for Domain Generalization based SRSS.
Sparse-to-dense Feature Matching: Intra and Inter domain Cross-modal Learning in Domain Adaptation for 3D Semantic Segmentation
Domain adaptation is critical for success when confronting with the lack of annotations in a new domain.
A Generalized Framework for Edge-preserving and Structure-preserving Image Smoothing
The effectiveness and superior performance of our approach are validated through comprehensive experiments in a range of applications.
HAT: Hierarchical Aggregation Transformers for Person Re-identification
In this work, we take advantages of both CNNs and Transformers, and propose a novel learning framework named Hierarchical Aggregation Transformer (HAT) for image-based person Re-ID with high performance.
A Video Is Worth Three Views: Trigeminal Transformers for Video-based Person Re-identification
To capture richer perceptions and extract more comprehensive video representations, in this paper we propose a novel framework named Trigeminal Transformers (TMT) for video-based person Re-ID.
Watching You: Global-guided Reciprocal Learning for Video-based Person Re-identification
Specifically, we first propose a Global-guided Correlation Estimation (GCE) to generate feature correlation maps of local features and global features, which help to localize the high- and low-correlation regions for identifying the same person.
Pyramid Spatial-Temporal Aggregation for Video-Based Person Re-Identification
Video-based person re-identification aims to associate the video clips of the same person across multiple non-overlapping cameras.
Ranked #1 on
Person Re-Identification
on DukeMTMC-VideoReID
Hierarchical Paired Channel Fusion Network for Street Scene Change Detection
Based on the MPFL strategy, our framework achieves a novel approach to adapt to the scale and location diversities of the scene change regions.
Semi-Supervised Crowd Counting via Self-Training on Surrogate Tasks
Most existing crowd counting systems rely on the availability of the object location annotation which can be expensive to obtain.
Towards Using Count-level Weak Supervision for Crowd Counting
Most existing crowd counting methods require object location-level annotation, i. e., placing a dot at the center of an object.
When Relation Networks meet GANs: Relation GANs with Triplet Loss
Though recent research has achieved remarkable progress in generating realistic images with generative adversarial networks (GANs), the lack of training stability is still a lingering concern of most GANs, especially on high-resolution inputs and complex datasets.
Improving Distant Supervised Relation Extraction by Dynamic Neural Network
To account for this style shift, the model should adjust its parameters in accordance with entity types.
Deep Multiphase Level Set for Scene Parsing
The proposed method consists of three modules, i. e., recurrent FCNs, adaptive multiphase level set, and deeply supervised learning.
Towards High-Resolution Salient Object Detection
This paper pushes forward high-resolution saliency detection, and contributes a new dataset, named High-Resolution Salient Object Detection (HRSOD).
Ranked #12 on
RGB Salient Object Detection
on DAVIS-S
(using extra training data)
Cascaded Context Pyramid for Full-Resolution 3D Semantic Scene Completion
To address these issues, in this work we propose a novel deep learning framework, named Cascaded Context Pyramid Network (CCPNet), to jointly infer the occupancy and semantic labels of a volumetric 3D scene from a single depth image.
Ranked #6 on
3D Semantic Scene Completion
on NYUv2
(using extra training data)
A Generalized Framework for Edge-preserving and Structure-preserving Image Smoothing
In this paper, a non-convex non-smooth optimization framework is proposed to achieve diverse smoothing natures where even contradictive smoothing behaviors can be achieved.
A Sketch Based 3D Shape Retrieval Approach Based on Efficient Deep Point-to-Subspace Metric Learning
A sketch based 3D shape retrieval
Salient Object Detection with Lossless Feature Reflection and Weighted Structural Loss
Inspired by the intrinsic reflection of natural images, in this paper we propose a novel feature learning framework for large-scale salient object detection.
Boundary-guided Feature Aggregation Network for Salient Object Detection
Fully convolutional networks (FCN) has significantly improved the performance of many pixel-labeling tasks, such as semantic segmentation and depth estimation.
Troy: Give Attention to Saliency and for Saliency
In addition, our work has text overlap with arXiv:1804. 06242, arXiv:1705. 00938 by other authors.
HyperFusion-Net: Densely Reflective Fusion for Salient Object Detection
Salient object detection (SOD), which aims to find the most important region of interest and segment the relevant object/item in that area, is an important yet challenging vision task.
Non-rigid Object Tracking via Deep Multi-scale Spatial-temporal Discriminative Saliency Maps
Extensive experiments demonstrate that the proposed algorithm achieves competitive performance in both saliency detection and visual tracking, especially outperforming other related trackers on the non-rigid object tracking datasets.
Video Person Re-identification by Temporal Residual Learning
In this paper, we propose a novel feature learning framework for video person re-identification (re-ID).
Agile Amulet: Real-Time Salient Object Detection with Contextual Attention
This paper proposes an Agile Aggregating Multi-Level feaTure framework (Agile Amulet) for salient object detection.
Unsupervised Band Selection of Hyperspectral Images via Multi-dictionary Sparse Representation
Band selection is a direct and effective method to remove redundant information and reduce the spectral dimension for decreasing computational complexity and avoiding the curse of dimensionality.
Salient Object Detection by Lossless Feature Reflection
Inspired by the intrinsic reflection of natural images, in this paper we propose a novel feature learning framework for large-scale salient object detection.
Robustness of classification ability of spiking neural networks
The numerical results show that there is not significant reduction in the classification ability of the network if the input signals are subject to sinusoidal and Gaussian perturbations.
A Stagewise Refinement Model for Detecting Salient Objects in Images
To remedy this problem, here we propose to augment feedforward neural networks with a novel pyramid pooling module and a multi-stage refinement mechanism for saliency detection.
Ranked #15 on
RGB Salient Object Detection
on DUTS-TE
(max F-measure metric)
Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection
In addition, to achieve accurate boundary inference and semantic enhancement, edge-aware feature maps in low-level layers and the predicted results of low resolution features are recursively embedded into the learning framework.
Ranked #21 on
RGB Salient Object Detection
on DUTS-TE
(max F-measure metric)
Learning Uncertain Convolutional Features for Accurate Saliency Detection
In this paper, we propose a novel deep fully convolutional network model for accurate salient object detection.
Ranked #5 on
Saliency Detection
on DUT-OMRON
