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- Relation-Aware Global Attention for Person Re-Identification654
This work proposes an effective Relation-Aware Global Attention (RGA) module which captures the global structural information for better attention learning and proposes to stack the relations, i.e., its pairwise correlations/affinities with all the feature positions together to learn the attention with a shallow convolutional model.
- Style Normalization and Restitution for Generalizable Person Re-Identification440
The aim of this paper is to design a generalizable person ReID framework which trains a model on source domains yet is able to generalize/perform well on target domains, and to enforce a dual causal loss constraint in SNR to encourage the separation of identity-relevant features and identity-irrelevant features.
- Image-to-Image Translation: Methods and Applications368
The key techniques of the existing I2I works are analyzed and clarified to clarify the main progress the community has made and point out remaining challenges in related fields.
- Inpaint Anything: Segment Anything Meets Image Inpainting334
The core idea behind IA is to combine the strengths of different models in order to build a very powerful and user-friendly pipeline for solving inpainting-related problems.
- Densely Semantically Aligned Person Re-Identification320
This work is the first to make use of fine grained semantics for addressing misalignment problems for re-ID and construct a set of densely semantically aligned part images (DSAP-images), where the same spatial positions have the same semantics across different person images.
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- Diffusion Models for Image Restoration and Enhancement: A Comprehensive Survey224
This first to present a comprehensive review of recent diffusion model-based methods on image restoration, encompassing the learning paradigm, conditional strategy, framework design, modeling strategy, and evaluation, and proposes nine potential and challenging directions for the future research of diffusion model-based IR.
- Region Normalization for Image Inpainting207
It is shown that the mean and variance shifts caused by full-spatial FN limit the image inpainting network training and a spatial region-wise normalization named Region Normalization (RN) is proposed to overcome the limitation.
- Cloth-Changing Person Re-identification from A Single Image with Gait Prediction and Regularization200
This paper introduces Gait recognition as an auxiliary task to drive the Image ReID model to learn cloth-agnostic representations by leveraging personal unique and cloth-independent gait information, and introduces this framework as GI-ReID, which enables an efficient and latency-free person identity matching for surveillance.
- Fast integer-pel and fractional-pel motion estimation for H.264/AVC195
A hybrid Unsymmetrical-cross Multi-hexagon-grid Search (UMHexagonS) algorithm is introduced, which well solves the false motion vector estimation problem because of the local-minimum and can save 30–50% computation compared with the Full Fractional-pel Search scheme.
- Causal Contextual Prediction for Learned Image Compression190
A causal context model is proposed that separates the latents across channels and makes use of channel-wise relationships to generate highly informative adjacent contexts and a causal global prediction model to find global reference points for accurate predictions of undecoded points.
- GraphIQA: Learning Distortion Graph Representations for Blind Image Quality Assessment185
A Distortion Graph Representation (DGR) learning framework for IQA is proposed, in which each distortion is represented as a graph, i.e., DGR, and one can distinguish distortion types by learning the contrast relationship between these different DGRs, and can infer the ranking distribution of samples from different levels in a DGR.
- Task Residual for Tuning Vision-Language Models182
A new efficient tuning approach for VLMs named Task Residual Tuning (TaskRes), which performs directly on the text-based classifier and explicitly decouples the prior knowledge of the pre-trained models and new knowledge regarding a target task.
- Semantics-Aligned Representation Learning for Person Re-Identification164
A framework that drives the reID network to learn semantics-aligned feature representation through delicate supervision designs is proposed and achieves the state-of-the-art performances on the benchmark datasets CUHK03, Market1501, MSMT17, and the partial person reID dataset Partial REID.
- Blind Omnidirectional Image Quality Assessment With Viewport Oriented Graph Convolutional Networks162
This paper proposes a novel Viewport oriented Graph Convolution Network (VGCN) for blind omnidirectional image quality assessment (IQA) and demonstrates that the proposed model outperforms state-of-the-art full-reference and no-reference IQA metrics on two public omniddirectional IQA databases.
- Conditional Image-to-Image Translation147
This paper twists two conditional translation models together for inputs combination and reconstruction while preserving domain independent features and carries out experiments on men's faces from-to women's faces translation and edges to shoes&bags translations.
- Locating induced earthquakes with a network of seismic stations in Oklahoma via a deep learning method146
A fully convolutional network is developed and located earthquakes induced during oil and gas operations in Oklahoma with data from 30 network stations and the automated system is able to distinguish certain interfered events or events out of the monitoring zone based on the output probability estimate.
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- Learning for Video Compression144
The proposed PixelMotionCNN (PMCNN) which includes motion extension and hybrid prediction networks can model spatiotemporal coherence to effectively perform predictive coding inside the learning network and provides a possible new direction to further improve compression efficiency and functionalities of future video coding.
- GraphAdapter: Tuning Vision-Language Models With Dual Knowledge Graph138
An effective adapter-style tuning strategy, dubbed GraphAdapter, which performs the textual adapter by explicitly modeling the dual-modality structure knowledge with a dual knowledge graph, which significantly outperforms previous adapter-based methods.
- Training-free Camera Control for Video Generation136
The capability of CamTrol to generalize to various base models, as well as its impressive applications in scalable motion control, dealing with complicated trajectories and unsupervised 3D video generation, are shown.
- MetaAlign: Coordinating Domain Alignment and Classification for Unsupervised Domain Adaptation135
This paper proposes an effective meta-optimization based strategy dubbed MetaAlign, which treats the domain alignment objective and the classification objective as the meta-train and meta-test tasks in a meta-learning scheme, and encourages both tasks to be optimized in a coordinated way.
- Towards Accurate One-Stage Object Detection With AP-Loss135
A novel optimization algorithm is developed, which seamlessly combines the error-driven update scheme in perceptron learning and backpropagation algorithm in deep networks, and good convergence property of the proposed algorithm is verified theoretically and empirically.
- Multi-Granularity Reference-Aided Attentive Feature Aggregation for Video-Based Person Re-Identification132
This paper proposes an attentive feature aggregation module, namely Multi-Granularity Reference-aided Attentive Feature Aggregation (MG-RAFA), to delicately aggregate spatio-temporal features into a discriminative video-level feature representation.
- Uncertainty-Aware Multi-Shot Knowledge Distillation for Image-Based Object Re-Identification127
This paper designs an Uncertainty-aware Multi-shot Teacher-Student (UMTS) Network that consists of a teacher network that learns the comprehensive features from multiple images of the same object, and a student network that takes a single image as input.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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