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Works391 from public data
- Fully Convolutional Instance-Aware Semantic Segmentation1,071
The first fully convolutional end-to-end solution for instance-aware semantic segmentation task, which achieves state-of-the-art performance in both accuracy and efficiency, wins the COCO 2016 segmentation competition by a large margin.
- DeepIM: Deep Iterative Matching for 6D Pose Estimation817
A novel deep neural network for 6D pose matching named DeepIM is proposed, trained to predict a relative pose transformation using a disentangled representation of 3D location and 3D orientation and an iterative training process.
- CIFAR10-DVS: An Event-Stream Dataset for Object Classification761
This work provides a large event-stream dataset and an initial benchmark for comparison, which may boost algorithm developments in even-driven pattern recognition and object classification, based on state-of-the-art classification algorithms.
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- GDRNPP: A Geometry-Guided and Fully Learning-Based Object Pose Estimator312
This work proposes a simple yet effective Geometry-guided Direct Regression Network (GDRN) to learn the 6D pose from monocular images in an end-to-end manner and introduces a geometry-guided pose refinement module, enhancing pose accuracy when extra depth data is available.
- Depth Image Denoising Using Nuclear Norm and Learning Graph Model289
A group-based nuclear norm and learning graph (GNNLG) model, where for each patch, the most similar patches within a searching window are found, which is superior to other current state-of-the-art denoising methods in both subjective and objective criterion.
- C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection261
A continuation optimization method is introduced into MIL and thereby creating continuation multiple instance learning (C-MIL), with the intention of alleviating the non-convexity problem in a systematic way.
- Beyond Bounding-Box: Convex-hull Feature Adaptation for Oriented and Densely Packed Object Detection258
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- Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon214
A new type of optical adversarial examples, in which the perturbations are generated by a very common natural phenomenon, shadow, to achieve naturalistic and stealthy physical-world adversarial attack under the black-box setting is studied.
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- GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting192
GPV-Pose is proposed, a novel framework for robust category-level pose estimation, harnessing geometric insights to enhance the learning of category- level pose-sensitive features, which produces superior results to state-of-the-art competitors on common public benchmarks, whilst almost achieving real-time inference speed at 20 FPS.
- Precise No-Reference Image Quality Evaluation Based on Distortion Identification183
This article proposes a novel scheme for precise NR IQA, which includes two successive steps, i.e., distortion identification and targeted quality evaluation, and employs the well-known Inception-ResNet-v2 neural network to train a classifier that classifies the possible distortion in the image into the four most common distortion types.
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- SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation174
This work addresses the challenge of directly regressing all 6 degrees-of-freedom for the object pose in a cluttered environment from a single RGB image by means of a novel reasoning about self-occlusion, in order to establish a two-layer representation for 3D objects which considerably enhances the accuracy of end-to-end 6D pose estimation.
- Multiple Instance Active Learning for Object Detection170
MI-AOD defines an instance uncertainty learning module, which leverages the discrepancy of two adversarial instance classifiers trained on the labeled set to predict instance uncertainty of the unlabeled set, and estimates the image uncertainty by re-weighting instances in a multiple instance learning (MIL) fashion.
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- Self6D: Self-supervised Monocular 6D Object Pose Estimation161
This work proposes the idea of monocular 6D pose estimation by means of self-supervised learning, removing the need for real annotations, and demonstrates that the proposed self- supervision model is able to significantly enhance the model's original performance, outperforming all other methods relying on synthetic data or employing elaborate techniques from the domain adaptation realm.
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- Feasibility of using deep learning to detect coronary artery disease based on facial photo152
The results suggested that a deep learning algorithm based on facial photos can assist in CAD detection in this Chinese cohort and may hold promise for pre-test CAD probability assessment in outpatient clinics or CAD screening in community.
- CAS-ViT: Convolutional Additive Self-Attention Vision Transformers for Efficient Mobile Applications116
This paper argues that the capability of token mixers to obtain global contextual information hinges on multiple information interactions, such as spatial and channel domains, and proposes Convolutional Additive Token Mixer (CATM) employing underlying spatial and channel attention as novel interaction forms, to achieve a balance between efficiency and performance in mobile applications.
- DiffusionRet: Generative Text-Video Retrieval with Diffusion Model113
This work creatively tackles the text-video retrieval task from a generative viewpoint and model the correlation between the text and the video as their joint probability p(candidates,query), which cleverly leverages the strengths of both generative and discriminative methods.
- UATVR: Uncertainty-Adaptive Text-Video Retrieval102
This paper proposes an Uncertainty-Adaptive Text-Video Retrieval approach, termed UATVR, which models each lookup as a distribution matching procedure and adds additional learnable tokens in the encoders to adaptively aggregate multi-grained semantics for flexible high-level reasoning.
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- Video-Text as Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation Learning99
This paper creatively model video-text as game players with multivariate cooperative game theory to wisely handle the uncertainty during fine-grained semantic interaction with diverse granularity, flexible combination, and vague intensity in order to achieve cooperative games at different semantic levels.
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- Learning with Noisy Labels via Sparse Regularization82
The sparse regularization strategy to approximate the one-hot constraint is introduced, which is composed of network output sharpening operation that enforces the output distribution of a net-work to be sharp and the ℓp-norm regularization that promotes the network output to be sparse.
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- High-Resolution Depth Maps Imaging via Attention-Based Hierarchical Multi-Modal Fusion79
To effectively extract and combine relevant information from LR depth and HR guidance, a multi-modal attention based fusion (MMAF) strategy for hierarchical convolutional layers, including a feature enhancement block to select valuable features and a feature recalibration block to unify the similarity metrics of modalities with different appearance characteristics are proposed.
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- CarsiDock: a deep learning paradigm for accurate protein–ligand docking and screening based on large-scale pre-training76
CarsiDock is proposed, the first deep learning-guided docking approach that leverages pre-training of millions of predicted protein–ligand complexes and can not only guarantee the topological reliability of the binding poses but also successfully reproduce the crucial interactions in crystalized structures, highlighting its superior applicability.
- Towards End-to-End Image Compression and Analysis with Transformers75
An end-to-end image compression and analysis model with Transformers, targeting to the cloud-based image classification application, which aims to perform image classification from the compressed features and facilitate image compression with the long-term information from the Transformer.
- Fast and High Quality Highlight Removal From a Single Image75
This paper derives a normalized dichromatic model for the pixels with identical diffuse color: a unit circle equation of projection coefficients in two subspaces that are orthogonal to and parallel with the illumination, respectively.
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- Out-of-Candidate Rectification for Weakly Supervised Semantic Segmentation73
A group ranking-based Out-of-Candidate Rectification (OCR) mechanism in a plug-and-play fashion that can achieve remarkable performance gains on both Pascal VOC and MS COCO datasets with negligible extra training overhead, which jus-tifies the effectiveness and generality of OCR.
- Deep Lossy Plus Residual Coding for Lossless and Near-Lossless Image Compression72
This paper proposes a unified and powerful deep lossy plus residual (DLPR) coding framework for both lossless and near-lossless image compression, and solves the joint lossy and residual compression problem in the approach of VAEs.
- Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation72
This paper proposes a novel approach that achieves self-supervised and magnification-flexible point clouds upsampling simultaneously, and defines two implicit neural functions to estimate projection direction and distance respectively, which can be trained by two pretext learning tasks.
- Computational optical sectioning with an incoherent multiscale scattering model for light-field microscopy72
An incoherent multiscale scattering model is presented which allows for quantitative 3D reconstruction in complex environments, and the superior performance is validated by imaging various biological dynamics in Drosophila embryos, zebrafish larvae, and mice.
- Guided Depth Map Super-Resolution: A Survey71
A comprehensive survey of recent progress in guided depth map super-resolution (GDSR), which aims to reconstruct a high-resolution depth map from a low-resolution observation with the help of a paired high- resolution color image, is presented.
- Deep Attentional Guided Image Filtering63
This work proposes an attentional kernel learning module to generate dual sets of filter kernels from the guidance and the target and then adaptively combine them by modeling the pixelwise dependency between the two images.
- Anti-aliasing Semantic Reconstruction for Few-Shot Semantic Segmentation61
This paper reformulates few-shot segmentation as a semantic reconstruction problem, and converts base class features into a series of basis vectors which span a class-level semantic space for novel class reconstruction, which provides a systematic yet interpretable solution for few- shot learning problems.
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- Diversify: A General Framework for Time Series Out-of-Distribution Detection and Generalization56
Qualitative and quantitative results demonstrate that Diversify learns more generalized features and significantly outperforms other baselines, as well as theoretical insights support the framework's validity.
- SSP-Pose: Symmetry-Aware Shape Prior Deformation for Direct Category-Level Object Pose Estimation55
An end-to-end trainable network SSP-Pose is proposed for category-level pose estimation, which integrates shape priors into a direct pose regression network and utilizes the symmetry information of each category to guide the shape prior deformation, and proposes a novel symmetry-aware loss to mitigate the matching ambiguity.
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- Range Scaling Global U-Net for Perceptual Image Enhancement on Mobile Devices54
This paper presents a novel deep learning based approach—the Range Scaling Global U-Net (RSGUNet)—for perceptual image enhancement on mobile devices that learns a global feature vector as well as a novel range scaling layer that alleviate artifacts in the enhanced images.
- Prior-information-free single-shot scattering imaging beyond the memory effect53
A prior-information-free single-shot scattering imaging method to exceed the ME range via Fourier spectrum guessing and iterative energy constrained compensation that can be reconstructed via a single shot.
- Unpaired Multi-Domain Stain Transfer for Kidney Histopathological Images49
A network based on unpaired training data is proposed to virtually generate multiple types of staining from one staining, and a style guided normalization (SGN) is devised to represent the relationship among different staining styles dynamically.
- Domain Contrast for Domain Adaptive Object Detection49
Domain Contrast is presented, a simple yet effective approach inspired by contrastive learning for training domain adaptive detectors that improves the baseline and state-of-the-art by significant margins, while demonstrating great potential for large domain divergence.
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- PUERT: Probabilistic Under-Sampling and Explicable Reconstruction Network for CS-MRI47
A novel end-to-end Probabilistic Under-sampling and Explicable Reconstruction neTwork, dubbed PUERT, to jointly optimize the sampling pattern and the reconstruction network, which achieves state-of-the-art results in terms of both quantitative metrics and visual quality.
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- DPFlow: Adaptive Optical Flow Estimation with a Dual-Pyramid Framework46
This paper proposes DPFlow, an adaptive optical flow architecture capable of generalizing up to 8K resolution inputs while trained with only low-resolution samples, and introduces Kubric-NK, a new benchmark for evaluating optical flow methods with input resolutions ranging from 1K to 8K.
- Dynamic Filtering with Large Sampling Field for ConvNets46
A dynamic filtering strategy with large sampling field for ConvNets (LS-DFN), where the position-specific kernels learn from not only the identical position but also multiple sampled neighbour regions, which efficiently alleviates the overfitting issue caused by much more parameters than normal CNNs.
- Cross-Modal Retrieval between 13C NMR Spectra and Structures for Compound Identification Using Deep Contrastive Learning45
A cross-modal retrieval between spectrum and structure (CReSS) system using deep contrastive learning, which allows us to search a molecular structure library using the 13C NMR spectrum of a compound.
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- Towards fairness-aware and privacy-preserving enhanced collaborative learning for healthcare42
A framework for healthcare institutions with varying resources to fairly collaborate on AI training while protecting patient privacy is developed, ensuring equitable access to high-quality medical AI regardless of resource constraints.
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- Recurrent Partial Kernel Network for Efficient Optical Flow Estimation38
This paper designs a new spatial recurrent encoder that extracts discriminative features at a significantly reduced size and utilizes Partial Kernel Convolution (PKConv) layers to produce variable multi-scale features with a single shared block.
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- PFRL: Pose-Free Reinforcement Learning for 6D Pose Estimation36
The Pose-Free approach is able to achieve state-of-the-art performance compared with the methods without using real-world ground-truth 6D pose labels, via a delicate reward definition and a composite reinforced optimization method for efficient and effective policy training.
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- TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization30
A novel statistics-based approach, Two-WIng NormliSation (TWINS)fine-tuning framework, which consists of two neural networks where one of them keeps the population means and variances of pre-training data in the batch normalization layers and is shown to be effective on a wide range of image classification datasets in terms of both generalization and robustness.
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- TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classication24
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- Raw Image Reconstruction From Rgb on Smartphones. Ntire 2025 Challenge Report23
This paper aims to recover RAW sensor images from smartphones given the corresponding sRGB images without metadata and, by doing this, “reverse” the ISP transformation.
- An Empirical Study on Distribution Shift Robustness From the Perspective of Pre-Training and Data Augmentation23
This paper studies the distribution shift problem from the perspective of pre-training and data augmentation, two important factors in the practice of deep learning that have not been systematically investigated by existing work.
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- Safe Multi-Agent Reinforcement Learning for Wireless Applications Against Adversarial Communications21
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- Learning Foreground-Background Segmentation from Improved Layered GANs20
A method to automatically synthesize paired photo-realistic images and segmentation masks for the use of training a foreground-background segmentation network that decomposes an image into foreground and background layers and avoids trivial decompositions is proposed.
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- The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks11
This study investigates the uncertainty of deep learning models by examining the performance of conformal prediction (CP) in the context of standard adversarial attacks within the adversarial defense community and proposed Uncertainty-Reducing AT (AT-UR) is validates the effectiveness.
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- LaPE: Layer-adaptive Position Embedding for Vision Transformers with Independent Layer Normalization8
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- Unsupervised Foreground-Background Segmentation with Equivariant Layered GANs.8
An unsupervised foreground-background segmentation method via training a segmentation network on the synthetic pseudo segmentation dataset generated from GANs, which are trained from a collection of images without annotations to explicitly disentangle foreground and background.
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- Learning to Annotate Part Segmentation with Gradient Matching7
This paper focuses on tackling semi-supervised part segmentation tasks by generating high-quality images with a pre-trained GAN and labelling the generated images with an automatic annotator, and formulate the annotator learning as a learning-to-learn problem.
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- Technique Report of CVPR 2024 PBDL Challenges2
This technical report summarizes the outcomes of the Physics-Based Vision Meets Deep Learning (PBDL) 2024 challenge, held in CVPR 2024 workshop, focusing on Low-Light Enhancement and Detection as well as High Dynamic Range (HDR) Imaging.
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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 2026-10-13. 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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