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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.
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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