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Works171 from public data
- MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition2,241
A benchmark task to recognize one million celebrities from their face images, by using all the possibly collected face images of this individual on the web as training data, which could lead to one of the largest classification problems in computer vision.
- Large Scale Incremental Learning1,588
This work found that the last fully connected layer has a strong bias towards the new classes, and this bias can be corrected by a linear model, and with two bias parameters, this method performs remarkably well on two large datasets.
- CRIS: CLIP-Driven Referring Image Segmentation567
This paper designs a vision-language decoder to propagate fine-grained semantic information from textual representations to each pixel-level activation, which promotes consistency between the two modalities and presents text-to-pixel contrastive learning.
- Recognize Anything: A Strong Image Tagging Model461
The Recognize Anything Model makes a substantial step for foundation models in computer vision, demonstrating the zero-shot ability to recognize any common category with high accuracy, by leveraging large-scale image-text pairs for training instead of manual annotations.
- Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study351
The UG2+ challenge Track 2 competition in IEEE CVPR 2019 is launched, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios.
- RoboMamba: Efficient Vision-Language-Action Model for Robotic Reasoning and Manipulation157
This work introduces RoboMamba, an end-to-end robotic VLA model that leverages Mamba to deliver both robotic reasoning and action capabilities, while maintaining efficient fine-tuning and inference.
- Structured Local Radiance Fields for Human Avatar Modeling151
This work introduces a novel representation on the basis of recent neural scene rendering techniques that enables automatic construction of animatable human avatars for various types of clothes without the need for scanning subject-specific templates, and can generate realistic images with dynamic details for novel poses.
- Personalized Image Aesthetics Assessment with Rich Attributes132
Experimental results indicate that the conditional PIAA model can outperform the control group, which is also the first attempt to demonstrate how image aesthetics and subject characters interact to produce the intricate personalized tastes on image aesthetics.
- Tag2Text: Guiding Vision-Language Model via Image Tagging121
This paper presents Tag2Text, a vision language pre-training (VLP) framework, which introduces image tagging into vision-language models to guide the learning of visual-linguistic features and effectively enhances the performance of vision-language models on both generation-based and aligning tasks.
- Discriminative Multi-Modality Speech Recognition118
A two-stage speech recognition model that consistently achieves the state-of-the-art performance by a significant margin is proposed, which demonstrates the necessity and effectiveness of AE-MSR.
- PiMAE: Point Cloud and Image Interactive Masked Autoencoders for 3D Object Detection117
This work proposes Pi-MAE, a self-supervised pre-training framework that promotes 3D and 2D interaction through three aspects, and designs a unique cross-modal reconstruction module to enhance representation learning for both modalities.
- View-Guided Point Cloud Completion117
This paper addresses this task by introducing ViPC (view-guided point cloud completion) that takes the missing crucial global structure information from an extra single-view image by leveraging a framework that sequentially performs effective cross-modality and cross-level fusions.
- Self-Distillation from the Last Mini-Batch for Consistency Regularization103
Experimental results on three classification benchmarks illustrate that the proposed self KD framework can consistently outperform state-of-the-art self-distillation approaches with different network architectures and shows strong compatibility with augmentation strategies by gaining additional performance improvement.
- Perceptual Extreme Super Resolution Network with Receptive Field Block102
A super resolution network with receptive field block based on Enhanced SRGAN based on RFB for the purpose of extracting multi-scale information and enhance the feature discriminability and the ensemble of 10 models of different iteration is used to improve the robustness of model and reduce the noise introduced by each individual model.
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- Learning Audio-Visual Source Localization via False Negative Aware Contrastive Learning76
This work proposes a new learning strategy named False Negative Aware Contrastive (FNAC), which utilizes the intra-modal similarities to identify potentially similar samples and construct corresponding adjacency matrices to guide contrastive learning.
- Learning to Detect Head Movement in Unconstrained Remote Gaze Estimation in the Wild60
Novel end-to-end appearance-based gaze estimation methods that could more robustly incorporate different levels of head-pose representations into gaze estimation are proposed that consistently outperforms the state-of-the-art by a significant margin.
- NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results54
This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results and gauges the state-of-the-art in single image super- resolution.
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- SoccerNet 2022 Challenges Results42
The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team, and tasks (3-6) were novel, including their underlying data and annotations.
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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