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- Deep Learning for Generic Object Detection: A Survey2,921
A comprehensive survey of the recent achievements in this field brought about by deep learning techniques, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics.
- Libra R-CNN: Towards Balanced Learning for Object Detection1,762
Libra R-CNN is proposed, a simple but effective framework towards balanced learning for object detection that integrates three novel components: IoU-balanced sampling, balanced feature pyramid, and balanced L1 loss, respectively for reducing the imbalance at sample, feature, and objective level.
- Hybrid Task Cascade for Instance Segmentation1,565
This work proposes a new framework, Hybrid Task Cascade (HTC), which differs in two important aspects: (1) instead of performing cascaded refinement on these two tasks separately, it interweaves them for a joint multi-stage processing; (2) it adopts a fully convolutional branch to provide spatial context, which can help distinguishing hard foreground from cluttered background.
- WorldSimBench: Towards Video Generation Models as World Simulators1,338
This work classify the functionalities of predictive models into a hierarchy and takes the first step in evaluating World Simulators by proposing a dual evaluation framework called WorldSimBench, encompassing human preference assessments from the visual perspective and action-level evaluations in embodied tasks.
- Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition1,222
A simple method to disentangle multi-scale graph convolutions and a unified spatial-temporal graph convolutional operator named G3D are presented and a powerful feature extractor named MS-G3D is developed based on which the model outperforms previous state-of-the-art methods on three large-scale datasets.
- Point Transformer V3: Simpler, Faster, Stronger1,171
This paper presents Point Transformer V3 (PTv3), which prioritizes simplicity and efficiency over the accuracy of certain mechanisms that are minor to the over-all performance after scaling, such as replacing the precise neighbor search by KNN with an efficient serialized neighbor mapping of point clouds organized with specific patterns.
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- Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification997
This work presents a pipeline for learning deep feature representations from multiple domains with Convolutional Neural Networks with CNNs and proposes a Domain Guided Dropout algorithm to improve the feature learning procedure.
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- DVC: An End-To-End Deep Video Compression Framework895
This paper proposes the first end-to-end video compression deep model that jointly optimizes all the components for video compression, and shows that the proposed approach can outperform the widely used video coding standard H.264 in terms of PSNR and be even on par with the latest standard MS-SSIM.
- Multi-context Attention for Human Pose Estimation736
This paper proposes to incorporate convolutional neural networks with a multi-context attention mechanism into an end-to-end framework for human pose estimation and designs novel Hourglass Residual Units (HRUs) to increase the receptive field of the network.
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- PAD-Net: Multi-tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing582
This paper proposes a novel multi-task guided prediction-and-distillation network (PAD-Net), which first predicts a set of intermediate auxiliary tasks ranging from low level to high level, and then the predictions from these intermediate Auxiliary tasks are utilized as multi-modal input via the authors' proposed multi- modal distillation modules for the final tasks.
- Equalization Loss for Long-Tailed Object Recognition577
This work proposes a simple but effective loss, named equalization loss, to tackle the problem of long-tailed rare categories by simply ignoring those gradients for rare categories, and wins the 1st place in the LVIS Challenge 2019.
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- Scene Graph Generation from Objects, Phrases and Region Captions560
This work proposes a novel neural network model, termed as Multi-level Scene Description Network (denoted as MSDN), to solve the three vision tasks jointly in an end-to-end manner and shows the joint learning across three tasks with the proposed method can bring mutual improvements over previous models.
- Scene Graph Generation from Objects, Phrases and Caption Regions.560
This work proposes a novel neural network model, termed as Multi-level Scene Description Network (denoted as MSDN), to solve the three vision tasks jointly in an end-to-end manner and shows the joint learning across three tasks with the proposed method can bring mutual improvements over previous models.
- SR-LSTM: State Refinement for LSTM Towards Pedestrian Trajectory Prediction556
A data-driven state refinement module for LSTM network (SR-LSTM), which activates the utilization of the current intention of neighbors, and jointly and iteratively refines the current states of all participants in the crowd through a message passing mechanism is proposed.
- T-CNN: Tubelets With Convolutional Neural Networks for Object Detection From Videos548
A deep learning framework that incorporates temporal and contextual information from tubelets obtained in videos, which dramatically improves the baseline performance of existing still-image detection frameworks when they are applied to videos is proposed, called T-CNN.
- Learning Feature Pyramids for Human Pose Estimation536
This work designs a Pyramid Residual Module (PRMs) to enhance the invariance in scales of DCNNs and provides theoretic derivation to extend the current weight initialization scheme to multi-branch network structures.
- Attention-Aware Compositional Network for Person Re-identification502
This work introduces a novel framework called Attention-Aware Compositional Network (AACN) for person ReID, which consists of two main components: Pose-guided Part Attention (PPA) and Attention-aware Feature Composition (AFC).
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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 2026-10-14. 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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