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Works206 from public data
- Learning Deep Features for Discriminative Localization10,981
This work revisits the global average pooling layer proposed in [13], and sheds light on how it explicitly enables the convolutional neural network (CNN) to have remarkable localization ability despite being trained on imagelevel labels.
- Places: A 10 Million Image Database for Scene Recognition4,995
The Places Database is described, a repository of 10 million scene photographs, labeled with scene semantic categories, comprising a large and diverse list of the types of environments encountered in the world, using the state-of-the-art Convolutional Neural Networks as baselines, that significantly outperform the previous approaches.
- Scene Parsing through ADE20K Dataset4,155
The ADE20K dataset, spanning diverse annotations of scenes, objects, parts of objects, and in some cases even parts of parts, is introduced and it is shown that the trained scene parsing networks can lead to applications such as image content removal and scene synthesis.
- Learning Deep Features for Scene Recognition using Places Database3,107
A new scene-centric database called Places with over 7 million labeled pictures of scenes is introduced with new methods to compare the density and diversity of image datasets and it is shown that Places is as dense as other scene datasets and has more diversity.
- Unified Perceptual Parsing for Scene Understanding2,690
A multi-task framework called UPerNet and a training strategy are developed to learn from heterogeneous image annotations and it is shown that it is able to effectively segment a wide range of concepts from images.
- Semantic Understanding of Scenes Through the ADE20K Dataset2,505
This work presents a densely annotated dataset ADE20K, which spans diverse annotations of scenes, objects, parts of objects, and in some cases even parts of parts, and shows that the networks trained on this dataset are able to segment a wide variety of scenes and objects.
- Network Dissection: Quantifying Interpretability of Deep Visual Representations1,855
This work uses the proposed Network Dissection method to test the hypothesis that interpretability is an axis-independent property of the representation space, then applies the method to compare the latent representations of various networks when trained to solve different classification problems.
- Object Detectors Emerge in Deep Scene CNNs1,334
This work demonstrates that the same network can perform both scene recognition and object localization in a single forward-pass, without ever having been explicitly taught the notion of objects.
- Interpreting the Latent Space of GANs for Semantic Face Editing1,275
This work proposes a novel framework, called InterFaceGAN, for semantic face editing by interpreting the latent semantics learned by GANs, and finds that the latent code of well-trained generative models actually learns a disentangled representation after linear transformations.
- Temporal Relational Reasoning in Videos1,150
This paper introduces an effective and interpretable network module, the Temporal Relation Network (TRN), designed to learn and reason about temporal dependencies between video frames at multiple time scales.
- Measuring human perceptions of a large-scale urban region using machine learning798
A deep learning model, which has been trained on millions of human ratings of street-level imagery, was used to predict human perceptions of a street view image and can help to map the distribution of the city-wide human perception for a new urban region.
- InterFaceGAN: Interpreting the Disentangled Face Representation Learned by GANs722
A framework called InterFaceGAN is proposed to interpret the disentangled face representation learned by the state-of-the-art GAN models and study the properties of the facial semantics encoded in the latent space to suggest that learning to synthesize faces spontaneously brings a disentangling and controllable face representation.
- In-Domain GAN Inversion for Real Image Editing697
An in-domain GAN inversion approach, which not only faithfully reconstructs the input image but also ensures the inverted code to be semantically meaningful for editing, which achieves satisfying real image reconstruction and facilitates various image editing tasks, significantly outperforming start-of-the-arts.
- Closed-Form Factorization of Latent Semantics in GANs664
This work examines the internal representation learned by GANs to reveal the underlying variation factors in an unsupervised manner and proposes a closedform factorization algorithm for latent semantic discovery by directly decomposing the pre-trained weights.
- GAN Inversion: A Survey653
This paper provides a survey of GAN inversion with a focus on its representative algorithms and its applications in image restoration and image manipulation, and discusses the trends and challenges for future research.
- Moments in Time Dataset: One Million Videos for Event Understanding642
The Moments in Time dataset, a large-scale human-annotated collection of one million short videos corresponding to dynamic events unfolding within three seconds, can serve as a new challenge to develop models that scale to the level of complexity and abstract reasoning that a human processes on a daily basis.
- Person Search with Natural Language Description593
An Recurrent Neural Network with Gated Neural Attention mechanism (GNA-RNN) is proposed to establish the state-of-the art performance on person search and a large-scale person description dataset with detailed natural language annotations and person samples from various sources is collected.
- 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.
- Understanding the role of individual units in a deep neural network553
This work presents network dissection, an analytic framework to systematically identify the semantics of individual hidden units within image classification and image generation networks, and applies it to understanding adversarial attacks and to semantic image editing.
- Multimodal Motion Prediction with Stacked Transformers537
A novel network architecture based on stacked transformers is designed to model the multimodality at feature level with a set of fixed independent proposals, and achieves the state-of-the-art performance on motion prediction.
- MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning497
The generalization experiments conducted on both procedurally generated scenarios and real-world scenarios show that increasing the diversity and the size of the training set leads to the improvement of the RL agent's generalizability.
- Places: An Image Database for Deep Scene Understanding495
The Places Database is described, a repository of 10 million scene photographs, labeled with scene semantic categories and attributes, comprising a quasi-exhaustive list of the types of environments encountered in the world.
- Temporal Pyramid Network for Action Recognition456
A generic Temporal Pyramid Network (TPN) at the feature-level is proposed, which can be flexibly integrated into 2D or 3D backbone networks in a plug-and-play manner and shows consistent improvements over other challenging baselines on several action recognition datasets.
- CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers453
The extensive experiments on the V2V perception dataset, OPV2V, demonstrate that CoBEVT achieves state-of-the-art performance for cooperative BEV semantic segmentation and is shown to be generalizable to other tasks, including 1) BEV segmentation with single-agent multi-camera and 2) 3D object detection with multi-agent LiDAR systems.
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 2026-10-15. 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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