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Works131 from public data
- 2,458
- Large-Scale Long-Tailed Recognition in an Open World1,483
An integrated OLTR algorithm is developed that maps an image to a feature space such that visual concepts can easily relate to each other based on a learned metric that respects the closed-world classification while acknowledging the novelty of the open world.
- Synthesized Classifiers for Zero-Shot Learning756
This work introduces a set of "phantom" object classes whose coordinates live in both the semantic space and the model space and demonstrates superior accuracy of this approach over the state of the art on four benchmark datasets for zero-shot learning.
- Adversarial Examples Improve Image Recognition634
This work proposes AdvProp, an enhanced adversarial training scheme which treats adversarial examples as additional examples, to prevent overfitting, and shows that AdvProp improves a wide range of models on various image recognition tasks and performs better when the models are bigger.
- PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation634
This work proposes a new LiDAR-specific, KNN-free segmentation algorithm - PolarNet, which greatly increases the mIoU in three drastically different real urban LiDar single-scan segmentation datasets while retaining ultra low latency and near real-time throughput.
- An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild629
It is shown that there is a large gap between the performance of existing approaches and the performance limit of GZSL, suggesting that improving the quality of class semantic embeddings is vital to improving ZSL.
- Spatiotemporal Contrastive Video Representation Learning600
This work proposes a temporally consistent spatial augmentation method to impose strong spatial augmentations on each frame of the video while maintaining the temporal consistency across frames, and proposes a sampling-based temporal augmentation methods to avoid overly enforcing invariance on clips that are distant in time.
- Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation522
This paper automatically discovers the existence of landmarks and uses them to bridge the source to the target by constructing provably easier auxiliary domain adaptation tasks, and shows how this composition can be optimized discriminatively without requiring labels from the target domain.
- Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes494
This work proposes a curriculum-style learning approach to minimize the domain gap in semantic segmentation, which significantly outperforms the baselines as well as the only known existing approach to the same problem.
- Diverse Sequential Subset Selection for Supervised Video Summarization485
This work proposes the sequential determinantal point process (seqDPP), a probabilistic model for diverse sequential subset selection, which heeds the inherent sequential structures in video data, thus overcoming the deficiency of the standard DPP.
- Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization Without Accessing Target Domain Data483
A new approach of domain randomization and pyramid consistency to learn a model with high generalizability for semantic segmentation of real-world self-driving scenes in a domain generalization fashion is proposed.
- A Fast and Accurate One-Stage Approach to Visual Grounding462
A simple, fast, and accurate one-stage approach to visual grounding that enables end-to-end joint optimization and shows great potential in terms of both accuracy and speed for both phrase localization and referring expression comprehension.
- MoViNets: Mobile Video Networks for Efficient Video Recognition313
Three progressive techniques allow MoViNets to achieve state-of-the-art accuracy and efficiency on the Kinetics, Moments in Time, and Charades video action recognition datasets and a simple ensembling technique to improve accuracy further without sacrificing efficiency is proposed.
- Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition From a Domain Adaptation Perspective312
This work connects existing class-balanced methods for long-tailed classification to target shift to reveal that these methods implicitly assume that the training data and test data share the same class-conditioned distribution, which does not hold in general and especially for the tail classes.
- Surrogate Gap Minimization Improves Sharpness-Aware Training240
GSAM seeks a region with both small loss and low sharpness, giving rise to a model with high generalization capabilities, and theoretically, shows the convergence of GSAM and provably better generalization than SAM.
- Constructing Self-Motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach239
This work proposes a new approach, called self-motivated pyramid curriculum domain adaptation (PyCDA), to facilitate the adaptation of semantic segmentation neural networks from synthetic source domains to real target domains and reports state-of-the-art results for the adaptation from both GTAV and SYNTHIA to Cityscapes.
- End-to-End Learning of Motion Representation for Video Understanding230
This work proposes TVNet, a novel end-to-end trainable neural network, to learn optical-flow-like features from data, which achieves better accuracies than all compared methods, while being competitive with the fastest counterpart in terms of features extraction time.
- Learning Attributes Equals Multi-Source Domain Generalization207
This work provides a novel perspective to attribute detection and proposes to gear the techniques in multi-source domain generalization for the purpose of learning cross-category generalizable attribute detectors.
- Contrastive Learning for Label Efficient Semantic Segmentation204
A simple and effective contrastive learning-based training strategy in which the network is pretrain the network using a pixel-wise, label-based contrastive loss, and then fine-tune it using the cross-entropy loss, which increases intra-class compactness and inter-class separability, thereby resulting in a better pixel classifier.
- Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds198
A Sparse Voxel Completion Network (SVCN) is designed to complete the 3D surfaces of a sparse point cloud to serve as a canonical domain from which semantic labels can transfer across different LiDAR sensors.
- Adversarially Adaptive Normalization for Single Domain Generalization176
A generic normalization approach, adaptive standardization and rescaling normalization (ASR-Norm), to complement the missing part in previous works and bring consistent improvement to the state-of-the-art ADA approaches.
- Look, Listen, and Act: Towards Audio-Visual Embodied Navigation174
This paper attempts to approach the problem of Audio-Visual Embodied Navigation, the task of planning the shortest path from a random starting location in a scene to the sound source in an indoor environment, given only raw egocentric visual and audio sensory data.
- Query-Focused Video Summarization: Dataset, Evaluation, and a Memory Network Based Approach155
This work explores the recently proposed query-focused video summarization which introduces user preferences in the form of text queries about the video into the summarization process and proposes a memory network parameterized sequential determinantal point process in order to attend the user query onto different video frames and shots.
- Open Compound Domain Adaptation146
This work proposes a new approach based on two technical insights into OCDA: a curriculum domain adaptation strategy to bootstrap generalization across domains in a data-driven self-organizing fashion and a memory module to increase the model's agility towards novel domains.
- A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban Scenes145
This work proposes a curriculum-style learning approach to minimizing the domain gap in urban scene semantic segmentation, which outperforms the baselines on two datasets and three backbone networks.
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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