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- 436
- Vision-Language Transformer and Query Generation for Referring Segmentation398
Transformer and multi-head attention are introduced and a Query Generation Module is proposed, which produces multiple sets of queries with different attention weights that represent the diversified comprehensions of the language expression from different aspects.
- GRES: Generalized Referring Expression Segmentation358
A region-based GRES baseline ReLA is proposed that adaptively divides the image into regions with subinstance clues, and explicitly models the region-region and region-language dependencies and achieves new state-of-the-art performance on the both newly proposed GRES and classic RES tasks.
- Transformer-Based Visual Segmentation: A Survey342
This survey provides a thorough overview of transformer-based visual segmentation, summarizing recent advancements and presents a meta-architecture that unifies all recent transformer-based approaches.
- MOSE: A New Dataset for Video Object Segmentation in Complex Scenes307
The experiments show that current VOS algorithms cannot well perceive objects in complex scenes, and there are unresolved challenges under complex scenes and more efforts are desired to explore these challenges in the future.
- MeViS: A Large-scale Benchmark for Video Segmentation with Motion Expressions305
The results show that current RVOS methods cannot effectively address motion expression-guided video segmentation, and a large-scale dataset called MeViS is proposed, which contains numerous motion expressions to indicate target objects in complex environments.
- Boundary-Aware Feature Propagation for Scene Segmentation292
A boundary-aware feature propagation (BFP) module to harvest and propagate the local features within their regions isolated by the learned boundaries in the UAG-structured image and achieves new state-of-the-art segmentation performance on three challenging semantic segmentation datasets, i.e., PASCAL-Context, CamVid, and Cityscapes.
- Towards Open Vocabulary Learning: A Survey285
This paper thoroughly reviews open vocabulary learning, summarizing and analyzing recent developments in the field, and juxtaposing open vocabulary learning with analogous concepts such as zero-shot learning, open-set recognition, and out-of-distribution detection.
- Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging225
A principled Degradation-Aware Unfolding Framework (DAUF) that estimates parameters from the compressed image and physical mask, and then uses these parameters to control each iteration, and customize a novel Half-Shuffle Transformer (HST) that simultaneously captures local contents and non-local dependencies.
- VLT: Vision-Language Transformer and Query Generation for Referring Segmentation196
A Query Generation Module, which dynamically produces multiple sets of input-specific queries to represent the diverse comprehensions of language expression, and a Query Balance Module to selectively fuse the corresponding responses of the set of queries to enhance the model's ability in dealing with diverse language expressions.
- MINE: Towards Continuous Depth MPI with NeRF for Novel View Synthesis194
The proposed MINE is a continuous depth generalization of the Multiplane Images (MPI) by introducing the NEural radiance fields (NeRF) to perform novel view synthesis and depth estimation via dense 3D reconstruction from a single image.
- Semantic Correlation Promoted Shape-Variant Context for Segmentation193
This work proposes a novel paired convolution to infer the semantic correlation of the pair and based on that to generate a shape mask, of which the receptive field is controlled by the shape mask that varies with the appearance of input.
- 157
- OMG-Seg: Is One Model Good Enough for all Segmentation?140
It is shown that OMG-Seg, a transformer-based encoder-decoder architecture with task-specific queries and outputs, can support over ten distinct segmentation tasks and yet significantly reduce computational and parameter overhead across various tasks and datasets.
- Recovering the Unbiased Scene Graphs from the Biased Ones139
This paper shows that, due to the missing labels, SGG can be viewed as a "Learning from Positive and Unlabeled data" (PU learning) problem, where the reporting bias can be removed by recovering the unbiased probabilities from the biased ones by utilizing label frequencies.
- Feature Boosting Network For 3D Pose Estimation135
A context consistency gate (CCG) is introduced in this paper, with which the convolutional feature maps are modulated according to their consistency with the context representations, to improve the reliability of the features for representing each body part and enhance the LSTD module.
- VGSG: Vision-Guided Semantic-Group Network for Text-Based Person Search110
A Vision-Guided Semantic-Group Network (VGSG) is proposed for text-based person search to extract well-aligned fine-grained visual and textual features and a relational knowledge transfer is designed to adaptively propagate information of the vision-guided textual features to semantic-group textual features.
- A Closer Look at Few-shot Image Generation97
Extensive experiments show that the proposed DCL brings visually pleasant quality and state-of-the-art quantitative performance, while leading to a slower diversity-degrading generator during adaptation, the method is still plenty of room to further slow down diversity degradation.
- 94
- Decoupling Static and Hierarchical Motion Perception for Referring Video Segmentation88
This work proposes to de-couple video-level referring expression understanding into static and motion perception, with a specific emphasis on enhancing temporal comprehension, and introduces an expression-decoupling module to make static cues and motion cues perform their distinct role.
- Region Generation and Assessment Network for Occluded Person Re-Identification88
A Region Generation and Assessment Network (RGANet) to effectively and efficiently detect the human body regions and highlight the important regions is proposed and extensive experimental results demonstrate the superiority of RGANet against state-of-the-art methods.
- 88
- Prototype Adaption and Projection for Few- and Zero-Shot 3D Point Cloud Semantic Segmentation87
A Query-Guided Prototype Adaption (QGPA) module to adapt the prototype from support point clouds feature space to query point cloud feature space is proposed and a semantic-visual projection model is proposed to bridge the semantic and visual spaces.
- 87
- OVTrack: Open-Vocabulary Multiple Object Tracking86
OVTrack is developed, an open-vocabulary tracker that is capable of tracking arbitrary object classes and its design is based on two key ingredients, leveraging vision-language models for both classification and association via knowledge distillation and a data hallucination strategy for robust appearance feature learning from denoising diffusion probabilistic models.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. 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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