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Works540 from public data
- Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling1,232
Point-BERT, a new paradigm for learning Transformers to generalize the concept of BERT to 3D point cloud, is presented and it is shown that a pure Transformer architecture attains 93.8% accuracy on ModelNet40 and 83.1% accuracy in the hardest setting of ScanObjectNN, surpassing carefully designed point cloud models with much fewer hand-made designs.
- DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting886
This work presents a new framework for dense prediction by implicitly and explicitly leveraging the pre-trained knowledge from CLIP and converts the original image-text matching problem in CLIP to a pixel- text matching problem and uses the pixel-text score maps to guide the learning of dense prediction models.
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- PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers679
A new method is presented that reformulates point cloud completion as a set-to-set translation problem and design a new model, called PoinTr, that adopts a transformer encoder-decoder architecture for point clouds completion that outperforms state-of-the-art methods by a large margin.
- Tri-Perspective View for Vision-Based 3D Semantic Occupancy Prediction598
A tri-perspective view (TPV) representation which accompanies BEV with two additional perpendicular planes is proposed, and it is demonstrated for the first time that using only camera inputs can achieve comparable performance with LiDAR-based methods on theLiDAR segmentation task on nuScenes.
- A Siamese Long Short-Term Memory Architecture for Human Re-identification596
A novel siamese Long Short-Term Memory (LSTM) architecture that can process image regions sequentially and enhance the discriminative capability of local feature representation by leveraging contextual information.
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- Runtime Neural Pruning536
A Runtime Neural Pruning (RNP) framework which prunes the deep neural network dynamically at the runtime and preserves the full ability of the original network and conducts pruning according to the input image and current feature maps adaptively.
- LRRNet: A Novel Representation Learning Guided Fusion Network for Infrared and Visible Images493
A learnable representation approach to the fusion task is adopted, in which the construction of the fusion network architecture is guided by the optimisation algorithm producing the learnable model, and an end-to-end lightweight fusion network is constructed to fuse infrared and visible light images.
- COIN: A Large-Scale Dataset for Comprehensive Instructional Video Analysis477
A simple yet effective method to capture the dependencies among different steps, which can be easily plugged into conventional proposal-based action detection methods for localizing important steps in instruction videos is proposed.
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- SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving459
A SurroundOcc method to predict the 3D occupancy with multi-camera images, which fuse multi-frame LiDAR scans of dynamic objects and static scenes separately and adopts Poisson Reconstruction to fill the holes and voxelize the mesh to get dense occupancy labels.
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- Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion438
This paper presents a new framework to formulate the trajectory prediction task as a reverse process of motion indeterminacy diffusion (MID), in which the history behavior information and the social interactions are encoded as a state embedding and a Transformer-based diffusion model is devised to capture the temporal dependencies of trajectories.
- Structure-Preserving Super Resolution With Gradient Guidance414
A structure-preserving super resolution method which exploits gradient maps of images to guide the recovery in two aspects and proposes a gradient loss which imposes a second-order restriction on the super-resolved images.
- 399
- Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification378
This paper analyzes the effect of the learned visual attention on network prediction through counterfactual intervention and maximize the effect to encourage the network to learn more useful attention for fine-grained image recognition.
- WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition375
A distributed framework is developed to train face recognition models efficiently without tampering with the performance, and comprehensive baselines are established on a rich-attribute test set under FRUITS-100ms/500ms/1000ms protocol.
- Unleashing Text-to-Image Diffusion Models for Visual Perception366
It is shown that vision-language pre-trained diffusion models can be faster adapted to downstream visual perception tasks using the proposed VPD, a new framework that exploits the semantic information of a pre-trained text-to-image diffusion model in visual perception tasks.
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- Objects are Different: Flexible Monocular 3D Object Detection349
A flexible framework for monocular 3D object detection which explicitly decouples the truncated objects and adaptively combines multiple approaches for object depth estimation, which outperforms the state-of-the-art method.
- Discriminative multi-manifold analysis for face recognition from a single training sample per person349
- OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perception321
In the OpenOccupancy benchmark, the large-scale nuScenes dataset is extended with dense semantic occupancy annotations and the Cascade Occupancy Network (CONet) is proposed to refine the coarse prediction, which relatively enhances the performance by ∼30% than the baseline.
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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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