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Claim this profileWorks130 from public data
- Voxel Transformer for 3D Object Detection574
Voxel Transformer is presented, a novel and effective voxel-based Transformer backbone for 3D object detection from point clouds that shows consistent improvement over the convolutional baselines while maintaining computational efficiency on the KITTI dataset and the Waymo Open dataset.
- DetCo: Unsupervised Contrastive Learning for Object Detection379
Extensive experiments demonstrate that DetCo not only outperforms recent methods on a series of 2D and 3D instance-level detection tasks, but also competitive on image classification.
- A Survey on Video Diffusion Models318
This article presents a comprehensive review of video diffusion models in the AIGC era, and presents an overview of research on diffusion models in the video domain, categorizing the work into three key areas: video generation, video editing, and other video understanding tasks.
- CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending251
A novel lane-sensitive architecture search framework named CurveLane-NAS to automatically capture both long-ranged coherent and accurate short-range curve information while unifying both architecture search and post-processing on curve lane predictions via point blending.
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- Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection187
This work presents a flexible and high-performance framework, named Pyramid R-CNN, for two-stage 3D object detection from point clouds, which outperforms the state-of-the-art 3D detection models by a large margin on both the KITTI dataset and the Waymo Open dataset.
- NavCoT: Boosting LLM-Based Vision-and-Language Navigation via Learning Disentangled Reasoning145
Experimental results across various training settings and popular VLN benchmarks show the significant superiority of NavCoT over the direct action prediction variants, and through simple parameter-efficient finetuning, this NavCoT outperforms a recent GPT4-based approach with 7% relative improvement on the R2R dataset.
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- DetCLIPv2: Scalable Open-Vocabulary Object Detection Pre-training via Word-Region Alignment137
An efficient and scalable training framework that incorporates large-scale imagetext pairs to achieve open-vocabulary object detection (OVD) and is trained with a hybrid supervision from detection, grounding and image-text pair data under a unified data formulation.
- Holistic Autonomous Driving Understanding by Bird'View Injected Multi-Modal Large Models128
This work proposes a novel SQL-based method to generate instruction-response pairs automatically, which is inspired by the driving logical progression of humans, and presents BEV-InMLLM, an end-to-end method for efficiently deriving instruction-aware Bird's-Eye-View (BEV) features, language-aligned for large language models.
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- CLIP2: Contrastive Language-Image-Point Pretraining from Real-World Point Cloud Data116
This work proposes Contrastive Language-Image-Point Cloud Pretraining (CLIP2) to directly learn the transferable 3D point cloud representation in realistic scenarios with a novel proxy alignment mechanism, and exploits naturally-existed correspondences in 2D and 3D scenarios.
- Driver Anomaly Detection: A Dataset and Contrastive Learning Approach114
This work proposes a contrastive learning approach to learn a metric to differentiate normal driving from anomalous driving and introduces a new video-based benchmark, the Driver Anomaly Detection (DAD) dataset, which contains normal driving videos together with a set of anomalous actions in its training set.
- Segmenting Transparent Objects in the Wild with Transformer109
A novel Transformer-based segmentation pipeline termed Trans2Seg is proposed, which significantly outperforms all the CNN-based methods, showing the proposed algorithm's potential ability to solve transparent object segmentation.
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- ONCE-3DLanes: Building Monocular 3D Lane Detection98
This work presents ONCE-3DLanes, a real-world autonomous driving dataset with lane layout annotation in 3D space, and presents an extrinsic-free, anchorfree method, called SALAD, regressing the 3D coordinates of lanes in image view without converting the feature map into the bird's-eye view (BEV).
- TransNAS-Bench-101: Improving transferability and Generalizability of Cross-Task Neural Architecture Search98
This work proposes TransNAS-Bench-101, a benchmark dataset containing network performance across seven tasks, covering classification, regression, pixel-level prediction, and self-supervised tasks, and explores two fundamentally different types of search space: cell-level search space and macro- level search space.
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- Product1M: Towards Weakly Supervised Instance-Level Product Retrieval via Cross-Modal Pretraining91
A novel model named Cross-modal contrAstive Product Transformer for instance-level prodUct REtrieval (CAPTURE) is proposed, that excels in capturing the potential synergy between multi- modal inputs via a hybrid-stream transformer in a self-supervised manner.
- SM-NAS: Structural-to-Modular Neural Architecture Search for Object Detection90
This paper presents a two-stage coarse-to-fine searching strategy named Structural- to-Modular NAS (SM-NAS) for searching a GPU-friendly design of both an efficient combination of modules and better modular-level architecture for object detection.
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- Effective Sparsification of Neural Networks with Global Sparsity Constraint81
ProbMask is proposed, which solves a natural sparsification formulation under global sparsity constraint and can outperform previous state-of-the-art methods by a significant margin, especially in the high pruning rate situation.
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 2026-10-13. 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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