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- MOTR: End-to-End Multiple-Object Tracking with Transformer906
MOTR is proposed, which extends DETR and introduces track query to model the tracked instances in the entire video to enhance temporal relation modeling and serve as a stronger baseline for future research on temporal modeling and Transformer-based trackers.
- PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images579
PETRv2, a unified framework for 3D perception from multi-view images, is proposed and the 3D position embedding in PETR is extended for temporal modeling, which achieves the temporal alignment on object position of different frames.
- Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detection482
StreamPETR is the first online multi-view method that achieves comparable performance with lidar-based methods and is the first online multi-view method that achieves comparable performance with lidar-based methods on the standard nuScenes benchmark.
- MOTRv2: Bootstrapping End-to-End Multi-Object Tracking by Pretrained Object Detectors335
This paper first adopt the anchor formulation of queries and then use an extra object detector to generate proposals as anchors, providing detection prior to MOTR, a simple yet effective pipeline to bootstrap end-to-end multi-object tracking with a pretrained object detector.
- Learning Human-Object Interaction Detection Using Interaction Points256
This paper proposes a novel fully-convolutional approach that directly detects the interactions between human-object pairs and predicts interaction points, which directly localize and classify the inter-action.
- Panacea: Panoramic and Controllable Video Generation for Autonomous Driving209
This work proposes Panacea, an innovative approach to generate panoramic and controllable videos in driving scenarios, capable of yielding an unlimited numbers of diverse, annotated samples pivotal for autonomous driving advancements, by effectively augmenting the training dataset used for advanced BEV perception techniques.
- Cross Modal Transformer: Towards Fast and Robust 3D Object Detection193
A robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection that achieves 74.1% NDS on nuScenes test set while maintaining faster inference speed.
- Referring Multi-Object Tracking156
This paper proposes a new and general referring understanding task, termed referring multi-object tracking (RMOT), whose core idea is to employ a language expression as a semantic cue to guide the prediction of multi- object tracking.
- Deep Contextual Attention for Human-Object Interaction Detection138
This work proposes a contextual attention framework for human-object interaction detection that leverages context by learning contextually-aware appearance features for human and object instances and adaptively selects relevant instance-centric context information to highlight image regions likely to contain human- object interactions.
- ADriver-I: A General World Model for Autonomous Driving127
This paper constructs a general world model based on MLLM and diffusion model for autonomous driving, termed ADriver-I, which takes the vision-action pairs as inputs and autoregressively predicts the control signal of the current frame.
- UniScene: Unified Occupancy-centric Driving Scene Generation125
UniScene is introduced, the first unified framework for generating three key data forms — semantic occupancy, video, and LiDAR — in driving scenes in driving scenes, and outperforms previous SOTAs in the occupancy, video, and LiDAR generation.
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- Tree Energy Loss: Towards Sparsely Annotated Semantic Segmentation109
A novel tree energy loss for SASS is proposed by providing semantic guidance for unlabeled pixels by sequentially applying these affinities to the net-work prediction, achieving dynamic online self-training.
- OnlineRefer: A Simple Online Baseline for Referring Video Object Segmentation100
This work breaks up the previous offline belief and proposes a simple yet effective online model using explicit query propagation, named OnlineRefer, that leverages target cues that gather semantic information and position prior to improve the accuracy and ease of referring predictions for the current frame.
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- RoboGSim: A Real2Sim2Real Robotic Gaussian Splatting Simulator82
The experimental results show that the RoboGSim data model can achieve zero-shot performance on the real robot, with results comparable to real robot data, and the real2sim and sim2real transfer experiments show a high consistency in the texture and physics.
- Forest R-CNN: Large-Vocabulary Long-Tailed Object Detection and Instance Segmentation80
Prior knowledge of the relations among object categories is exploited to cluster fine-grained classes into coarser parent classes, and construct a classification tree that is responsible for parsing an object instance into a fine- grained category via its parent class.
- GeoVLA: Empowering 3D Representations in Vision-Language-Action Models76
GeoVLA is a novel VLA framework that effectively integrates 3D information to advance robotic manipulation and achieves state-of-the-art results in the LIBERO and ManiSkill2 simulation benchmarks and shows remarkable robustness in real-world tasks requiring height adaptability, scale awareness and viewpoint invariance.
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- MOTRv3: Release-Fetch Supervision for End-to-End Multi-Object Tracking75
This work proposes MOTRv3, which balances the label assignment process using the developed release-fetch supervision strategy, and achieves impressive performance across diverse benchmarks, e.g., MOT17, DanceTrack.
- Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection63
This paper proposes a simple but effective mechanism, called Co-mining, for sparsely annotated object detection, where two branches of a siamese network predict the pseudo-label sets for each other in order to enhance multi-view learning and better mine unlabeled instances.
- Reconstructive Visual Instruction Tuning60
Recon reconstructive visual instruction tuning (ROSS), a family of Large Multimodal Models (LMMs) that exploit vision-centric supervision signals, delivers competitive performance with a single SigLIP visual encoder, demonstrating the efficacy of the vision-centric supervision tailored for visual outputs.
- TopoMLP: A Simple yet Strong Pipeline for Driving Topology Reasoning58
This work introduces a powerful 3D lane detector and an improved 2D traffic element detector to extend the upper limit of topology performance and proposes TopoMLP, a simple yet high-performance pipeline for driving topology reasoning.
- Neural networks for power management optimal strategy in hybrid microgrid58
A new method by radial basis function neural network is proposed to predict the power values of renewable energy and load demand, which are used as the input values in the optimal process.
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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