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Claim this profileWorks218 from public data
- Planning-oriented Autonomous Driving1,690
This work introduces Unified Autonomous Driving (UniAD), a comprehensive framework up-to-date that incorporates full-stack driving tasks in one network and is exquisitely devised to leverage advantages of each module, and provide complementary feature abstractions for agent interaction from a global perspective.
- Vision Transformer Adapter for Dense Predictions938
The ViT-Adapter is proposed, which allows plain ViT to achieve comparable performance to vision-specific transformers and facilitate future research and is verified on multiple dense prediction tasks, including object detection, instance segmentation, and semantic segmentation.
- Shape Robust Text Detection With Progressive Scale Expansion Network860
A novel Progressive Scale Expansion Network (PSENet) is proposed, which can precisely detect text instances with arbitrary shapes and is effective to split the close text instances, making it easier to use segmentation-based methods to detect arbitrary-shaped text instances.
- Efficient and Accurate Arbitrary-Shaped Text Detection With Pixel Aggregation Network632
This paper proposes an efficient and accurate arbitrary-shaped text detector, termed Pixel Aggregation Network (PAN), which is equipped with a low computational-cost segmentation head and a learnable post-processing.
- EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought459
This work introduces EmbodiedGPT, an end-to-end multi-modal foundation model for embodied AI, empowering embodied agents with multi- modal understanding and execution capabilities, and significantly enhances the success rate of the embodied control task by extracting more effective features.
- Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization270
This work designs an automated preference data construction pipeline to create MMPR, a high-quality, large-scale multimodal reasoning preference dataset and explores integrating PO with MLLMs, developing a simple yet effective method, termed Mixed Preference Optimization (MPO), which boosts multimodal CoT performance.
- Segmenting Transparent Objects in the Wild256
A novel boundary-aware segmentation method, termed TransLab, is proposed, which exploits boundary as the clue to improve segmentation of transparent objects and significantly outperforms 20 recent object segmentation methods based on deep learning.
- Scene Text Image Super-Resolution in the Wild186
A new Text Super-Resolution Network termed TSRN, with three novel modules is developed, which improves the recognition accuracy by over 13% of CRNN, and by nearly 9.0% of ASTER and MORAN compared to synthetic SR data.
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- A novel intrusion detection system based on an optimal hybrid kernel extreme learning machine170
A novel accurate and effective misuse intrusion detection system that relies on specific attack signatures to distinguish between normal and malicious activities is presented to detect various attacks based on an extreme learning machine with a hybrid kernel function (HKELM).
- Vision-RWKV: Efficient and Scalable Visual Perception with RWKV-Like Architectures166
Vision-RWKV (VRWKV), a model adapted from the RWKV model used in the NLP field with necessary modifications for vision tasks, surpasses ViT's performance in image classification and has significantly faster speeds and lower memory usage processing high-resolution inputs.
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- The All-Seeing Project: Towards Panoptic Visual Recognition and Understanding of the Open World133
The All-Seeing model (ASM), a unified framework for panoptic visual recognition and understanding, is developed with open-ended language prompts and locations, which allows it to generalize to various vision and language tasks with remarkable zero-shot performance.
- InternGPT: Solving Vision-Centric Tasks by Interacting with ChatGPT Beyond Language113
By incorporating pointing instructions, the proposed iGPT significantly improves the efficiency of communication between users and chatbots, as well as the accuracy of chatbots in vision-centric tasks, especially in complicated visual scenarios where the number of objects is greater than 2.
- Performance assessment of extensive green roof runoff flow and quality control capacity based on pilot experiments101
The runoff quantity and quality control capacity of five types of extensive green roofs (EGRs) were analyzed in Beijing for 51 nature rainfall events and 6 simulated events from July 2017 to October 2018 and showed that as the module scale and substrate thickness increased, the retention capacity of the EGRs increased.
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- MM-Interleaved: Interleaved Image-Text Generative Modeling via Multi-modal Feature Synchronizer83
MM-Interleaved is an end-to-end generative model for interleaved image-text data that introduces a multi-scale and multi-image feature synchronizer module, allowing direct access to fine-grained image features in the previous context during the generation process.
- Research advances in traditional Chinese medicine syndromes in cancer patients82
The authors reviewed the presentations of TCM syndromes in seven common malignancies, the objectivity and the standardization of TCm syndrome differentiation, the evaluation ofTCM syndrome modeling in cancer research, and syndrome differentiation-guided TCM treatment of cancers.
- Uni-Perceiver v2: A Generalist Model for Large-Scale Vision and Vision-Language Tasks75
This paper proposes Uni-Perceiver v2, which is the first generalist model capable of handling major large-scale vision and vision-language tasks with competitive performance and proposes an effective optimization technique named Task-Balanced Gradient Normalization to ensure stable multi-task learning with an unmixed sampling strategy, helpful for tasks requiring large batch-size training.
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- A Survey of Reasoning with Foundation Models73
This paper introduces seminal foundation models proposed or adaptable for reasoning, highlighting the latest advancements in various reasoning tasks, methods, and benchmarks and discusses the relevance of multimodal learning, autonomous agents, and super alignment in the context of reasoning.
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- Causal augmented ConvNet: A temporal memory dilated convolution model for long-sequence time series prediction68
A causal augmented convolution network (CaConvNet) and its application for long-sequence time series prediction and a comprehensive ablation study is performed to provide insights into the contribution of each sub-structure of the CaConv net model to the observed performance.
- MMBench-GUI: Hierarchical Multi-Platform Evaluation Framework for GUI Agents66
This work introduces MMBench-GUI, a hierarchical benchmark for evaluating GUI automation agents across Windows, macOS, Linux, iOS, Android, and Web platforms, and proposes a novel Efficiency-Quality Area (EQA) metric to assess GUI agent execution efficiency in online automation scenarios.
- RoboCodeX: Multimodal Code Generation for Robotic Behavior Synthesis66
A tree-structured multimodal code generation framework for generalized robotic behavior synthesis, termed RoboCodeX, which decomposes high-level human instructions into multiple object-centric manipulation units consisting of physical preferences such as affordance and safety constraints, and applies code generation to introduce generalization ability across various robotics platforms.
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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