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- InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models1,936
InternVL3 incorporates variable visual position encoding (V2PE) to support extended multimodal contexts, employs advanced post-training techniques such as supervised fine-tuning (SFT) and mixed preference optimization (MPO), and adopts test-time scaling strategies alongside an optimized training infrastructure.
- Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling1,813
This work explores the relationship between model scaling and performance, systematically exploring the performance trends in vision encoders, language models, dataset sizes, and test-time configurations and hopes this model contributes to the open-source community by setting new standards for developing and applying multimodal AI systems.
- UniViLM: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation.410
Experimental results demonstrate that the UniVL can learn strong video-text representation and achieves state-of-the-art results on five downstream tasks.
- Drive Like a Human: Rethinking Autonomous Driving with Large Language Models301
This paper explores the potential of using a large language model (LLM) to understand the driving environment in a human-like manner and analyze its ability to reason, interpret, and memorize when facing complex scenarios and demonstrates the feasibility of employing an LLM in driving scenarios.
- DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models298
The DiLu framework is proposed, which combines a Reasoning and a Reflection module to enable the system to perform decision-making based on common-sense knowledge and evolve continuously and is the first to leverage knowledge-driven capability in decision-making for autonomous vehicles.
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- LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross- Modal Fusion202
The novel Local-to-Global fusion network (LoGoNet), which performs LiDAR-camerafusion at both local and global levels, and is ranked 1st on Waymo 3D object detection leaderboard and obtains 81.02 mAPH (L2) detection performance.
- OmniDocBench: Benchmarking Diverse PDF Document Parsing with Comprehensive Annotations182
This work introduces OmniDocBench, a novel benchmark featuring high-quality annotations across nine document sources, including academic papers, textbooks, and more challenging cases such as handwritten notes and densely typeset newspapers, and sets a new standard for the fair, diverse, and fine-grained evaluation in document parsing.
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- Knowledge Aware Semantic Concept Expansion for Image-Text Matching82
This paper develops a Scene Concept Graph (SCG) by aggregating image scene graphs and extracting frequently co-occurred concept pairs as scene common-sense knowledge and proposes a novel model to incorporate this knowledge to improve image-text matching.
- Dense Procedure Captioning in Narrated Instructional Videos68
A framework to extract procedures by a cross-modality module, which fuses video content with the entire transcript; and generate captions by encoding video frames as well as a snippet of transcripts within each extracted procedure is introduced.
- OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text60
This paper introduces OmniCorpus, a 10 billion-scale image-text interleaved dataset that has 15 times larger scales while maintaining good data quality and is more flexible, easily degradable from an image-text interleaved format to pure text corpus and image-text pairs.
- Uni3D: A Unified Baseline for Multi-Dataset 3D Object Detection55
A Uni3D is presented which leverages a simple data-level correction operation and a designed semantic-level coupling-and-recoupling module to alleviate the unavoidable data- level and taxonomy-level differences, respectively.
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- LimSim++: A Closed-Loop Platform for Deploying Multimodal LLMs in Autonomous Driving52
LimSim++, an extended version of LimSim designed for the application of (M)LLMs in autonomous driving, is introduced, addressing the need for a long-term closed-loop infrastructure supporting continuous learning and improved generalization in autonomous driving.
- Learning Cross-Image Object Semantic Relation in Transformer for Few-Shot Fine-Grained Image Classification52
Extensive experiments demonstrate that HelixFormer can effectively enhance the cross-image object semantic relation matching for recognizing fine-grained objects, achieving much better performance over most state-of-the-art methods under 1-shot and 5-shot scenarios.
- DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point Clouds51
This work proposes a novel paradigm of offboard 3D object detection, named DetZero, which combines an offline tracker coupled with a multi-frame detector to focus on the completeness of generated object tracks and an attention-mechanism refining module to strengthen contextual information interaction across long-term sequential point clouds for object refining with decomposed regression methods.
- Microsoft Concept Graph: Mining Semantic Concepts for Short Text Understanding50
This paper introduces Microsoft Concept Graph, a knowledge graph engine that provides concept tagging APIs to facilitate the understanding of human languages and uses conceptualization models to represent text in the concept space to empower text-related applications.
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- Bi3D: Bi-Domain Active Learning for Cross-Domain 3D Object Detection41
This work proposes a Bi-domain active learning approach, namely Bi3D, to solve the cross-domain 3D object detection task, and develops a domainness-aware source sampling strategy, which identifies target-domain-like samples from the source domain to avoid the model being interfered by irrelevant source data.
- Docopilot: Improving Multimodal Models for Document-Level Understanding38
This work presents a high-quality document-level dataset, Doc-750K, designed to support in-depth understanding of multimodal documents, and develops a native multimodal model, Docopilot, which can accurately handle document-level dependencies without relying on RAG.
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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 2026-10-13. 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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