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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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- A Benchmark for Structured Procedural Knowledge Extraction from Cooking Videos22
The analysis shows that the proposed task is challenging and standard modeling approaches like unsupervised segmentation, semantic role labeling, and visual action detection perform poorly when forced to predict every action of a procedure in a structured form.
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- ReSimAD: Zero-Shot 3D Domain Transfer for Autonomous Driving with Source Reconstruction and Target Simulation17
Results demonstrate that the proposed Reconstruction-Simulation-Perception (ReSimAD) scheme is beneficial to boost the domain generalization ability, even promising for 3D pre-training.
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- Chimera: Improving Generalist Model with Domain-Specific Experts11
Chimera, a scalable and low-cost multi-modal pipeline designed to boost the ability of existing LMMs with domain-specific experts with a novel Generalist-Specialist Collaboration Masking mechanism, results in a versatile model that excels across the chart, table, math, and document domains.
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- Hashing based Efficient Inference for Image-Text Matching10
A novel hashing based efficient inference module called HEI is proposed, which can be plugged into the existing framework to speed up the inference step without reducing the retrieval performance.
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- SPOT: Scalable 3D Pre-Training via Occupancy Prediction for Learning Transferable 3D Representations7
SPOT, namely Scalable Pre-training via Occupancy prediction for learning Transferable 3D representations under such a label-efficient fine-tuning paradigm, achieves effectiveness on various public datasets with different downstream tasks, showcasing its general representation power, cross-domain robustness and data scalability.
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- Vision-Centric 4D Occupancy Forecasting and Planning via Implicit Residual World Models6
IR-WM, an Implicit Residual World Model that focuses on modeling the current state and evolution of the world, establishes a robust bird’s-eye-view representation of the current state from the visual observation and applies an alignment module to calibrate semantic and dynamic misalignments.
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- Functionality Discovery and Prediction of Physical Objects2
This paper mine object-functionality knowledge through pattern-based and model-based methods from text, introduces a novel task on physical object- functionality prediction, and proposes a method to leverage the mined functionality knowledge for the new task.
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- The Agent’s First Day: Benchmarking Learning, Exploration, and Scheduling in the Workplace Scenarios–
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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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