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Works100 from public data
- OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models539
An Omnidirectionally calibrated Quantization technique for LLMs is introduced, which achieves good performance in diverse quantization settings while maintaining the computational efficiency of PTQ by efficiently optimizing various quantization parameters.
- SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models313
SPHINX, a versatile multi-modal large language model (MLLM) with a joint mixing of model weights, tuning tasks, and visual embeddings is presented, and an efficient strategy aiming to better capture fine-grained appearances of high-resolution images is proposed.
- LVLM-EHub: A Comprehensive Evaluation Benchmark for Large Vision-Language Models296
A comprehensive evaluation of publicly available large multimodal models by building an LVLM evaluation Hub (LVLM-eHub), which consists of 13 representative LVLMs such as InstructBLIP and LLaVA, which are thoroughly evaluated by a quantitative capability evaluation and an online arena platform.
- OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLM283
OmniMedVQA is introduced, a novel comprehensive medical Visual Question Answering (VQA) benchmark collected from 73 different medical datasets, including 12 different modalities and covering more than 20 distinct anatomical regions, to reveal the current limitations of LVLM in understanding real medical images and highlight the dataset's significance.
- MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI217
This study presents MMT-Bench, a comprehensive benchmark designed to assess LVLMs across massive multimodal tasks requiring expert knowledge and deliberate visual recognition, localization, reasoning, and planning, and hopes that MMT-Bench will inspire the community to develop next-generation multimodal foundation models aimed at achieving general-purpose multimodal intelligence.
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- Towards World Simulator: Crafting Physical Commonsense-Based Benchmark for Video Generation190
The evaluation results and in-depth analysis demonstrate that current models struggle to generate videos that comply with physical commonsense, and it is hoped this study will inspire the community to prioritize the learning of physical commonsense in these models beyond entertainment applications.
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- SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Models162
The SPHINX framework is modified by removing redundant visual encoders, bypassing fully-padded sub-images with skip tokens, and simplifying multi-stage training into a one-stage all-in-one paradigm to improve the architecture and training efficiency.
- Lumina-T2X: Transforming Text into Any Modality, Resolution, and Duration via Flow-based Large Diffusion Transformers147
This technical report introduces the Lumina-T2X family - a series of Flow-based Large Diffusion Transformers (Flag-DiT) equipped with zero-initialized attention, as a unified framework designed to transform noise into images, videos, multi-view 3D objects, and audio clips conditioned on text instructions.
- DiffRate : Differentiable Compression Rate for Efficient Vision Transformers110
Differentiable Compression Rate (DiffRate), a novel token compression method that has several appealing properties prior arts do not have, is proposed and achieves state-of-the-art performance.
- Beyond One-to-One: Rethinking the Referring Image Segmentation108
A Dual Multi-Modal Interaction (DMMI) Network is proposed, which contains two decoder branches and enables information flow in two directions and achieves state-of-the-art performance on different datasets, and the Ref-ZOM-trained model performs well on various types of text inputs.
- ChartAssisstant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction Tuning99
ChartAssistant, a chart-based vision-language model for universal chart comprehension and reasoning, is proposed, which leverages ChartSFT, a comprehensive dataset covering diverse chart-related tasks with basic and specialized chart types.
- MMIU: Multimodal Multi-image Understanding for Evaluating Large Vision-Language Models71
The Multimodal Multi-image Understanding (MMIU) benchmark is introduced, a comprehensive evaluation suite designed to assess LVLMs across a wide range of multi-image tasks, making it the most extensive benchmark of its kind.
- 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.
- Tree-Planner: Efficient Close-loop Task Planning with Large Language Models66
Tree-Planner is proposed, which reframes task planning with Large Language Models into three distinct phases: plan sampling, action tree construction, and grounded deciding, which achieves state-of-the-art performance while maintaining high efficiency.
- BESA: Pruning Large Language Models with Blockwise Parameter-Efficient Sparsity Allocation62
A novel LLM pruning technique dubbed blockwise parameter-efficient sparsity allocation (BESA) by applying a blockwise reconstruction loss, characterized by two distinctive attributes: i) it targets the overall pruning error with respect to individual transformer blocks, and ii) it allocates layer-specific sparsity in a differentiable manner, both of which ensure reduced performance degradation after pruning.
- SSN: Learning Sparse Switchable Normalization via SparsestMax57
Sparse switchable normalization (SSN) is presented where the importance ratios are constrained to be sparse, and SparsestMax is a sparse version of softmax, which is guaranteed to select only one normalizer for each normalization layer, avoiding redundant computations and improving interpretability of normalizer selection.
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- ZipVL: Efficient Large Vision-Language Models with Dynamic Token Sparsification53
ZipVL is presented, an efficient inference framework designed for LVLMs through a dynamic ratio allocation strategy of important tokens that improves efficiency for less complex tasks while maintaining high performance for more challenging ones.
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- B-AVIBench: Toward Evaluating the Robustness of Large Vision-Language Model on Black-Box Adversarial Visual-Instructions40
B-AVIBench is introduced, a framework designed to analyze the robustness of LVLMs when facing various Black-box Adversarial Visual-Instructions (B-AVIs), including four types of image-based B-AVIs, ten types of text-based B-AVIs, and nine types of content bias B-AVIs (such as gender, violence, cultural, and racial biases, among others).
- Differentiable Learning-to-Group Channels via Groupable Convolutional Neural Networks40
This work presents Groupable ConvNet (GroupNet) built by using a novel dynamic grouping convolution (DGConv) operation, which is able to learn the number of groups in an end-to-end manner and outperforms its counterparts such as ResNet and ResNeXt in terms of accuracy and computational complexity.
- RoboScript: Code Generation for Free-Form Manipulation Tasks across Real and Simulation37
The RobotScript platform addresses the gap in deployability of generated code on real robots, by emphasizing the unified interface with both simulation and real robots, based on abstraction from the Robot Operating System (ROS), ensuring syntax compliance and simulation validation with Gazebo.
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 2026-10-12. 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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