Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileAcademic lineage
Possible advisorsa guess from early papers, not confirmed
- Suggested from co-authorship
Is this you? Claim this profile to confirm or dismiss it.
Works67 from public data
- 668
- InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model397
Experimental results demonstrate the superiority of InternLM-XComposer2 based on InternLM2-7B in producing high-quality long-text multi-modal content and its exceptional vision-language understanding performance across various benchmarks, where it not only significantly outperforms existing multimodal models but also matches or even surpasses GPT-4V and Gemini Pro in certain assessments.
- InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition361
This work proposes InternLM-XComposer, a vision-language large model that enables advanced image-text comprehension and composition that achieves competitive text-image composition scores compared to public solutions, including GPT4-V and GPT3.5.
- PyramidDrop: Accelerating Your Large Vision-Language Models via Pyramid Visual Redundancy Reduction332
This work proposes PyramidDrop, a visual redundancy reduction strategy for LVLMs to boost their efficiency in both training and inference with neglectable performance loss, and could also serve as a plug-and-play strategy for inference acceleration without training, with better performance and lower inference cost than counterparts.
- Seesaw Loss for Long-Tailed Instance Segmentation314
This work proposes Seesaw Loss to dynamically re-balance gradients of positive and negative samples for each category, with two complementary factors, i.e., mitigation factor and compensation factor, which obtains significant gains over Cross-Entropy Loss and achieves state-of-the-art performance on LVIS dataset without bells and whistles.
- Prime Sample Attention in Object Detection243
The notion of Prime Samples, those that play a key role in driving the detection performance are proposed, and a simple yet effective sampling and learning strategy called PrIme Sample Attention (PISA) is developed that directs the focus of the training process towards such samples.
- InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output216
A versatile large-vision language model that supports long-contextual input and output, InternLM-XComposer-2.5 excels in various text-image comprehension and composition applications, achieving GPT-4V level capabilities with merely 7B LLM backend.
- Side-Aware Boundary Localization for More Precise Object Detection167
This paper proposes an alternative approach, named as Side-Aware Boundary Localization (SABL), where each side of the bounding box is respectively localized with a dedicated network branch, to tackle the difficulty of precise localization in the presence of displacements with large variance.
- OVO-Bench: How Far is Your Video-LLMs from Real-World Online Video Understanding?159
OVO-Bench evaluates the ability of video LLMs to reason and respond to events occurring at specific timestamps under three distinct scenarios: Backward tracing, real-time understanding, and forward active responding.
- Dispider: Enabling Video LLMs with Active Real-Time Interaction via Disentangled Perception, Decision, and Reaction146
Experiments prove that Dispider outperforms existing methods not only in its superior understanding of video content in conventional video QA settings, but also in proactive response capability and temporal awareness under the streaming setting.
- 111
- V3Det: Vast Vocabulary Visual Detection Dataset104
V3Det, a vast vocabulary visual detection dataset with precisely annotated bounding boxes on massive images, has the potential to serve as a cornerstone dataset for developing more general visual perception systems.
- 95
- VideoRoPE: What Makes for Good Video Rotary Position Embedding?71
VideoRoPE is introduced, with a 3D structure designed to preserve spatio-temporal relationships, which consistently surpasses previous RoPE variants, across diverse downstream tasks such as long video retrieval, video understanding, and video hallucination.
- MM-IFEngine: Towards Multimodal Instruction Following49
The MMIFEngine pipeline is presented, an effective pipeline to generate high-quality image-instruction pairs and a comprehensive evaluation pipeline incorporating both rule-based assessment and judge model, and introduces MM-IFEval, a challenging and diverse multi-modal instruction-following benchmark.
- Spatial-SSRL: Enhancing Spatial Understanding via Self-Supervised Reinforcement Learning44
Spatial-SSRL is introduced, a self-supervised RL paradigm that derives verifiable signals directly from ordinary RGB or RGB-D images and provides a practical route to stronger spatial intelligence in LVLMs.
- Light-a-Video: Training-Free Video Relighting via Progressive Light Fusion44
Experiments show that Light-AVideo improves the temporal consistency of relighted video while maintaining the relighted image quality, ensuring coherent lighting transitions across frames.
- Beyond Fixed: Training-Free Variable-Length Denoising for Diffusion Large Language Models36
DAEDAL is introduced, a novel training-free denoising strategy that enables Dynamic Adaptive Length Expansion for Diffusion Large Language Models and achieves performance comparable, and in some cases superior, to meticulously tuned fixed-length baselines, while simultaneously enhancing computational efficiency by achieving a higher effective token ratio.
- 35
- MIA-DPO: Multi-Image Augmented Direct Preference Optimization For Large Vision-Language Models33
Multi-Image Augmented Direct Preference Optimization (MIA-DPO) mitigates the scarcity of diverse multi-image training data by extending single-image data with unrelated images arranged in grid collages or pic-in-pic formats, significantly reducing the costs associated with multi-image data annotations.
- 28
- 24
- BoostStep: Boosting mathematical capability of Large Language Models via improved single-step reasoning22
BoostStep is a method that enhances reasoning accuracy through step-aligned ICL, a novel mechanism that carefully aligns retrieved reference steps with the corresponding reasoning steps, and enhances state-of-the-art LLMs to solve challenging math problems using simpler examples.
- Deciphering Cross-Modal Alignment in Large Vision-Language Models with Modality Integration Rate22
This paper presents the Modality Integration Rate (MIR), an effective, robust, and generalized metric to indicate the multi-modal pre-training quality of Large Vision Language Models (LVLMs), and proposes evaluating the pre-training quality from the inter-modal distribution distance perspective.
- DualFocus: Integrating Macro and Micro Perspectives in Multi-modal Large Language Models17
DualFocus's superiority in balancing detailed examination with holistic insight is demonstrated, significantly reducing hallucination instances in MLLMs and improving their performance in various vision-language tasks.
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 2026-10-16. 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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.