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Works96 from public data
- 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.
- VLMEvalKit: An Open-Source ToolKit for Evaluating Large Multi-Modality Models628
The VLMEvalKit toolkit aims to provide a user-friendly and comprehensive framework for researchers and developers to evaluate existing multi-modality models and publish reproducible evaluation results.
- Visual-RFT: Visual Reinforcement Fine-Tuning575
This work introduces Visual Reinforcement Fine-Tuning (Visual-RFT), which further extends the application areas of RFT on visual tasks and represents a paradigm shift in fine-tuning LVLMs, offering a data-efficient, rewarddriven approach that enhances reasoning and adaptability for domain-specific tasks.
- InternLM2 Technical Report440
InternLM2 is introduced, an open-source LLM that outperforms its predecessors in comprehensive evaluations across 6 dimensions and 30 benchmarks, long-context modeling, and open-ended subjective evaluations through innovative pre-training and optimization techniques.
- 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.
- Open-Vocabulary DETR with Conditional Matching336
This work proposes a novel open-vocabulary detector based on DETR -- hence the name OV-DETR, which, once trained, can detect any object given its class name or an exemplar image and achieves non-trivial improvements over current state of the arts.
- 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.
- Scene Text Detection with Supervised Pyramid Context Network238
A supervised pyramid context network (SPCNET) is proposed to precisely locate text regions while suppressing false positives in scene text detection, based on Feature Pyramid Network (FPN) and instance segmentation.
- Alpha-CLIP: A CLIP Model Focusing on Wherever you Want223
Alpha-CLIp, an enhanced version of CLIP with an auxiliary alpha channel to suggest attentive regions and fine-tuned with constructed millions of RGBA region-text pairs, is introduced, which not only preserves the visual recognition ability of CLIP but also enables precise control over the emphasis of image contents.
- 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.
- Unified Vision and Language Prompt Learning213
A systematic study on two representative prompt tuning methods, namely text prompt tuning and visual prompt tuning, and proposes a simple approach called Unified Prompt Tuning (UPT), which essentially learns a tiny neural network to jointly optimize prompts across different modalities.
- Contextual Object Detection with Multimodal Large Language Models182
This work presents ContextDET, a unified multimodal model that is capable of end-to-end differentiable modeling of visual-language contexts, so as to locate, identify, and associate visual objects with language inputs for human-AI interaction.
- 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.
- FASA: Feature Augmentation and Sampling Adaptation for Long-Tailed Instance Segmentation154
This work proposes a simple yet effective method, Feature Augmentation and Sampling Adaptation (FASA), that addresses the data scarcity issue by augmenting the feature space especially for rare classes by eliminating the need for inter-class transfer learning.
- 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.
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- MVSGaussian: Fast Generalizable Gaussian Splatting Reconstruction from Multi-View Stereo125
A new generalizable 3D Gaussian representation approach derived from Multi-View Stereo that can efficiently reconstruct unseen scenes with convincing generalizability, real-time rendering speed, and fast per-scene optimization.
- MotionClone: Training-Free Motion Cloning for Controllable Video Generation124
MotionClone is proposed, a training-free framework that enables motion cloning from reference videos to versatile motion-controlled video generation, including text-to-video and image-to-video, with notable superiority in terms of motion fidelity, textual alignment, and temporal consistency.
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- Pref-GRPO: Pairwise Preference Reward-based GRPO for Stable Text-to-Image Reinforcement Learning105
This work proposes Pref-GRPO, a pairwise preference reward-based GRPO method that shifts the optimization objective from score maximization to preference fitting, ensuring more stable training and mitigating reward hacking.
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- Visionary-R1: Mitigating Shortcuts in Visual Reasoning with Reinforcement Learning94
This paper aims to train visual language models to perform reasoning on image data through reinforcement learning and visual question-answer pairs, without any explicit chain-of-thought (CoT) supervision, and trains the model to adhere to a caption-reason-answer output format.
- 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.
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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 2026-10-15. 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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