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- Nenghai YuSuggested from co-authorship
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Works49 from public data
- CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows1,458
The Cross-Shaped Window self-attention mechanism for computing self-Attention in the horizontal and vertical stripes in parallel that form a cross-shaped window is developed, with each stripe obtained by splitting the input feature into stripes of equal width.
- Mobile-Former: Bridging MobileNet and Transformer727
The proposed light-weight cross attention to model the bridge enables bidirectional fusion of local and global features and builds an efficient end-to-end detector by replacing backbone, encoder and decoder in DETR with Mobile-Former, which outperforms DETR but saves 52% of computational cost and 36% of parameters.
- 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.
- Open-Sora Plan: Open-Source Large Video Generation Model325
This work introduces Open-Sora Plan, an open-source project that aims to contribute a large generation model for generating desired high-resolution videos with long durations based on various user inputs, and hopes it can inspire the video generation research community.
- PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers289
A self-supervised transformer model for deep feature extraction and it is shown that it works well for calculating perceptual similarity, and such learned visual tokens indeed exhibit better semantic meanings, and help pre-training achieve superior transfer performance in various downstream tasks.
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- 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.
- 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.
- LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of Point Cloud Based Deep Networks130
The proposed LG-GAN can support flexible targeted attack on the fly while guaranteeing good attack performance and higher efficiency simultaneously, and is the first generation based 3D point cloud attack method.
- Shape-invariant 3D Adversarial Point Clouds120
A novel Point-Cloud Sensitivity Map is proposed to boost both the efficiency and imperceptibility of point perturbations and reveals the vulnerability of point cloud recognition models when encountering shape-invariant adversarial noises.
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- Local Geometric Distortions Resilient Watermarking Scheme Based on Symmetry85
Experimental results demonstrate the excellent robustness of the proposed watermarking scheme to local geometric distortion, global geometric distortions, common image processing operations, and some kinds of combined attacks.
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- 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.
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- Maximum Entropy Reinforcement Learning with Diffusion Policy41
The diffusion model is employed, a powerful generative model capable of capturing complex multimodal distributions, as the policy representation to fulfill the MaxEnt RL objective, developing a method named MaxEnt RL with Diffusion Policy (MaxEntDP), which enables efficient exploration and brings the policy closer to the optimal MaxEnt policy.
- 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.
- Once a MAN: Towards Multi-Target Attack via Learning Multi-Target Adversarial Network Once33
This paper proposes the first Multi-target Adversarial Network (MAN), which can generate multi-target adversarial samples with a single model and can achieve better classification accuracy than other methods when attacked by various methods.
- RAR: Retrieving and Ranking Augmented MLLMs for Visual Recognition31
RAR, a Retrieving And Ranking augmented method for Multimodal Large Language Models, demonstrates a significant improvement in performance on 5 fine-grained visual recognition benchmarks, 11 few-shot image recognition datasets, and the 2 object detection datasets under the zero-shot recognition setting.
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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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