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- VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual Recognition109
A visual-linguistic long-tailed recognition framework that can not only learn visual representation from images but also learn corresponding linguistic representation from noisy class-level text descriptions collected from the Internet, and is close to the prevailing performance training on the full ImageNet.
- MM-Interleaved: Interleaved Image-Text Generative Modeling via Multi-modal Feature Synchronizer83
MM-Interleaved is an end-to-end generative model for interleaved image-text data that introduces a multi-scale and multi-image feature synchronizer module, allowing direct access to fine-grained image features in the previous context during the generation process.
- 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.
- SynerGen-VL: Towards Synergistic Image Understanding and Generation with Vision Experts and Token Folding49
To address challenges identified in existing encoder-free unified MLLMs, the token folding mechanism and the vision-expert-based progressive alignment pretraining strategy are introduced, which effectively support high-resolution image understanding while reducing training complexity.
- ADDP: Learning General Representations for Image Recognition and Generation with Alternating Denoising Diffusion Process22
This method represents the first successful development of general representations applicable to both generation and dense recognition tasks and can be used to generate diverse high-fidelity images and also demonstrate excellent transfer performance on recognition tasks.
- Learning Adaptive and Temporally Causal Video Tokenization in a 1D Latent Space16
AdapTok is equipped with a block-wise masking strategy that randomly drops tail tokens of each block during training, and a block causal scorer to predict the reconstruction quality of video frames using different numbers of tokens.
- Parameter-Inverted Image Pyramid Networks for Visual Perception and Multimodal Understanding11
This work proposes a novel network architecture, called Parameter-Inverted Image Pyramid Networks (PIIP), which uses pretrained models (ViTs or CNNs) as branches to process multi-scale images, where images of higher resolutions are processed by smaller network branches to balance computational cost and performance.
- LangBridge: Interpreting Image as a Combination of Language Embeddings11
LangBridge is a novel adapter that explicitly maps visual tokens to linear combinations of LLM vocabulary embeddings to enable interpretable vision-language alignment by grounding visual representations in LLM vocab embedding, while its plug-and-play design ensures efficient reuse across multiple LLMs with nearly no performance degradation.
- Trust Your Critic: Robust Reward Modeling and Reinforcement Learning for Faithful Image Editing and Generation8
Firm-reward.io presents FIRM (Faithful Image Reward Modeling), a comprehensive framework that develops robust reward models to provide accurate and reliable guidance for faithful image generation and editing and achieves substantial performance breakthroughs.
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Publication data from OpenAlex; 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.
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