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- Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators896
This paper proposes a low-cost approach (without any training or optimization) by leveraging the power of existing text-to-image synthesis methods, making them suitable for the video domain, and introduces a new task, zero-shot text-to-video generation.
- OneFormer: One Transformer to Rule Universal Image Segmentation653
This work proposes OneFormer, a universal image segmentation framework that unifies segmentation with a multi-task train-once design that outperforms specialized Mask2Former models across all three segmentation tasks on ADE20k, Cityscapes, and COCO.
- Neighborhood Attention Transformer540
NA is a pixel-wise operation, localizing self attention to the nearest neighboring pixels, and therefore enjoys a linear time and space complexity compared to the quadratic complexity of SA, and is the first efficient and scalable sliding window attention mechanism for vision.
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- Forget-Me-Not: Learning to Forget in Text-to-Image Diffusion Models369
The Forget-Me-Not method is proposed, a model-centric and efficient solution designed to remove identities, objects, or styles from a well-configured text-to-image model in as little as 30 seconds, without significantly impairing its ability to generate other content.
- More Control for Free! Image Synthesis with Semantic Diffusion Guidance354
This work investigates fine-grained, continuous control of the denoising diffusion probabilistic model class, and introduces a novel unified framework for semantic diffusion guidance, which allows either language or image guidance, or both.
- Versatile Diffusion: Text, Images and Variations All in One Diffusion Model295
This work expands the existing single-flow diffusion pipeline into a multi-task multimodal network, dubbed Versatile Diffusion (VD), that handles multiple flows of text-to-image, image- to-text, and variations in one unified model.
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- StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Text273
Experiments show that StreamingT2V produces more motion, while competing methods suffer from video stagnation when applied naively in an autoregressive fashion, surpassing competitors in both consistency and motion.
- AlignSeg: Feature-Aligned Segmentation Networks199
This paper proposes Feature-Aligned Segmentation Networks (AlignSeg), a feature aggregation network that adopts a simple learnable interpolation strategy to learn transformation offsets of pixels that can effectively relieve the feature misalignment issue caused by multi-resolution feature aggregation.
- VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolution179
This work proposes Video Implicit Neural Representation (VideoINR), and shows its applications for Space-Time Video Super-Resolution, and shows that VideoINR achieves competitive performances with state-of-the-art STVSR methods on common up-sampling scales and significantly outperforms prior works on continuous and out- of-training-distribution scales.
- Cosmos 3: Omnimodal World Models for Physical AI150
This report introduces Cosmos 3, a family of omnimodal world models designed to jointly process and generate language, image, video, audio, and action sequences within a unified mixture-of-transformers architecture and demonstrates omnimodal world models as scalable, general-purpose backbones for embodied agents.
- Pyramid Attention Network for Image Restoration142
A novel Pyramid Attention module for image restoration, which captures long-range feature correspondences from a multi-scale feature pyramid, and is designed to be able to borrow clean signals from their “clean” correspondences at the coarser levels.
- 141
- SeMask: Semantically Masked Transformers for Semantic Segmentation132
This paper argues that incorporating semantic information of the image into pretrained hierarchical transformer-based backbones while finetuning improves the performance considerably and proposes SeMask, a simple and effective framework that incorporates semantic information into the encoder with the help of a semantic attention operation.
- Deep learning-based automated image segmentation for concrete petrographic analysis120
The experimental results showed that CNN outperforms the color-based segmentation by a considerable margin, and has comparable accuracy to human experts, and the segmentation time is reduced to mere seconds.
- Adaptive Consistency Regularization for Semi-Supervised Transfer Learning115
This work considers semi-supervised learning and transfer learning jointly, leading to a more practical and competitive paradigm that can utilize both powerful pre-trained models from source domain as well as labeled/unlabeled data in the target domain.
- Towards Layer-wise Image Vectorization114
This work proposes Layer-wise Image Vectorization, namely LIVE, to convert raster images to SVGs and simultaneously maintain its image topology, and demonstrates that LIVE presents more plausible vectorized forms than prior works and can be generalized to new images.
- ConvMLP: Hierarchical Convolutional MLPs for Vision102
Experiments on object detection and semantic segmentation show that visual representation learned by ConvMLP can be seamlessly transferred to downstream tasks and achieve competitive results with fewer parameters.
- OpenBias: Open-Set Bias Detection in Text-to-Image Generative Models97
This paper presents OpenBias, a new pipeline that identifies and quantifies the severity of biases agnostically, without access to any precompiled set, and demonstrates that OpenBias agrees with current closed-set bias detection methods and human judgement.
- Prompt-Free Diffusion: Taking “Text” Out of Text-to-Image Diffusion Models92
The proposed frame-work, Prompt-Free Diffusion, relies on only visual inputs to generate new images and outperform prior exemplar-based image synthesis approaches, and is naturally extensible to other downstream applications such as anime figure generation and virtual try-on, with promising quality.
- HD-Painter: High-Resolution and Prompt-Faithful Text-Guided Image Inpainting with Diffusion Models87
The Prompt-Aware Introverted Attention layer enhancing self-attention scores by prompt information resulting in better text aligned generations is designed and the Reweighting Attention Score Guidance mechanism seamlessly integrating a post-hoc sampling strategy into the general form of DDIM is introduced.
- Rethinking Text Segmentation: A Novel Dataset and A Text-Specific Refinement Approach86
This work proposes TextSeg, a large-scale fine-annotated text dataset with six types of annotations, and introduces Text Refinement Network (TexRNet), a novel text segmentation approach that adapts to the unique properties of text, e.g. non-convex boundary, diverse texture, etc., which often impose burdens on traditional segmentation models.
- Keys to Better Image Inpainting: Structure and Texture Go Hand in Hand83
A novel inpainting network combining the advantages of the two designs achieves a remarkable visual quality to match state-of-the-art performance in both structure generation and repeating texture synthesis using a single network.
- CompFeat: Comprehensive Feature Aggregation for Video Instance Segmentation80
This work proposes a novel comprehensive feature aggregation approach (CompFeat) to refine features at both frame-level and object-level with temporal and spatial context information to eliminate ambiguities introduced by only using single-frame features.
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 2026-10-13. 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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