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- Learning to See in the Dark1,622
A pipeline for processing low-light images is developed, based on end-to-end training of a fully-convolutional network that operates directly on raw sensor data and replaces much of the traditional image processing pipeline, which tends to perform poorly on such data.
- Photographic Image Synthesis with Cascaded Refinement Networks1,013
It is shown that photographic images can be synthesized from semantic layouts by a single feedforward network with appropriate structure, trained end-to-end with a direct regression objective.
- VideoCrafter1: Open Diffusion Models for High-Quality Video Generation633
This model is the first open-source I2V foundation model capable of transforming a given image into a video clip while maintaining content preservation constraints, and can generate realistic and cinematic-quality videos with a resolution of $1024 \times 576.
- FateZero: Fusing Attentions for Zero-shot Text-based Video Editing594
F FateZero, a zero-shot text-based editing method on real-world videos without per-prompt training or use-specific mask, is proposed, which is the first one to show the ability of zero-shot text-driven video style and local attribute editing from the trained text-to-image model.
- Defending ChatGPT against jailbreak attack via self-reminders515
This work systematically documents the threats posed by jailbreak attacks, introduces and analyses a dataset for evaluating defensive interventions and proposes the psychologically inspired self-reminder technique that can efficiently and effectively mitigate against jailbreaks without further training.
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- Involution: Inverting the Inherence of Convolution for Visual Recognition418
The proposed involution operator could be leveraged as fundamental bricks to build the new generation of neural networks for visual recognition, powering different deep learning models on several prevalent benchmarks, including ImageNet classification, COCO detection and segmentation, together with Cityscapes segmentation.
- RODIN: A Generative Model for Sculpting 3D Digital Avatars Using Diffusion405
The RODIN model brings much-needed computational efficiency while preserving the integrity of 3D diffusion by using 3D-aware convolution that attends to projected features in the 2D plane according to their original relationships in 3D.
- Single Image Reflection Separation with Perceptual Losses404
The approach uses a fully convolutional network trained end-to-end with losses that exploit low-level and high-level image information and proposes a novel exclusion loss that enforces pixel-level layer separation.
- FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation379
This work proposes FFB6D, a Full Flow Bidirectional fusion network designed for 6D pose estimation from a single RGBD image, which learns to combine appearance and geometry information for representation learning as well as output representation selection.
- Fast Image Processing with Fully-Convolutional Networks360
This work presents an approach to accelerating a wide variety of image processing operators using a fully-convolutional network that is trained on input-output pairs that demonstrate the operator’s action, and demonstrates that the presented approach is significantly more accurate than prior approximation schemes.
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- High-Fidelity GAN Inversion for Image Attribute Editing311
A novel highfidelity generative adversarial network (GAN) inversion framework that enables attribute editing with image-specific details well-preserved with a clear improvement in both inversion and editing quality is presented.
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- TextDiffuser: Diffusion Models as Text Painters294
This work introduces TextDiffuser, focusing on generating images with visually appealing text that is coherent with backgrounds, and contributes the first large-scale text images dataset with OCR annotations, MARIO-10M, containing 10 million image-text pairs with text recognition, detection, and character-level segmentation annotations.
- Zoom to Learn, Learn to Zoom288
This paper shows that when applying machine learning to digital zoom, it is beneficial to operate on real, RAW sensor data, and shows how to obtain such ground-truth data via optical zoom and contribute a dataset, SR-RAW, for real-world computational zoom.
- Enhanced Invertible Encoding for Learned Image Compression263
This paper proposes an enhanced Invertible Encoding Network with invertible neural networks (INNs) to largely mitigate the information loss problem for better compression, and shows that this method outperforms the existing learned image compression methods and compression standards, including VVC (VTM 12.1), especially for high-resolution images.
- Latent Video Diffusion Models for High-Fidelity Long Video Generation260
This work introduces lightweight video diffusion models that synthesize high-fidelity and arbitrary-long videos from pure noise that outperforms previous methods on 3D pixel space when under a limited computational budget and introduces conditional latent perturbation to reduce performance degradation during generating long-duration videos.
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- Pretraining is All You Need for Image-to-Image Translation219
Examination of the proposed pretraining-based image-to-image translation (PITI) is shown to be capable of synthesizing images of unprecedented realism and faithfulness, and an adversarial training to enhance the texture synthesis in the diffusion model training is proposed.
- TPCN: Temporal Point Cloud Networks for Motion Forecasting218
The Temporal Point Cloud Networks (TPCN) is proposed, a novel and flexible framework with joint spatial and temporal learning for trajectory prediction that achieves state-of-the-art results on the Argoverse motion forecasting benchmark.
- Full Flow: Optical Flow Estimation By Global Optimization over Regular Grids201
It is shown that one-shot global optimization of a classical Horn-Schunck-type objective over regular grids at a single resolution is sufficient to initialize continuous interpolation and achieve state-of-the-art performance on challenging modern benchmarks.
- Follow-Your-Emoji: Fine-Controllable and Expressive Freestyle Portrait Animation199
Follow-Your-Emoji, a diffusion-based framework for portrait animation, which animates a reference portrait with target landmark sequences, is presented and a facial fine-grained loss is proposed to improve the model’s ability of subtle expression perception and reference portrait appearance reconstruction.
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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 2026-10-12. 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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