Search Results for author:
Found 18 papers, 10 papers with code
AnyPattern: Towards In-context Image Copy Detection
This paper explores in-context learning for image copy detection (ICD), i. e., prompting an ICD model to identify replicated images with new tampering patterns without the need for additional training.
Transformer based Pluralistic Image Completion with Reduced Information Loss
The indices of quantized pixels are used as tokens for the inputs and prediction targets of the transformer.
MiM-ISTD: Mamba-in-Mamba for Efficient Infrared Small Target Detection
Inspired by the recent basic model with linear complexity for long-distance modeling, called Mamba, we explore the potential of this state space model for ISTD task in terms of effectiveness and efficiency in the paper.
Bootstrapping Audio-Visual Segmentation by Strengthening Audio Cues
This bidirectional interaction narrows the modality imbalance, facilitating more effective learning of integrated audio-visual representations.
TCI-Former: Thermal Conduction-Inspired Transformer for Infrared Small Target Detection
We abstract this process as the directional movement of feature map pixels to target areas through convolution, pooling and interactions with surrounding pixels, which can be analogous to the movement of thermal particles constrained by surrounding variables and particles.
SimAC: A Simple Anti-Customization Method against Text-to-Image Synthesis of Diffusion Models
Despite the success of diffusion-based customization methods on visual content creation, increasing concerns have been raised about such techniques from both privacy and political perspectives.
Towards More Unified In-context Visual Understanding
Thanks to this design, the model is capable of handling in-context vision understanding tasks with multimodal output in a unified pipeline. Experimental results demonstrate that our model achieves competitive performance compared with specialized models and previous ICL baselines.
Exploring the Application of Large-scale Pre-trained Models on Adverse Weather Removal
Inspired by the various successful applications of large-scale pre-trained models (e. g, CLIP), in this paper, we explore the potential benefits of them for this task through both spatial feature representation learning and semantic information embedding aspects: 1) for spatial feature representation learning, we design a Spatially-Adaptive Residual (\textbf{SAR}) Encoder to extract degraded areas adaptively.
HQ-50K: A Large-scale, High-quality Dataset for Image Restoration
This paper introduces a new large-scale image restoration dataset, called HQ-50K, which contains 50, 000 high-quality images with rich texture details and semantic diversity.
Multi-spectral Class Center Network for Face Manipulation Detection and Localization
Next, a novel Multi-Spectral Class Center Network (MSCCNet) is proposed for face manipulation detection and localization.
Video Action Segmentation via Contextually Refined Temporal Keypoints
Video action segmentation refers to the task of densely casting each video frame or short segment in an untrimmed video into some pre-specified action categories.
UIA-ViT: Unsupervised Inconsistency-Aware Method based on Vision Transformer for Face Forgery Detection
UPCL is designed for learning the consistency-related representation with progressive optimized pseudo annotations.
Reduce Information Loss in Transformers for Pluralistic Image Inpainting
The indices of quantized pixels are used as tokens for the inputs and prediction targets of transformer.
Ranked #6 on
Seeing Beyond the Visible
on KITTI360-EX
HairCLIP: Design Your Hair by Text and Reference Image
Hair editing is an interesting and challenging problem in computer vision and graphics.
Diverse Semantic Image Synthesis via Probability Distribution Modeling
In this paper, we propose a novel diverse semantic image synthesis framework from the perspective of semantic class distributions, which naturally supports diverse generation at semantic or even instance level.
Ranked #1 on
Image-to-Image Translation
on ADE20K Labels-to-Photos
(LPIPS metric)
Efficient Semantic Image Synthesis via Class-Adaptive Normalization
Spatially-adaptive normalization (SPADE) is remarkably successful recently in conditional semantic image synthesis \cite{park2019semantic}, which modulates the normalized activation with spatially-varying transformations learned from semantic layouts, to prevent the semantic information from being washed away.
MichiGAN: Multi-Input-Conditioned Hair Image Generation for Portrait Editing
In this paper, we present MichiGAN (Multi-Input-Conditioned Hair Image GAN), a novel conditional image generation method for interactive portrait hair manipulation.
Rethinking Spatially-Adaptive Normalization
Despite its impressive performance, a more thorough understanding of the true advantages inside the box is still highly demanded, to help reduce the significant computation and parameter overheads introduced by these new structures.
