Search Results for author:
Found 26 papers, 14 papers with code
Origin Identification for Text-Guided Image-to-Image Diffusion Models
Subsequently, it is demonstrated that such a simple linear transformation can be generalized across different diffusion models.
UNICL-SAM: Uncertainty-Driven In-Context Segmentation with Part Prototype Discovery
In response, we propose UNICL-SAM, to better model the example distribution and extract robust representations to help in-context segmentation.
SweetTokenizer: Semantic-Aware Spatial-Temporal Tokenizer for Compact Visual Discretization
SweetTokenizer achieves comparable video reconstruction fidelity with only \textbf{25\%} of the tokens used in previous state-of-the-art video tokenizers, and boost video generation results by \textbf{32. 9\%} w. r. t gFVD.
Llama SLayer 8B: Shallow Layers Hold the Key to Knowledge Injection
We observe performance dips in question-answering benchmarks after the removal or expansion of the shallow layers, and the degradation shrinks as the layer gets deeper, indicating that the shallow layers hold the key to knowledge injection.
Image Copy Detection for Diffusion Models
Existing Image Copy Detection (ICD) models, though accurate in detecting hand-crafted replicas, overlook the challenge from diffusion models.
Mixture-of-Noises Enhanced Forgery-Aware Predictor for Multi-Face Manipulation Detection and Localization
The FUP integrates detection and localization tasks using a token learning strategy and multiple forgery-aware transformers, which facilitates the use of classification information to enhance localization capability.
Replication in Visual Diffusion Models: A Survey and Outlook
In this survey, we provide the first comprehensive review of replication in visual diffusion models, marking a novel contribution to the field by systematically categorizing the existing studies into unveiling, understanding, and mitigating this phenomenon.
Learning Solution-Aware Transformers for Efficiently Solving Quadratic Assignment Problem
Recently various optimization problems, such as Mixed Integer Linear Programming Problems (MILPs), have undergone comprehensive investigation, leveraging the capabilities of machine learning.
AnyPattern: Towards In-context Image Copy Detection
To accommodate the "seen $\rightarrow$ unseen" generalization scenario, we construct the first large-scale pattern dataset named AnyPattern, which has the largest number of tamper patterns ($90$ for training and $10$ for testing) among all the existing ones.
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
By aggregating the visual word and visual sentence features, our MiM-ISTD can effectively explore both global and local information.
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 for Protecting Face Privacy 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.
Multi-spectral Class Center Network for Face Manipulation Detection and Localization
To this end, 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 Deep-Fashion
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.
