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Found 16 papers, 10 papers with code
Attacking Transformers with Feature Diversity Adversarial Perturbation
Understanding the mechanisms behind Vision Transformer (ViT), particularly its vulnerability to adversarial perturba tions, is crucial for addressing challenges in its real-world applications.
AMD:Anatomical Motion Diffusion with Interpretable Motion Decomposition and Fusion
Generating realistic human motion sequences from text descriptions is a challenging task that requires capturing the rich expressiveness of both natural language and human motion. Recent advances in diffusion models have enabled significant progress in human motion synthesis. However, existing methods struggle to handle text inputs that describe complex or long motions. In this paper, we propose the Adaptable Motion Diffusion (AMD) model, which leverages a Large Language Model (LLM) to parse the input text into a sequence of concise and interpretable anatomical scripts that correspond to the target motion. This process exploits the LLM's ability to provide anatomical guidance for complex motion synthesis. We then devise a two-branch fusion scheme that balances the influence of the input text and the anatomical scripts on the inverse diffusion process, which adaptively ensures the semantic fidelity and diversity of the synthesized motion. Our method can effectively handle texts with complex or long motion descriptions, where existing methods often fail.
Optimized View and Geometry Distillation from Multi-view Diffuser
We leverage the rendered views from the optimized radiance field as the basis and develop a two-step specialization process of a 2D diffusion model, which is adept at conducting object-specific denoising and generating high-quality multi-view images.
Fine-grained Appearance Transfer with Diffusion Models
A pivotal aspect of our approach is the strategic use of the predicted $x_0$ space by diffusion models within the latent space of diffusion processes.
Progressive Text-to-Image Diffusion with Soft Latent Direction
In spite of the rapidly evolving landscape of text-to-image generation, the synthesis and manipulation of multiple entities while adhering to specific relational constraints pose enduring challenges.
NeMF: Inverse Volume Rendering with Neural Microflake Field
Recovering the physical attributes of an object's appearance from its images captured under an unknown illumination is challenging yet essential for photo-realistic rendering.
Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly Detection
To address this issue, we propose an Uncertainty Regulated Dual Memory Units (UR-DMU) model to learn both the representations of normal data and discriminative features of abnormal data.
Compact Transformer Tracker with Correlative Masked Modeling
Transformer framework has been showing superior performances in visual object tracking for its great strength in information aggregation across the template and search image with the well-known attention mechanism.
Dynamic Feature Pruning and Consolidation for Occluded Person Re-Identification
The framework mainly consists of a sparse encoder, a multi-view feature mathcing module, and a feature consolidation decoder.
Transformer Tracking with Cyclic Shifting Window Attention
Transformer architecture has been showing its great strength in visual object tracking, for its effective attention mechanism.
NeReF: Neural Refractive Field for Fluid Surface Reconstruction and Implicit Representation
We present a novel neural refractive field(NeReF) to recover wavefront of transparent fluids by simultaneously estimating the surface position and normal of the fluid front.
Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation
We aim at the problem named One-Shot Unsupervised Domain Adaptation.
domain classification
One-shot Unsupervised Domain Adaptation
+2
Significance-aware Information Bottleneck for Domain Adaptive Semantic Segmentation
For unsupervised domain adaptation problems, the strategy of aligning the two domains in latent feature space through adversarial learning has achieved much progress in image classification, but usually fails in semantic segmentation tasks in which the latent representations are overcomplex.
Every Node Counts: Self-Ensembling Graph Convolutional Networks for Semi-Supervised Learning
To capitalize on the information from unlabeled nodes to boost the training for GCN, we propose a novel framework named Self-Ensembling GCN (SEGCN), which marries GCN with Mean Teacher - another powerful model in semi-supervised learning.
Ranked #4 on
Node Classification
on Cora: fixed 20 node per class
Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation
We consider the problem of unsupervised domain adaptation in semantic segmentation.
Ranked #8 on
Semantic Segmentation
on DADA-seg
Macro-Micro Adversarial Network for Human Parsing
To address the two kinds of inconsistencies, this paper proposes the Macro-Micro Adversarial Net (MMAN).
Ranked #12 on
Semantic Segmentation
on LIP val
