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Found 59 papers, 32 papers with code
Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmark
Moreover, current metrics such as AU-ROC have nearly reached saturation on simple datasets, which prevents a comprehensive evaluation of different methods.
MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection
Recent advancements in anomaly detection have seen the efficacy of CNN- and transformer-based approaches.
Deepfake Generation and Detection: A Benchmark and Survey
Deepfake is a technology dedicated to creating highly realistic facial images and videos under specific conditions, which has significant application potential in fields such as entertainment, movie production, digital human creation, to name a few.
DiffFAE: Advancing High-fidelity One-shot Facial Appearance Editing with Space-sensitive Customization and Semantic Preservation
Facial Appearance Editing (FAE) aims to modify physical attributes, such as pose, expression and lighting, of human facial images while preserving attributes like identity and background, showing great importance in photograph.
TexDreamer: Towards Zero-Shot High-Fidelity 3D Human Texture Generation
Texturing 3D humans with semantic UV maps remains a challenge due to the difficulty of acquiring reasonably unfolded UV.
DMAD: Dual Memory Bank for Real-World Anomaly Detection
To address the challenge of real-world anomaly detection, we propose a new framework named Dual Memory bank enhanced representation learning for Anomaly Detection (DMAD).
Explore In-Context Segmentation via Latent Diffusion Models
In-context segmentation has drawn more attention with the introduction of vision foundation models.
PointSeg: A Training-Free Paradigm for 3D Scene Segmentation via Foundation Models
On top of that, PointSeg can incorporate with various segmentation models and even surpasses the supervised methods.
DiffuMatting: Synthesizing Arbitrary Objects with Matting-level Annotation
Our DiffuMatting shows several potential applications (e. g., matting-data generator, community-friendly art design and controllable generation).
Dual-path Frequency Discriminators for Few-shot Anomaly Detection
We consider anomaly detection as a discriminative classification problem, wherefore the dual-path feature discrimination module is employed to detect and locate the image-level and feature-level anomalies in the feature space.
A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect
Visual Anomaly Detection (VAD) endeavors to pinpoint deviations from the concept of normality in visual data, widely applied across diverse domains, e. g., industrial defect inspection, and medical lesion detection.
UniM-OV3D: Uni-Modality Open-Vocabulary 3D Scene Understanding with Fine-Grained Feature Representation
3D open-vocabulary scene understanding aims to recognize arbitrary novel categories beyond the base label space.
Towards Language-Driven Video Inpainting via Multimodal Large Language Models
We introduce a new task -- language-driven video inpainting, which uses natural language instructions to guide the inpainting process.
Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection
Industrial anomaly detection is generally addressed as an unsupervised task that aims at locating defects with only normal training samples.
A Generalist FaceX via Learning Unified Facial Representation
This work presents FaceX framework, a novel facial generalist model capable of handling diverse facial tasks simultaneously.
Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection
Following this spirit, this paper explores plain ViT architecture for MUAD.
PortraitBooth: A Versatile Portrait Model for Fast Identity-preserved Personalization
Moreover, these methods often grapple with identity distortion and limited expression diversity.
DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection
Reconstruction-based approaches have achieved remarkable outcomes in anomaly detection.
AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model
Existing anomaly inspection methods are limited in their performance due to insufficient anomaly data.
GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detection
Large Multimodal Model (LMM) GPT-4V(ision) endows GPT-4 with visual grounding capabilities, making it possible to handle certain tasks through the Visual Question Answering (VQA) paradigm.
CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection
This paper considers zero-shot Anomaly Detection (AD), performing AD without reference images of the test objects.
Toward High Quality Facial Representation Learning
To improve the facial representation quality, we use feature map of a pre-trained visual backbone as a supervision item and use a partially pre-trained decoder for mask image modeling.
Phasic Content Fusing Diffusion Model with Directional Distribution Consistency for Few-Shot Model Adaption
To address these problems, we propose a novel phasic content fusing few-shot diffusion model with directional distribution consistency loss, which targets different learning objectives at distinct training stages of the diffusion model.
PVG: Progressive Vision Graph for Vision Recognition
Convolution-based and Transformer-based vision backbone networks process images into the grid or sequence structures, respectively, which are inflexible for capturing irregular objects.
Towards Open Vocabulary Learning: A Survey
To our knowledge, this is the first comprehensive literature review of open vocabulary learning.
APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD
In this challenge, our method achieved first place in the zero-shot track, especially excelling in segmentation with an impressive F1 score improvement of 0. 0489 over the second-ranked participant.
Dual Path Transformer with Partition Attention
This paper introduces a novel attention mechanism, called dual attention, which is both efficient and effective.
Learning Global-aware Kernel for Image Harmonization
To address this issue, we propose a novel Global-aware Kernel Network (GKNet) to harmonize local regions with comprehensive consideration of long-distance background references.
Ranked #5 on
Image Harmonization
on iHarmony4
Transavs: End-To-End Audio-Visual Segmentation With Transformer
Generally AVS faces two key challenges: (1) Audio signals inherently exhibit a high degree of information density, as sounds produced by multiple objects are entangled within the same audio stream; (2) Objects of the same category tend to produce similar audio signals, making it difficult to distinguish between them and thus leading to unclear segmentation results.
High-fidelity Generalized Emotional Talking Face Generation with Multi-modal Emotion Space Learning
Specifically, we supplement the emotion style in text prompts and use an Aligned Multi-modal Emotion encoder to embed the text, image, and audio emotion modality into a unified space, which inherits rich semantic prior from CLIP.
Multimodal-driven Talking Face Generation via a Unified Diffusion-based Generator
More specifically, given a textured face as the source and the rendered face projected from the desired 3DMM coefficients as the target, our proposed Texture-Geometry-aware Diffusion Model decomposes the complex transfer problem into multi-conditional denoising process, where a Texture Attention-based module accurately models the correspondences between appearance and geometry cues contained in source and target conditions, and incorporate extra implicit information for high-fidelity talking face generation.
Better "CMOS" Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-Resolution
Most of the existing blind image Super-Resolution (SR) methods assume that the blur kernels are space-invariant.
MixTeacher: Mining Promising Labels with Mixed Scale Teacher for Semi-Supervised Object Detection
Despite the remarkable progress made by modern detection models, this challenge is particularly evident in the semi-supervised case.
Calibrated Teacher for Sparsely Annotated Object Detection
Recent works on sparsely annotated object detection alleviate this problem by generating pseudo labels for the missing annotations.
Iterative Few-shot Semantic Segmentation from Image Label Text
Few-shot semantic segmentation aims to learn to segment unseen class objects with the guidance of only a few support images.
Ranked #40 on
Few-Shot Semantic Segmentation
on COCO-20i (1-shot)
Multimodal Industrial Anomaly Detection via Hybrid Fusion
2D-based Industrial Anomaly Detection has been widely discussed, however, multimodal industrial anomaly detection based on 3D point clouds and RGB images still has many untouched fields.
Ranked #3 on
RGB+3D Anomaly Detection and Segmentation
on MVTEC 3D-AD
(using extra training data)
Contrastive Learning
RGB+3D Anomaly Detection and Segmentation
Learning with Noisy labels via Self-supervised Adversarial Noisy Masking
Collecting large-scale datasets is crucial for training deep models, annotating the data, however, inevitably yields noisy labels, which poses challenges to deep learning algorithms.
Ranked #2 on
Image Classification
on Clothing1M
(using extra training data)
Self-Supervised Likelihood Estimation with Energy Guidance for Anomaly Segmentation in Urban Scenes
Based on the proposed estimators, we devise an adaptive self-supervised training framework, which exploits the contextual reliance and estimated likelihood to refine mask annotations in anomaly areas.
Rethinking Mobile Block for Efficient Attention-based Models
This paper focuses on developing modern, efficient, lightweight models for dense predictions while trading off parameters, FLOPs, and performance.
Reference Twice: A Simple and Unified Baseline for Few-Shot Instance Segmentation
In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework.
Remembering Normality: Memory-guided Knowledge Distillation for Unsupervised Anomaly Detection
Specifically, we first propose a normality recall memory (NR Memory) to strengthen the normality of student-generated features by recalling the stored normal information.
Ranked #11 on
Anomaly Detection
on MVTec AD
Learning To Measure the Point Cloud Reconstruction Loss in a Representation Space
Specifically, we use the contrastive constraint to help CALoss learn a representation space with shape similarity, while we introduce the adversarial strategy to help CALoss mine differences between reconstructed results and ground truths.
SFNet: Faster, Accurate, and Domain Agnostic Semantic Segmentation via Semantic Flow
In this paper, we focus on exploring effective methods for faster, accurate, and domain agnostic semantic segmentation.
EATFormer: Improving Vision Transformer Inspired by Evolutionary Algorithm
Motivated by biological evolution, this paper explains the rationality of Vision Transformer by analogy with the proven practical Evolutionary Algorithm (EA) and derives that both have consistent mathematical formulation.
Region-Aware Face Swapping
This paper presents a novel Region-Aware Face Swapping (RAFSwap) network to achieve identity-consistent harmonious high-resolution face generation in a local-global manner: \textbf{1)} Local Facial Region-Aware (FRA) branch augments local identity-relevant features by introducing the Transformer to effectively model misaligned cross-scale semantic interaction.
Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detection
Density-based and classification-based methods have ruled unsupervised anomaly detection in recent years, while reconstruction-based methods are rarely mentioned for the poor reconstruction ability and low performance.
Ranked #39 on
Anomaly Detection
on MVTec AD
SCSNet: An Efficient Paradigm for Learning Simultaneously Image Colorization and Super-Resolution
In the practical application of restoring low-resolution gray-scale images, we generally need to run three separate processes of image colorization, super-resolution, and dows-sampling operation for the target device.
SelFSR: Self-Conditioned Face Super-Resolution in the Wild via Flow Field Degradation Network
To tackle this problem, we propose a novel domain-adaptive degradation network for face super-resolution in the wild.
Riemannian Manifold Embeddings for Straight-Through Estimator
Quantized Neural Networks (QNNs) aim at replacing full-precision weights $\boldsymbol{W}$ with quantized weights $\boldsymbol{\hat{W}}$, which make it possible to deploy large models to mobile and miniaturized devices easily.
Analogous to Evolutionary Algorithm: Designing a Unified Sequence Model
Inspired by biological evolution, we explain the rationality of Vision Transformer by analogy with the proven practical Evolutionary Algorithm (EA) and derive that both of them have consistent mathematical representation.
RFNet: Recurrent Forward Network for Dense Point Cloud Completion
The RFE extracts multiple global features from the incomplete point clouds for different recurrent levels, and the FDC generates point clouds in a coarse-to-fine pipeline.
Optimizing Quantized Neural Networks with Natural Gradient
Quantized Neural Networks (QNNs) have achieved an enormous step in improving computational efficiency, making it possible to deploy large models to mobile and miniaturized devices.
APB2FaceV2: Real-Time Audio-Guided Multi-Face Reenactment
Audio-guided face reenactment aims to generate a photorealistic face that has matched facial expression with the input audio.
DTVNet: Dynamic Time-lapse Video Generation via Single Still Image
The proposed DTVNet consists of two submodules: \emph{Optical Flow Encoder} (OFE) and \emph{Dynamic Video Generator} (DVG).
Hierarchical and Efficient Learning for Person Re-Identification
Recent works in the person re-identification task mainly focus on the model accuracy while ignore factors related to the efficiency, e. g. model size and latency, which are critical for practical application.
APB2Face: Audio-guided face reenactment with auxiliary pose and blink signals
Audio-guided face reenactment aims at generating photorealistic faces using audio information while maintaining the same facial movement as when speaking to a real person.
Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimation
Unsupervised learning of optical flow, which leverages the supervision from view synthesis, has emerged as a promising alternative to supervised methods.
Ranked #2 on
Optical Flow Estimation
on KITTI 2012 unsupervised
Realistic Face Reenactment via Self-Supervised Disentangling of Identity and Pose
On the one hand, we adopt the deforming autoencoder to disentangle identity and pose representations.
FReeNet: Multi-Identity Face Reenactment
This paper presents a novel multi-identity face reenactment framework, named FReeNet, to transfer facial expressions from an arbitrary source face to a target face with a shared model.
