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Found 36 papers, 10 papers with code
Training-free Diffusion Model Alignment with Sampling Demons
To address these limitations, we propose a stochastic optimization approach, dubbed Demon, to guide the denoising process at inference time without backpropagation through reward functions or model retraining.
Improving Visual Object Tracking through Visual Prompting
PiVOT proposes a prompt generation network with the pre-trained foundation model CLIP to automatically generate and refine visual prompts, enabling the transfer of foundation model knowledge for tracking.
Ranked #1 on
Visual Object Tracking
on AVisT
DiffQRCoder: Diffusion-based Aesthetic QR Code Generation with Scanning Robustness Guided Iterative Refinement
With the success of Diffusion Models for image generation, the technologies also have revolutionized the aesthetic Quick Response (QR) code generation.
Make Graph-based Referring Expression Comprehension Great Again through Expression-guided Dynamic Gating and Regression
Extensive experimental results on the RefCOCO, RefCOCO+, RefCOCOg, Flickr30K, RefClef, and Ref-reasoning datasets demonstrate the effectiveness of the DGC module and the EGR strategy in consistently boosting the performances of various graph-based REC methods.
Pixel Is Not A Barrier: An Effective Evasion Attack for Pixel-Domain Diffusion Models
Diffusion Models have emerged as powerful generative models for high-quality image synthesis, with many subsequent image editing techniques based on them.
A Recipe for CAC: Mosaic-based Generalized Loss for Improved Class-Agnostic Counting
Class agnostic counting (CAC) is a vision task that can be used to count the total occurrence number of any given reference objects in the query image.
Towards More General Video-based Deepfake Detection through Facial Feature Guided Adaptation for Foundation Model
With the rise of deep learning, generative models have enabled the creation of highly realistic synthetic images, presenting challenges due to their potential misuse.
Diffusion-based Aesthetic QR Code Generation via Scanning-Robust Perceptual Guidance
In this paper, we introduce a novel diffusion-model-based aesthetic QR code generation pipeline, utilizing pre-trained ControlNet and guided iterative refinement via a novel classifier guidance (SRG) based on the proposed Scanning-Robust Loss (SRL) tailored with QR code mechanisms, which ensures both aesthetics and scannability.
BlenDA: Domain Adaptive Object Detection through diffusion-based blending
Unsupervised domain adaptation (UDA) aims to transfer a model learned using labeled data from the source domain to unlabeled data in the target domain.
Deep Complex U-Net with Conformer for Audio-Visual Speech Enhancement
Recent studies have increasingly acknowledged the advantages of incorporating visual data into speech enhancement (SE) systems.
MeDM: Mediating Image Diffusion Models for Video-to-Video Translation with Temporal Correspondence Guidance
This study introduces an efficient and effective method, MeDM, that utilizes pre-trained image Diffusion Models for video-to-video translation with consistent temporal flow.
Diffusion to Confusion: Naturalistic Adversarial Patch Generation Based on Diffusion Model for Object Detector
To the best of our knowledge, we are the first to propose DM-based naturalistic adversarial patch generation for object detectors.
Video ControlNet: Towards Temporally Consistent Synthetic-to-Real Video Translation Using Conditional Image Diffusion Models
To the best of our knowledge, our proposed method is the first to accomplish diverse and temporally consistent synthetic-to-real video translation using conditional image diffusion models.
DDPG based on multi-scale strokes for financial time series trading strategy
With the development of artificial intelligence, more and more financial practitioners apply deep reinforcement learning to financial trading strategies. However, It is difficult to extract accurate features due to the characteristics of considerable noise, highly non-stationary, and non-linearity of single-scale time series, which makes it hard to obtain high returns. In this paper, we extract a multi-scale feature matrix on multiple time scales of financial time series, according to the classic financial theory-Chan Theory, and put forward to an approach of multi-scale stroke deep deterministic policy gradient reinforcement learning model(MSSDDPG)to search for the optimal trading strategy. We carried out experiments on the datasets of the Dow Jones, S&P 500 of U. S. stocks, and China's CSI 300, SSE Composite, evaluate the performance of our approach compared with turtle trading strategy, Deep Q-learning(DQN)reinforcement learning strategy, and deep deterministic policy gradient (DDPG) reinforcement learning strategy. The result shows that our approach gets the best performance in China CSI 300, SSE Composite, and get an outstanding result in Dow Jones, S&P 500 of U. S.
Continual Learning for Visual Search with Backward Consistent Feature Embedding
In visual search, the gallery set could be incrementally growing and added to the database in practice.
LR-to-HR Face Hallucination with an Adversarial Progressive Attribute-Induced Network
Face super-resolution is a challenging and highly ill-posed problem since a low-resolution (LR) face image may correspond to multiple high-resolution (HR) ones during the hallucination process and cause a dramatic identity change for the final super-resolved results.
Video-based Person Re-identification without Bells and Whistles
Besides, we discover the errors not only for the identity labels of tracklets but also for the evaluation protocol for the test data of MARS.
Class-Aware Robust Adversarial Training for Object Detection
Meanwhile, instead of normalizing the total loss with the number of objects, the proposed approach decomposes the total loss into class-wise losses and normalizes each class loss using the number of objects for the class.
Naturalistic Physical Adversarial Patch for Object Detectors
Most prior works on physical adversarial attacks mainly focus on the attack performance but seldom enforce any restrictions over the appearance of the generated adversarial patches.
Pose And Joint-Aware Action Recognition
Unlike other modalities, constellation of joints and their motion generate models with succinct human motion information for activity recognition.
Ranked #1 on
Action Recognition
on Mimetics
The Devil is in the Details: Self-Supervised Attention for Vehicle Re-Identification
In recent years, the research community has approached the problem of vehicle re-identification (re-id) with attention-based models, specifically focusing on regions of a vehicle containing discriminative information.
A Dual-Path Model With Adaptive Attention For Vehicle Re-Identification
In this paper, we present a novel dual-path adaptive attention model for vehicle re-identification (AAVER).
Vehicle Key-Point and Orientation Estimation
Vehicle Re-Identification
Uncertainty Modeling of Contextual-Connections between Tracklets for Unconstrained Video-based Face Recognition
In this paper, we propose the Uncertainty-Gated Graph (UGG), which conducts graph-based identity propagation between tracklets, which are represented by nodes in a graph.
Unsupervised Domain-Specific Deblurring via Disentangled Representations
Image deblurring aims to restore the latent sharp images from the corresponding blurred ones.
Deep Convolutional Neural Networks in the Face of Caricature: Identity and Image Revealed
Deep convolutional neural networks (DCNNs) also create generalizable face representations, but with cascades of simulated neurons.
An Automatic System for Unconstrained Video-Based Face Recognition
In this work, we consider challenging scenarios for unconstrained video-based face recognition from multiple-shot videos and surveillance videos with low-quality frames.
A Fast and Accurate System for Face Detection, Identification, and Verification
We provide evaluation results of the proposed face detector on challenging unconstrained face detection datasets.
An Experimental Evaluation of Covariates Effects on Unconstrained Face Verification
In this paper, we comprehensively study two covariate related problems for unconstrained face verification: first, how covariates affect the performance of deep neural networks on the large-scale unconstrained face verification problem; second, how to utilize covariates to improve verification performance.
Deep Density Clustering of Unconstrained Faces
In this paper, we consider the problem of grouping a collection of unconstrained face images in which the number of subjects is not known.
Crystal Loss and Quality Pooling for Unconstrained Face Verification and Recognition
We show that integrating this simple step in the training pipeline significantly improves the performance of face verification and recognition systems.
A Proximity-Aware Hierarchical Clustering of Faces
In this paper, we propose an unsupervised face clustering algorithm called "Proximity-Aware Hierarchical Clustering" (PAHC) that exploits the local structure of deep representations.
Deep Heterogeneous Feature Fusion for Template-Based Face Recognition
Thus, in this work, we propose a deep heterogeneous feature fusion network to exploit the complementary information present in features generated by different deep convolutional neural networks (DCNNs) for template-based face recognition, where a template refers to a set of still face images or video frames from different sources which introduces more blur, pose, illumination and other variations than traditional face datasets.
Deep Convolutional Neural Network Features and the Original Image
The results show that the DCNN features contain surprisingly accurate information about the yaw and pitch of a face, and about whether the face came from a still image or a video frame.
Unconstrained Still/Video-Based Face Verification with Deep Convolutional Neural Networks
Over the last five years, methods based on Deep Convolutional Neural Networks (DCNNs) have shown impressive performance improvements for object detection and recognition problems.
Towards the Design of an End-to-End Automated System for Image and Video-based Recognition
In this paper, we present a brief history of developments in computer vision and artificial neural networks over the last forty years for the problem of image-based recognition.
Unconstrained Face Verification using Deep CNN Features
In this paper, we present an algorithm for unconstrained face verification based on deep convolutional features and evaluate it on the newly released IARPA Janus Benchmark A (IJB-A) dataset.
Ranked #13 on
Face Verification
on IJB-A
