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Found 24 papers, 9 papers with code
Extract and Merge: Superpixel Segmentation with Regional Attributes
For a certain object in an image, the relationship between its central region and the peripheral region is not well utilized in existing superpixel segmentation methods.
Generalizing to Out-of-Sample Degradations via Model Reprogramming
To address this issue, we propose a model reprogramming framework, which translates out-of-sample degradations by quantum mechanic and wave functions.
Prompt-Driven Dynamic Object-Centric Learning for Single Domain Generalization
In this paper, we propose a dynamic object-centric perception network based on prompt learning, aiming to adapt to the variations in image complexity.
Multi-Source Collaborative Gradient Discrepancy Minimization for Federated Domain Generalization
Federated Domain Generalization aims to learn a domain-invariant model from multiple decentralized source domains for deployment on unseen target domain.
Joint Correcting and Refinement for Balanced Low-Light Image Enhancement
Specifically, the proposed method, so-called Joint Correcting and Refinement Network (JCRNet), which mainly consists of three stages to balance brightness, color, and illumination of enhancement.
Weakly-Supervised Video Anomaly Detection with Snippet Anomalous Attention
Our approach takes into account snippet-level encoded features without the supervision of pseudo labels.
Reliable and Interpretable Personalized Federated Learning
When there are large differences in data distribution among clients, it is crucial for federated learning to design a reliable client selection strategy and an interpretable client communication framework to better utilize group knowledge.
Prototype-guided Cross-task Knowledge Distillation for Large-scale Models
Considering the complexity and variability of real scene tasks, we propose a Prototype-guided Cross-task Knowledge Distillation (ProC-KD) approach to transfer the intrinsic local-level object knowledge of a large-scale teacher network to various task scenarios.
Peng Cheng Object Detection Benchmark for Smart City
Considering the diversity and complexity of scenes in intelligent city governance, we build a large-scale object detection benchmark for the smart city.
Action Keypoint Network for Efficient Video Recognition
An effective approach is to select informative content from the holistic video, yielding a popular family of dynamic video recognition methods.
Ranked #42 on
Action Recognition
on Something-Something V1
Frequency Spectrum Augmentation Consistency for Domain Adaptive Object Detection
In the second stage, augmented source and target data with pseudo labels are adopted to perform the self-training for prediction consistency.
Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial Removal
On the other hand, the neglect of noise sensitivity differences between image regions by existing decision-based attacks further compromises the efficiency of noise compression, especially for ViTs.
Vector-Decomposed Disentanglement for Domain-Invariant Object Detection
Secondly, domain-specific representations are introduced as the differences between the input and domain-invariant representations.
Domain Adaptation without Model Transferring
Existing unsupervised domain adaptation (UDA) methods can achieve promising performance without transferring data from source domain to target domain.
Anomaly Detection with Prototype-Guided Discriminative Latent Embeddings
Recent efforts towards video anomaly detection (VAD) try to learn a deep autoencoder to describe normal event patterns with small reconstruction errors.
Exploring Uncertainty in Deep Learning for Construction of Prediction Intervals
In this paper, We explore the uncertainty in deep learning to construct the prediction intervals.
Universal-Prototype Enhancing for Few-Shot Object Detection
Thus, we develop a new framework of few-shot object detection with universal prototypes ({FSOD}^{up}) that owns the merit of feature generalization towards novel objects.
Ranked #23 on
Few-Shot Object Detection
on MS-COCO (10-shot)
Hierarchical memory decoder for visual narrating
In this article, we devise a novel memory decoder for visual narrating.
Ranked #13 on
Visual Storytelling
on VIST
Hierarchical Memory Decoding for Video Captioning
Instead of the common practice, i. e., sequence decoding with RNN, in this paper, we devise a novel memory decoder for video captioning.
Connective Cognition Network for Directional Visual Commonsense Reasoning
Inspired by this idea, towards VCR, we propose a connective cognition network (CCN) to dynamically reorganize the visual neuron connectivity that is contextualized by the meaning of questions and answers.
Instance-Invariant Domain Adaptive Object Detection via Progressive Disentanglement
Most state-of-the-art methods of object detection suffer from poor generalization ability when the training and test data are from different domains, e. g., with different styles.
Curls & Whey: Boosting Black-Box Adversarial Attacks
On the one hand, existing iterative attacks add noises monotonically along the direction of gradient ascent, resulting in a lack of diversity and adaptability of the generated iterative trajectories.
Movie Question Answering: Remembering the Textual Cues for Layered Visual Contents
Movies provide us with a mass of visual content as well as attracting stories.
Pooling the Convolutional Layers in Deep ConvNets for Action Recognition
It achieves the accuracy of 93. 78\% on UCF101 which is the state-of-the-art and the accuracy of 65. 62\% on HMDB51 which is comparable to the state-of-the-art.
