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Found 32 papers, 5 papers with code
Unsupervised Collaborative Metric Learning with Mixed-Scale Groups for General Object Retrieval
This paper presents a novel unsupervised deep metric learning approach, termed unsupervised collaborative metric learning with mixed-scale groups (MS-UGCML), devised to learn embeddings for objects of varying scales.
Concept-Guided Prompt Learning for Generalization in Vision-Language Models
However, for generalization tasks, the current fine-tuning methods for CLIP, such as CoOp and CoCoOp, demonstrate relatively low performance on some fine-grained datasets.
Learning to Adapt CLIP for Few-Shot Monocular Depth Estimation
When CLIP is used for depth estimation tasks, the patches, divided from the input images, can be combined with a series of semantic descriptions of the depth information to obtain similarity results.
BDC-Adapter: Brownian Distance Covariance for Better Vision-Language Reasoning
Large-scale pre-trained Vision-Language Models (VLMs), such as CLIP and ALIGN, have introduced a new paradigm for learning transferable visual representations.
Unsupervised Prototype Adapter for Vision-Language Models
To leverage the valuable knowledge encoded within these models for downstream tasks, several fine-tuning approaches, including prompt tuning methods and adapter-based methods, have been developed to adapt vision-language models effectively with supervision.
Cross-Modal Concept Learning and Inference for Vision-Language Models
Based on these visual concepts, we construct a discriminative representation of images and learn a concept inference network to perform downstream image classification tasks, such as few-shot learning and domain generalization.
Cross-Inferential Networks for Source-free Unsupervised Domain Adaptation
Our main idea is that, when we adapt the network model to predict the sample labels from encoded features, we use these prediction results to construct new training samples with derived labels to learn a new examiner network that performs a different but compatible task in the target domain.
Critical Sampling for Robust Evolution Operator Learning of Unknown Dynamical Systems
Given an unknown dynamical system, what is the minimum number of samples needed for effective learning of its governing laws and accurate prediction of its future evolution behavior, and how to select these critical samples?
Self-Correctable and Adaptable Inference for Generalizable Human Pose Estimation
This strong correlation suggests that we can use this error as feedback to guide the correction process.
Neuro-Modulated Hebbian Learning for Fully Test-Time Adaptation
We observe that the performance of this feed-forward Hebbian learning for fully test-time adaptation can be significantly improved by incorporating a feedback neuro-modulation layer.
Contrastive Bayesian Analysis for Deep Metric Learning
Recent methods for deep metric learning have been focusing on designing different contrastive loss functions between positive and negative pairs of samples so that the learned feature embedding is able to pull positive samples of the same class closer and push negative samples from different classes away from each other.
Coded Residual Transform for Generalizable Deep Metric Learning
To address this challenge, in this paper, we introduce a new method called coded residual transform (CRT) for deep metric learning to significantly improve its generalization capability.
Self-Constrained Inference Optimization on Structural Groups for Human Pose Estimation
This group-wise structural correlation can be explored to improve the accuracy and robustness of human pose estimation.
Ranked #1 on
Multi-Person Pose Estimation
on COCO test-dev
Self-Supervised Prime-Dual Networks for Few-Shot Image Classification
We construct a prime-dual network structure for few-shot learning which establishes a commutative relationship between the support set and the query set, as well as a new self- supervision constraint for highly effective few-shot learning.
Invariance-Guided Feature Evolution for Few-Shot Learning
Few-shot learning (FSL) aims to characterize the inherent visual relationship between support and query samples which can be well generalized to unseen classes so that we can accurately infer the labels of query samples from very few support samples.
Relative Order Analysis and Optimization for Unsupervised Deep Metric Learning
During training, this relative order prediction network and the feature embedding network are tightly coupled, providing mutual constraints to each other to improve overall metric learning performance in a cooperative manner.
Spatial Assembly Networks for Image Representation Learning
It has been long recognized that deep neural networks are sensitive to changes in spatial configurations or scene structures.
Structure-Preserving Progressive Low-rank Image Completion for Defending Adversarial Attacks
In this work, we propose to develop a structure-preserving progressive low-rank image completion (SPLIC) method to remove unneeded texture details from the input images and shift the bias of deep neural networks towards global object structures and semantic cues.
Consistency-Sensitivity Guided Ensemble Black-Box Adversarial Attacks in Low-Dimensional Spaces
Specifically, we esti-mate the victim model in the black box using a learned lin-ear composition of an ensemble of surrogate models withdiversified network structures.
Unsupervised Deep Metric Learning with Transformed Attention Consistency and Contrastive Clustering Loss
To further enhance the inter-class discriminative power of the feature generated by this network, we adapt the concept of triplet loss from supervised metric learning to our unsupervised case and introduce the contrastive clustering loss.
Ensemble Generative Cleaning with Feedback Loops for Defending Adversarial Attacks
In this paper, we develop a new method called ensemble generative cleaning with feedback loops (EGC-FL) for effective defense of deep neural networks.
Reciprocal Learning Networks for Human Trajectory Prediction
Based on this unique property, we develop a new approach, called reciprocal learning, for human trajectory prediction.
Generative Cleaning Networks with Quantized Nonlinear Transform for Deep Neural Network Defense
In this paper, we develop a new generative cleaning network with quantized nonlinear transform for effective defense of deep neural networks.
Snowball: Iterative Model Evolution and Confident Sample Discovery for Semi-Supervised Learning on Very Small Labeled Datasets
In this work, we develop a joint sample discovery and iterative model evolution method for semi-supervised learning on very small labeled training sets.
Progressive Neural Networks for Image Classification
The inference structures and computational complexity of existing deep neural networks, once trained, are fixed and remain the same for all test images.
Dual Path Networks for Multi-Person Human Pose Estimation
The task of multi-person human pose estimation in natural scenes is quite challenging.
Knowledge Projection for Deep Neural Networks
In this paper, we will develop a new framework for training deep neural networks on datasets with limited labeled samples using cross-network knowledge projection which is able to improve the network performance while reducing the overall computational complexity significantly.
Towards a Social Virtual Reality Learning Environment in High Fidelity
Virtual Learning Environments (VLEs) are spaces designed to educate students remotely via online platforms.
Human-Computer Interaction Multimedia
Knowledge-Guided Deep Fractal Neural Networks for Human Pose Estimation
Human pose estimation using deep neural networks aims to map input images with large variations into multiple body keypoints which must satisfy a set of geometric constraints and inter-dependency imposed by the human body model.
Ranked #6 on
Pose Estimation
on Leeds Sports Poses
Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking
In this paper, we develop a new approach of spatially supervised recurrent convolutional neural networks for visual object tracking.
A Classification Leveraged Object Detector
Currently, the state-of-the-art image classification algorithms outperform the best available object detector by a big margin in terms of average precision.
Ensemble Video Object Cut in Highly Dynamic Scenes
We incorporate this similarity information into a graph-cut energy minimization framework for foreground object segmentation.
