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Found 53 papers, 31 papers with code
FCC: Fully Connected Correlation for Few-Shot Segmentation
Therefore, having strong prior information for the target object using the support set is essential for guiding the initial training of FSS, which leads to the success of few-shot segmentation in challenging cases, such as when the target object shows considerable variation in appearance, texture, or scale across the support and query images.
Divergent Domains, Convergent Grading: Enhancing Generalization in Diabetic Retinopathy Grading
This approach ensures that our DG model remains robust against early susceptibility to label noise, even when only a limited dataset of non-DR fundus images is available for pretraining.
NT-VOT211: A Large-Scale Benchmark for Night-time Visual Object Tracking
To this end, this paper presents NT-VOT211, a new benchmark tailored for evaluating visual object tracking algorithms in the challenging night-time conditions.
Depth Attention for Robust RGB Tracking
RGB video object tracking is a fundamental task in computer vision.
Towards Combating Frequency Simplicity-biased Learning for Domain Generalization
Given the theoretical justification of models' biased learning behavior on different spatial frequency components, which is based on the dataset frequency properties, we argue that the learning behavior on various frequency components could be manipulated by changing the dataset statistical structure in the Fourier domain.
SynFER: Towards Boosting Facial Expression Recognition with Synthetic Data
Facial expression datasets remain limited in scale due to privacy concerns, the subjectivity of annotations, and the labor-intensive nature of data collection.
Multi-modal Medical Image Fusion For Non-Small Cell Lung Cancer Classification
The early detection and nuanced subtype classification of non-small cell lung cancer (NSCLC), a predominant cause of cancer mortality worldwide, is a critical and complex issue.
Industry 6.0: New Generation of Industry driven by Generative AI and Swarm of Heterogeneous Robots
This paper presents the concept of Industry 6. 0, introducing the world's first fully automated production system that autonomously handles the entire product design and manufacturing process based on user-provided natural language descriptions.
Realistic and Efficient Face Swapping: A Unified Approach with Diffusion Models
Unlike prior works reliant on multiple off-the-shelf models, ours is a relatively unified approach and so it is resilient to errors in other off-the-shelf models.
Domain-Guided Weight Modulation for Semi-Supervised Domain Generalization
In search of this endeavor, we study the challenging problem of semi-supervised domain generalization (SSDG), where the goal is to learn a domain-generalizable model while using only a small fraction of labeled data and a relatively large fraction of unlabeled data.
DPA: Dual Prototypes Alignment for Unsupervised Adaptation of Vision-Language Models
Finally, it addresses visual-textual misalignment by aligning textual prototypes with image prototypes to further improve the adaptation performance.
Modality Invariant Multimodal Learning to Handle Missing Modalities: A Single-Branch Approach
Multimodal networks have demonstrated remarkable performance improvements over their unimodal counterparts.
How Effective are Self-Supervised Models for Contact Identification in Videos
The exploration of video content via Self-Supervised Learning (SSL) models has unveiled a dynamic field of study, emphasizing both the complex challenges and unique opportunities inherent in this area.
Attention Based Simple Primitives for Open World Compositional Zero-Shot Learning
We are exploring Open World Compositional Zero-Shot Learning (OW-CZSL) in this study, where our test space encompasses all potential combinations of attributes and objects.
Judging from Support-set: A New Way to Utilize Few-Shot Segmentation for Segmentation Refinement Process
However, to our knowledge, no method has been developed that can determine the success of segmentation refinement.
Improving Single Domain-Generalized Object Detection: A Focus on Diversification and Alignment
The results consistently demonstrate the superiority of our approach compared to existing methods.
PerSense: Personalized Instance Segmentation in Dense Images
To address this, we propose PerSense, an end-to-end, training-free, and model-agnostic one-shot framework for Personalized instance Segmentation in dense images.
Face-voice Association in Multilingual Environments (FAME) Challenge 2024 Evaluation Plan
The Face-voice Association in Multilingual Environments (FAME) Challenge 2024 focuses on exploring face-voice association under a unique condition of multilingual scenario.
Pose-Guided Self-Training with Two-Stage Clustering for Unsupervised Landmark Discovery
Second, motivated by the ZeroShot performance, we develop a ULD algorithm based on diffusion features using self-training and clustering which also outperforms prior methods by notable margins.
Towards Generalizing to Unseen Domains with Few Labels
Existing domain generalization (DG) methods which are unable to exploit unlabeled data perform poorly compared to semi-supervised learning (SSL) methods under SSDG setting.
Why Not Use Your Textbook? Knowledge-Enhanced Procedure Planning of Instructional Videos
This knowledge, sourced from training procedure plans and structured as a directed weighted graph, equips the agent to better navigate the complexities of step sequencing and its potential variations.
Improving Pseudo-labelling and Enhancing Robustness for Semi-Supervised Domain Generalization
A key challenge, faced by the best-performing SSL-based SSDG methods, is selecting accurate pseudo-labels under multiple domain shifts and reducing overfitting to source domains under limited labels.
Unified Spatio-Temporal Tri-Perspective View Representation for 3D Semantic Occupancy Prediction
Holistic understanding and reasoning in 3D scenes play a vital role in the success of autonomous driving systems.
Domain Adaptive Object Detection via Balancing Between Self-Training and Adversarial Learning
Deep learning based object detectors struggle generalizing to a new target domain bearing significant variations in object and background.
Cal-DETR: Calibrated Detection Transformer
Third, we develop a logit mixing approach that acts as a regularizer with detection-specific losses and is also complementary to the uncertainty-guided logit modulation technique to further improve the calibration performance.
Generalizing to Unseen Domains in Diabetic Retinopathy Classification
Therefore, automated diabetic retinopathy classification using deep learning techniques has gained interest in the medical imaging community.
Noise-Tolerant Few-Shot Unsupervised Adapter for Vision-Language Models
NtUA works as a key-value cache that formulates visual features and predicted pseudo-labels of the few unlabelled target samples as key-value pairs.
Unsupervised Landmark Discovery Using Consistency Guided Bottleneck
In the current work, we introduce a consistency-guided bottleneck in an image reconstruction-based pipeline that leverages landmark consistency, a measure of compatibility score with the pseudo-ground truth to generate adaptive heatmaps.
Multiclass Alignment of Confidence and Certainty for Network Calibration
It is based on the observation that a model miscalibration is directly related to its predictive certainty, so a higher gap between the mean confidence and certainty amounts to a poor calibration both for in-distribution and out-of-distribution predictions.
Exploring the Transfer Learning Capabilities of CLIP in Domain Generalization for Diabetic Retinopathy
In this study, we investigate CLIP's transfer learning capabilities and its potential for cross-domain generalization in diabetic retinopathy (DR) classification.
Unsupervised Deep Graph Matching Based on Cycle Consistency
We contribute to the sparsely populated area of unsupervised deep graph matching with application to keypoint matching in images.
Multiclass Confidence and Localization Calibration for Object Detection
Surprisingly, very little to no attempts have been made in studying the calibration of object detection methods, which occupy a pivotal space in vision-based security-sensitive, and safety-critical applications.
Bridging Precision and Confidence: A Train-Time Loss for Calibrating Object Detection
Since the original formulation of our loss depends on the counts of true positives and false positives in a minibatch, we develop a differentiable proxy of our loss that can be used during training with other application-specific loss functions.
Single-branch Network for Multimodal Training
With the rapid growth of social media platforms, users are sharing billions of multimedia posts containing audio, images, and text.
MSI: Maximize Support-Set Information for Few-Shot Segmentation
To extract information relevant to the target class, a dominant approach in best-performing FSS methods removes background features using a support mask.
Ranked #3 on
Few-Shot Semantic Segmentation
on FSS-1000 (1-shot)
Towards Improving Calibration in Object Detection Under Domain Shift
To this end, we first propose a new, plug-and-play, train-time calibration loss for object detection (coined as TCD).
Learning Branched Fusion and Orthogonal Projection for Face-Voice Association
In addition, we leverage cross-modal verification and matching tasks to analyze the impact of multiple languages on face-voice association.
Self-Distilled Vision Transformer for Domain Generalization
Similar to CNNs, ViTs also struggle in out-of-distribution scenarios and the main culprit is overfitting to source domains.
Video Instance Segmentation via Multi-scale Spatio-temporal Split Attention Transformer
When using the ResNet50 backbone, our MS-STS achieves a mask AP of 50. 1 %, outperforming the best reported results in literature by 2. 7 % and by 4. 8 % at higher overlap threshold of AP_75, while being comparable in model size and speed on Youtube-VIS 2019 val.
HM: Hybrid Masking for Few-Shot Segmentation
A fundamental limitation of FM is the inability to preserve the fine-grained spatial details that affect the accuracy of segmentation mask, especially for small target objects.
Ranked #5 on
Few-Shot Semantic Segmentation
on FSS-1000 (1-shot)
Generative Cooperative Learning for Unsupervised Video Anomaly Detection
Video anomaly detection is well investigated in weakly-supervised and one-class classification (OCC) settings.
Transformers in Medical Imaging: A Survey
Following unprecedented success on the natural language tasks, Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results and prompting researchers to reconsider the supremacy of convolutional neural networks (CNNs) as {de facto} operators.
Fusion and Orthogonal Projection for Improved Face-Voice Association
Prior works adopt pairwise or triplet loss formulations to learn an embedding space amenable for associated matching and verification tasks.
Visual Object Tracking with Discriminative Filters and Siamese Networks: A Survey and Outlook
Accurate and robust visual object tracking is one of the most challenging and fundamental computer vision problems.
SSAL: Synergizing between Self-Training and Adversarial Learning for Domain Adaptive Object Detection
In this paper, we propose to leverage model’s predictive uncertainty to strike the right balance between adversarial feature alignment and class-level alignment.
Synergizing between Self-Training and Adversarial Learning for Domain Adaptive Object Detection
In this paper, we propose to leverage model predictive uncertainty to strike the right balance between adversarial feature alignment and class-level alignment.
Rich Semantics Improve Few-shot Learning
Human learning benefits from multi-modal inputs that often appear as rich semantics (e. g., description of an object's attributes while learning about it).
Ranked #1 on
Few-Shot Image Classification
on Oxford 102 Flower
(using extra training data)
Deep Contextual Attention for Human-Object Interaction Detection
Our approach outperforms the state-of-the-art on all datasets.
Mask-Guided Attention Network for Occluded Pedestrian Detection
Our approach obtains an absolute gain of 9. 5% in log-average miss rate, compared to the best reported results on the heavily occluded (HO) pedestrian set of CityPersons test set.
AnimalWeb: A Large-Scale Hierarchical Dataset of Annotated Animal Faces
Several studies show that animal needs are often expressed through their faces.
Pushing the boundaries of audiovisual word recognition using Residual Networks and LSTMs
A further analysis on the utility of target word boundaries is provided, as well as on the capacity of the network in modeling the linguistic context of the target word.
Synergy Between Face Alignment and Tracking via Discriminative Global Consensus Optimization
Tracking-by-detection is drift-free but results in low accuracy fittings.
TRIC-track: Tracking by Regression With Incrementally Learned Cascades
This paper proposes a novel approach to part-based tracking by replacing local matching of an appearance model by direct prediction of the displacement between local image patches and part locations.
