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Found 35 papers, 21 papers with code
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.
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.
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.
