Semi-Supervised Gaze Estimation via Disentangled Subspace Contrastive Learning

arXiv — cs.CV•Wednesday, May 27, 2026 at 4:00:00 AM

NeutralArtificial Intelligence

What Happened
A new study presents a semi-supervised learning architecture for gaze estimation, addressing the challenges of limited annotated samples and dataset diversity. By leveraging unlabeled data and employing Jacobian regularization, the model aims to enhance domain generalization and reduce the need for extensive manual annotations. This approach focuses on disentangling feature representations into specific gaze components, such as pitch and yaw angles, to improve accuracy.
Why It Matters
This development is significant as it offers a more efficient method for gaze estimation, which is crucial for applications in human-computer interaction and augmented reality. By minimizing reliance on labor-intensive data labeling, the research could accelerate advancements in AI systems that require accurate gaze tracking.
The Bigger Picture
The study aligns with ongoing efforts in the AI field to improve model robustness through innovative learning techniques. It reflects a broader trend towards utilizing unlabeled data and enhancing model performance in real-world scenarios, as seen in recent works exploring visual object tracking and gaze following frameworks, which also emphasize the importance of understanding human behavior in AI applications.

— via World Pulse Now AI Editorial System

Read Original

Was this article worth reading? Share it

Continue Readings

DEV Community21 hours ago

Coding-Agent Misalignment: Turn Failure Taxonomies into QA Checks

NeutralArtificial Intelligence

GitHub's Copilot cloud agent and OpenAI's Codex integration represent a significant evolution in coding agents, enabling them to research repositories, create implementation plans, and execute code changes autonomously. A recent arXiv paper highlights the importance of understanding how these agents can misalign with developer intent, emphasizing the need for teams to detect deviations before code reaches production.

Read full article

via DEV Community

arXiv — cs.LG2 days ago

Self-Attention as Transport: Limits of Symmetric Spectral Diagnostics

NeutralArtificial Intelligence

A recent study titled 'Self-Attention as Transport: Limits of Symmetric Spectral Diagnostics' examines the limitations of attention routing in language models, particularly when processing hallucinated responses. The research highlights two failure shapes: over-concentration on specific positions and excessive diffusion of attention, both of which provide diagnostic signals derived from attention matrices during forced scoring of benchmark responses.

Read full article

via arXiv — cs.LG

arXiv — cs.CL2 days ago

UR-BERT: Scaling Text Encoders for Massively Multilingual TTS Through Universal Romanization and Speech Token Prediction

PositiveArtificial Intelligence

Researchers have introduced UR-BERT, a novel text-to-speech (TTS) encoder designed to support massively multilingual systems by utilizing a unified Romanization representation, enabling it to scale to 495 languages. This approach overcomes the limitations of traditional grapheme-to-phoneme methods, which are restricted to about 100 languages due to resource availability. The encoder also incorporates a speech token prediction objective to enhance phonetic accuracy and text-speech alignment during training.

Read full article

via arXiv — cs.CL

arXiv — cs.CL2 days ago

The Long Tail, Not the Front Page: Cold-Start Prediction of Crowd Highlight Salience

NeutralArtificial Intelligence

A recent study published on arXiv explores the cold-start prediction of crowd highlight salience, revealing that a logistic ranker model trained on highlight data can outperform a baseline model in predicting which passages will be marked by readers. The findings indicate a small but significant improvement in average precision, suggesting that models can effectively predict reader engagement before actual highlights are accumulated.

Read full article

via arXiv — cs.CL

arXiv — cs.LG2 days ago

Projected random forests and conformal prediction of circular data

NeutralArtificial Intelligence

A recent study published on arXiv explores the application of conformal prediction techniques to regression problems involving circular responses, demonstrating how these methods can produce adaptive prediction sets with finite-sample coverage guarantees. The research highlights a projection procedure that transforms linear-response regression models into those suitable for circular data, particularly when using random forests as base models.

Read full article

via arXiv — cs.LG

arXiv — cs.LG2 days ago

What Uncertainties Do We Need for Dynamical Systems?

NeutralArtificial Intelligence

A new paper titled 'What Uncertainties Do We Need for Dynamical Systems?' has been released on arXiv, focusing on the distinction between aleatoric and epistemic uncertainty in the context of machine learning, particularly for dynamical systems. The authors explore various sources of uncertainty and their implications for different tasks within this field.

Read full article

via arXiv — cs.LG

arXiv — cs.CV2 days ago

CineDance: Towards Next-Generation Multi-Shot Long-Form Cinematic Audio-Video Generation

NeutralArtificial Intelligence

The introduction of CineDance-1M marks a significant advancement in the field of audio-video generation, providing a large-scale, open research dataset specifically designed for multi-shot, long-form cinematic narratives. This dataset features an average duration of 92.8 seconds and includes 24.2 continuous shots per video, supported by a rigorous curation process that enhances the quality of both audio and video modalities.

Read full article

via arXiv — cs.CV

arXiv — cs.LG2 days ago

Signed Compression Progress on a Sealed Audit is Goodhart-Resistant

NeutralArtificial Intelligence

A recent study published on arXiv presents a significant advancement in intrinsic motivation for AI agents, demonstrating that signed compression progress on a sealed audit is resistant to Goodhart's law. The research establishes that rewarding agents based on their learning improvements leads to a direct correlation between cumulative rewards and actual audit performance, preventing indefinite reward inflation.

Read full article

via arXiv — cs.LG

Ready to build your own newsroom?

Subscribe to unlock a personalised feed, podcasts, newsletters, and notifications tailored to the topics you actually care about