Collaborative Attention
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Related Articles from SNS
HeedVision: Attention Awareness in Collaborative Immersive Analytics Environments
arXiv:2505.07069v3 Announce Type: replace Abstract: Group awareness--the ability to perceive the activities of collaborators in a shared space--is a vital mechanism to support effective coordination and joint data analysis in collaborative visualization. We introduce collaborative attention-aware visualizations (CAAVs) that track, record, and revisualize the collective attention of multiple users over time. We implement this concept in HeedVision, a standards-compliant WebXR system built...
LoCAtion: Long-time Collaborative Attention Framework for High Dynamic Range Video Reconstruction
arXiv:2603.14377v2 Announce Type: replace Abstract: Prevailing High Dynamic Range (HDR) video reconstruction methods are fundamentally trapped in a fragile alignment-and-fusion paradigm. While explicit spatial alignment can successfully recover fine details in controlled environments, it becomes a severe bottleneck in unconstrained dynamic scenes. By forcing rigid alignment across unpredictable motions and varying exposures, these methods inevitably translate registration errors into severe...
Reconstructing Content with Collaborative Attention for Universal Multimodal Representation Learning
arXiv:2603.01471v3 Announce Type: replace Abstract: Multimodal embedding models, rooted in multimodal large language models (MLLMs), have yielded significant performance improvements across diverse tasks such as retrieval and classification. However, most existing approaches rely heavily on large-scale contrastive learning, with limited exploration of how the architectural and training paradigms of MLLMs affect embedding quality. While effective for generation, the causal attention and...
Reconstructing Content via Collaborative Attention to Improve Multimodal Embedding Quality
arXiv:2603.01471v2 Announce Type: replace Abstract: Multimodal embedding models, rooted in multimodal large language models (MLLMs), have yielded significant performance improvements across diverse tasks such as retrieval and classification. However, most existing approaches rely heavily on large-scale contrastive learning, with limited exploration of how the architectural and training paradigms of MLLMs affect embedding quality. While effective for generation, the causal attention and...
SAILRec: Steering LLM Attention to Dual-Side Semantically Aligned Collaborative Embeddings for Recommendation
arXiv:2606.04514v1 Announce Type: new Abstract: Recent LLM-based recommenders enhance language models with collaborative embeddings from user-item interactions, but making such embeddings available does not ensure their proper use during inference. Through a diagnostic attention analysis, we find that the utilization of collaborative embeddings is depth-dependent and alignment-sensitive, suggesting that LLMs need to balance their internal semantic knowledge with external collaborative...
Federated Learning for Multi-Center Sepsis Early Prediction with Privacy-Preserving
new Abstract: Privacy-sensitive and distributed characteristics of multi-center medical data bring severe obstacles to centralized modeling for accurate early prediction of sepsis. Federated learning (FL) has attracted growing attention as a promising framework for collaborative model development, as it allows multiple institutions to jointly train predictive models without directly sharing or centralizing raw data. Nevertheless, its practical performance, robustness, and privacy-preserving...
Anthropic expands Mythos to 150 additional organizations in more than 15 countries
Anthropic on Tuesday said it's expanding Project Glasswing to an additional 150 partners in more than 15 countries. The startup said the expansion includes industries that weren't well-represented in the initial launch, such as power, water, healthcare, communications, and hardware. New partners will need to meet security requirements before gaining access to its Mythos model.
The Attention-Aware Pipeline: Design Tensions from Making Attention Visible in XR
Announce Type: new Abstract: Where people look during shared activity carries coordination cues that speech and gesture cannot replace, but these patterns remain invisible to participants. XR headsets make gaze available as real-time input, yet few systems feed it back visually. We frame our work using the Attention-Aware Pipeline (Capture, Record, Revisualize), whose feedback loop means the systems visual response alters what users attend to next, triggering further responses.
SFMambaNet: Spectral-Frequency Enhanced Selective State Space Model for Correspondence Pruning
arXiv:2606.04493v1 Announce Type: new Abstract: Correspondence pruning aims to identify inliers from an initial set of correspondences. Most existing Graph Neural Network (GNN)-based methods rely on geometric features mapped from coarse Euclidean coordinates, which struggle to capture the subtle geometric consistencies presented by inliers. While Mamba-based methods possess global receptive fields and long sequence modeling capabilities, they tend to accumulate substantial inconsistent...