Posters

Low-rank block trifocal tensor unlocks accurate, efficient camera pose synchronization.

ConVRT: A novel framework restores turbulence-distorted videos by decoupling spatial and temporal information in a neural representation, achieving temporally consistent mitigation.

Artificial Dopamine (AD) algorithm achieves comparable performance to backpropagation methods in complex RL tasks by using only synchronously distributed per-layer TD errors, demonstrating the suffici…

RaTSG network tackles Temporal Sentence Grounding with Relevance Feedback (TSG-RF) by discerning query relevance at multiple granularities before selectively grounding segments.

Temp-G³NTK: a novel temporal graph neural tangent kernel guarantees convergence to graphon NTK, offering superior performance in temporal graph classification and node-level tasks.

This research introduces a template-free articulated Gaussian splatting method for real-time dynamic view synthesis, automatically discovering object skeletons from videos to enable reposing.

VATT: Text-guided video-to-audio generation, enabling refined audio control via text prompts and improved compatibility.

Team-Fictitious Play (Team-FP) enables self-interested agents to learn near-optimal team coordination in multi-team games, reaching a Team-Nash equilibrium with quantifiable error bounds.

Smart prompt engineering is key to unlocking LLMs’ full potential. This paper reveals that cleverly selecting examples (exemplar optimization) can outperform optimizing instructions alone, even with S…

AdaGauss tackles task-recency bias in exemplar-free class incremental learning by adapting class covariances and introducing an anti-collapse loss, achieving state-of-the-art results.

Task-oriented time series imputation is revolutionized! This research introduces a novel approach that efficiently assesses imputation strategies based on downstream task performance without costly mo…

PSPS: a novel task-agnostic framework enables valid and efficient ML-assisted statistical inference for virtually any task, simply using summary statistics from existing analysis routines!

Researchers unveil the Infeasibility Theorem, proving optimal class-incremental learning is impossible with discriminative models due to task confusion, and the Feasibility Theorem, showing generative…

TARSS-Net: A novel temporal-aware radar semantic segmentation network uses a data-driven approach to aggregate temporal information, enhancing accuracy and performance.

TARP-VP reveals a surprising lack of trade-off between adversarial robustness and privacy for label mapping visual prompting models, showing that transferred adversarial training significantly improve…

Adaptive experiment design optimizes indirect experiments in complex systems by sequentially narrowing the gap between upper and lower bounds on a targeted query, providing more efficient and informat…

APCT: a novel architecture enhances 3D point cloud recognition by using an adversarial feature erasing mechanism to improve global structure capture and robustness against real-world corruptions.

TAPTRv2 enhances point tracking by introducing an attention-based position update, eliminating cost-volume reliance for improved accuracy and efficiency.

Tangent Space Causal Inference (TSCI) enhances causal discovery in dynamical systems by leveraging vector fields, outperforming existing methods in accuracy and interpretability.

LoTNext framework tackles human mobility prediction’s long-tail problem by using graph and loss adjustments to improve the accuracy of predicting less-visited locations.