The field of computer vision and machine learning is witnessing significant developments in multi-modal learning and object tracking. Researchers are exploring innovative approaches to improve the robustness and accuracy of models in various applications, including object re-identification, human activity recognition, and long-term multi-object tracking. A key trend is the use of statistical models and attention mechanisms to align modality-specific representations and detect outliers. Another area of focus is the development of frameworks that can handle uncertain identities and sparse data, enabling more accurate tracking and recognition in real-world scenarios. Noteworthy papers in this area include: Similarity-based Outlier Detection for Noisy Object Re-Identification Using Beta Mixtures, which proposes a novel statistical outlier detection framework for object re-identification. D-CAT: Decoupled Cross-Attention Transfer between Sensor Modalities for Unimodal Inference, which introduces a framework for cross-modal transfer learning without requiring paired sensor data during inference. An HMM-based framework for identity-aware long-term multi-object tracking from sparse and uncertain identification, which combines uncertain identities and tracking using a Hidden Markov Model formulation. Dual-Stage Reweighted MoE for Long-Tailed Egocentric Mistake Detection, which proposes a dual-stage reweighted mixture-of-experts framework for detecting mistakes in egocentric video data. RLBind: Adversarial-Invariant Cross-Modal Alignment for Unified Robust Embeddings, which introduces a two-stage adversarial-invariant cross-modal alignment framework for robust unified embeddings.