Daily TMLR digest for Jan 04, 2026
TMLR
Accepted papers
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Title: Federated Multimodal Fusion for Action Recognition Leveraging Vision-Language Embeddings and Spatio- Temporal CNNs
Authors: Aditi Palit, Kalidas Yeturu
Abstract: Federated learning (FL) for Video Action Recognition (VAR) faces significant challenges in balancing privacy preservation, communication efficiency, and model performance. This paper introduces FLAMeST (Federated Learning for Action Recognition with Multimodal embeddings and Spacio-Temporal Fusion), a FL framework that synergizes Vision-Language Models (VLMs) and spatiotemporal CNNs to address these challenges. Unlike existing works that use BLIP (VLM) solely for caption generation, FLAMeST leverages BLIP in a dual manner. To enhance temporal modeling, complementary spatiotemporal features are extracted using a pre-trained 3D CNN (Slow network). These semantic (BLIP) and motion (Slow) embeddings are concatenated into a unified representation to train a lightweight Multi-Layer Perceptron (MLP). Within the FL paradigm, only the MLP parameters are shared with the server, ensuring raw video data and generated captions remain local. FLAMeST employs the FedAvg algorithm for model aggregation, achieving 99%(↓) lower communication overhead compared to full-model training. Experiments on UCF101 and HMDB51 datasets demonstrate the framework’s robustness, achieving improved accuracies of 5.13%(↑) and 2.71%(↑), respectively, against the baseline.
URL: https://openreview.net/forum?id=AobzdtqiMe
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New submissions
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Title: TabKDE: Simple and Scalable Tabular Data Generation with Kernel Density Estimates
Abstract: Tabular data generation considers a large table with multiple columns -- each column comprised of numerical, categorical, or sometimes ordinal values. The goal is to produce new rows for the table that replicate the distribution of rows from the original data -- without just copying those initial rows. The last 3 years has seen enormous progress on this problem, mostly using computational expensive methods that employ one-hot encoding, VAEs, and diffusion.
This paper describes a new approach to the problem of tabular data generation. By employing copula transformations and modeling the distribution as a kernel density estimate we can nearly match the accuracy and privacy-preservation achievements of the previous methods, but with almost no training time. Our method is very scalable, and can be run on data sets orders of magnitude larger than prior state-of-the-art on a simple laptop. Moreover, because we employ kernel density estimates, we can store the model as a coreset of the original data -- we believe the first for generative modeling -- and as a result, require significantly less space as well. Our code is available here: http://github.com/tabkde/tabkde-main
URL: https://openreview.net/forum?id=1eH8K8EAvM
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