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Tulane University is a private research university in New Orleans known for its strengths in law, public health, and environmental studies. It integrates academic excellence with community-focused research. NECLA collaborated with Tulane University on novel approaches to disentangled representation learning in generative networks. We introduced the AE-StyleGAN model, improved training methods for style-based generators, offering more control over visual output and boosting performance across a range of image synthesis tasks.

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Weakly-Supervised Temporal Action Localization with Multi-Modal Plateau Transformers

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Weakly Supervised Temporal Action Localization (WSTAL) aims to jointly localize and classify action segments in untrimmed videos with only video level annotations. To leverage video level annotations most existing methods adopt the multiple instance learning paradigm where frame/snippet level action predictions are first produced and then aggregated to form a video-level prediction. Although there are trials to improve snippet-level predictions by modeling temporal relationships we argue that those implementations have not sufficiently exploited such information. In this paper we propose Multi Modal Plateau Transformers (M2PT) for WSTAL by simultaneously exploiting temporal relationships among snippets complementary information across data modalities and temporal coherence among consecutive snippets. Specifically M2PT explores a dual Transformer architecture for RGB and optical flow modalities which models intra modality temporal relationship with a self attention mechanism and inter modality temporal relationship with a cross attention mechanism. To capture the temporal coherence that consecutive snippets are supposed to be assigned with the same action M2PT deploys a Plateau model to refine the temporal localization of action segments. Experimental results on popular benchmarks demonstrate that our proposed M2PT achieves state of the art performance.

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Few-Shot Video Classification via Representation Fusion and Promotion Learning

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Recent few-shot video classification (FSVC) works achieve promising performance by capturing similarity across support and query samples with different temporal alignment strategies or learning discriminative features via Transformer block within each episode. However, they ignore two important issues: a) It is difficult to capture rich intrinsic action semantics from a limited number of support instances within each task. b) Redundant or irrelevant frames in videos easily weaken the positive influence of discriminative frames. To address these two issues, this paper proposes a novel Representation Fusion and Promotion Learning (RFPL) mechanism with two sub-modules: meta-action learning (MAL) and reinforced image representation (RIR). Concretely, during training stage, we perform online learning for seeking a task-shared meta-action bank to enrich task-specific action representation by injecting global knowledge. Besides, we exploit reinforcement learning to obtain the importance of each frame and refine the representation. This operation maximizes the contribution of discriminative frames to further capture the similarity of support and query samples from the same category. Our RFPL framework is highly flexible that it can be integrated with many existing FSVC methods. Extensive experiments show that RFPL significantly enhances the performance of existing FSVC models when integrated with them.

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Personalized Semantics Excitation for Federated Image Classification

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Federated learning casts a light on the collaboration of distributed local clients with privacy protected to attain a more generic global model. However, significant distribution shift in input/label space across different clients makes it challenging to well generalize to all clients, which motivates personalized federated learning (PFL). Existing PFL methods typically customize the local model by fine-tuning with limited local supervision and the global model regularizer, which secures local specificity but risks ruining the global discriminative knowledge. In this paper, we propose a novel Personalized Semantics Excitation (PSE) mechanism to breakthrough this limitation by exciting and fusing personalized semantics from the global model during local model customization. Specifically, PSE explores channel-wise gradient differentiation across global and local models to identify important low-level semantics mostly from convolutional layers which are embedded into the client-specific training.In addition, PSE deploys the collaboration of global and local models to enrich high-level feature representations and facilitate the robustness of client classifier through a cross-model attention module. Extensive experiments and analysis on various image classification benchmarks demonstrate the effectiveness and advantage of our method over the state-of-the-art PFL methods.

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