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Found 6 papers, 3 papers with code
X-VARS: Introducing Explainability in Football Refereeing with Multi-Modal Large Language Model
The rapid advancement of artificial intelligence has led to significant improvements in automated decision-making.
SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training?
We present SynthCLIP, a novel framework for training CLIP models with entirely synthetic text-image pairs, significantly departing from previous methods relying on real data.
Revisiting Test Time Adaptation under Online Evaluation
To address this issue, we propose a more realistic evaluation protocol for TTA methods, where data is received in an online fashion from a constant-speed data stream, thereby accounting for the method's adaptation speed.
CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
Our contributions include introducing a novel communicative agent framework, offering a scalable approach for studying the cooperative behaviors and capabilities of multi-agent systems, and open-sourcing our library to support research on communicative agents and beyond: https://github. com/camel-ai/camel.
SALA: Soft Assignment Local Aggregation for Parameter Efficient 3D Semantic Segmentation
Since it is learnable, this mapping is allowed to be different per layer instead of being applied uniformly throughout the depth of the network.
FFTLasso: Large-Scale LASSO in the Fourier Domain
Since all operations in our FFTLasso method are element-wise, the subproblems are completely independent and can be trivially parallelized (e. g. on a GPU).
