5 papers · 1 filter
Hyper3-CLIP: Hierarchy-Conditioned Hyperbolic Vision-Language Training
Matin Mahmood, Antonio Rueda-Toicen, Mohamed ElBassat +3
CLIP-like vision-language models (VLMs) trained with contrastive objectives learn strong global image-text representations, but their Euclidean embeddings and global pooling fail t…
APEX4: Efficient Pure W4A4 LLM Inference via Intra-SM Compute Rebalancing
Hong Guo, Nianhui Guo, Weixing Wang +3
W4A4 quantization promises full utilization of INT4 Tensor Cores, yet group dequantization overhead on CUDA Cores has driven existing systems to mixed-precision fallbacks. We prese…
Beyond Accuracy: Benchmarking Cross-Task Consistency in Unified Multimodal Models
Weixing Wang, Liudvikas Zekas, Anton Hackl +5
Unified Multimodal Models (uMMs) aim to support both visual understanding and visual generation within a shared representation. However, existing evaluation protocols assess these…
Improving Neural Argumentative Stance Classification in Controversial Topics with Emotion-Lexicon Features
Mohammad Yeghaneh Abkenar, Weixing Wang, Manfred Stede +3
Argumentation mining comprises several subtasks, among which stance classification focuses on identifying the standpoint expressed in an argumentative text toward a specific target…
Can Large Language Models Generalize Procedures Across Representations?
Fangru Lin, Valentin Hofmann, Xingchen Wan +4
Large language models (LLMs) are trained and tested extensively on symbolic representations such as code and graphs, yet real-world user tasks are often specified in natural langua…