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Found 4 papers, 1 papers with code
UniBench: Visual Reasoning Requires Rethinking Vision-Language Beyond Scaling
To facilitate a systematic evaluation of VLM progress, we introduce UniBench: a unified implementation of 50+ VLM benchmarks spanning a comprehensive range of carefully categorized capabilities from object recognition to spatial awareness, counting, and much more.
An Introduction to Vision-Language Modeling
Then, we present and discuss approaches to evaluate VLMs.
The Impact of Spatiotemporal Augmentations on Self-Supervised Audiovisual Representation Learning
Hence, we extensively investigate composition of temporal augmentations suitable for learning audiovisual representations; we find lossy spatio-temporal transformations that do not corrupt the temporal coherency of videos are the most effective.
CLAR: Contrastive Learning of Auditory Representations
We illustrate that by combining all these methods and with substantially less labeled data, our framework (CLAR) achieves significant improvement on prediction performance compared to supervised approach.
