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Found 6 papers, 5 papers with code
JMMMU: A Japanese Massive Multi-discipline Multimodal Understanding Benchmark for Culture-aware Evaluation
Accelerating research on Large Multimodal Models (LMMs) in non-English languages is crucial for enhancing user experiences across broader populations.
Unsolvable Problem Detection: Evaluating Trustworthiness of Vision Language Models
This paper introduces a novel and significant challenge for Vision Language Models (VLMs), termed Unsolvable Problem Detection (UPD).
Can Pre-trained Networks Detect Familiar Out-of-Distribution Data?
We consider that such data may significantly affect the performance of large pre-trained networks because the discriminability of these OOD data depends on the pre-training algorithm.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
LoCoOp: Few-Shot Out-of-Distribution Detection via Prompt Learning
CLIP's local features have a lot of ID-irrelevant nuisances (e. g., backgrounds), and by learning to push them away from the ID class text embeddings, we can remove the nuisances in the ID class text embeddings and enhance the separation between ID and OOD.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Zero-Shot In-Distribution Detection in Multi-Object Settings Using Vision-Language Foundation Models
First, images should be collected using only the name of the ID class without training on the ID data.
Rethinking Rotation in Self-Supervised Contrastive Learning: Adaptive Positive or Negative Data Augmentation
The semantics of an image can be rotation-invariant or rotation-variant, so whether the rotated image is treated as positive or negative should be determined based on the content of the image.
