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Found 6 papers, 1 papers with code
Enhancing Conceptual Understanding in Multimodal Contrastive Learning through Hard Negative Samples
Current multimodal models leveraging contrastive learning often face limitations in developing fine-grained conceptual understanding.
dacl1k: Real-World Bridge Damage Dataset Putting Open-Source Data to the Test
Recognising reinforced concrete defects (RCDs) is a crucial element for determining the structural integrity, traffic safety and durability of bridges.
dacl10k: Benchmark for Semantic Bridge Damage Segmentation
Reliably identifying reinforced concrete defects (RCDs)plays a crucial role in assessing the structural integrity, traffic safety, and long-term durability of concrete bridges, which represent the most common bridge type worldwide.
Ranked #1 on
Semantic Segmentation
on dacl10k v1 testfinal
Probing the Role of Positional Information in Vision-Language Models
Our results thus highlight an important issue of multimodal modeling: the mere presence of information detectable by a probing classifier is not a guarantee that the information is available in a cross-modal setup.
Less Is More: Linear Layers on CLIP Features as Powerful VizWiz Model
Current architectures for multi-modality tasks such as visual question answering suffer from their high complexity.
Building Inspection Toolkit: Unified Evaluation and Strong Baselines for Damage Recognition
In recent years, several companies and researchers have started to tackle the problem of damage recognition within the scope of automated inspection of built structures.
