Despite significant progress in generative AI, comprehensive evaluation remains
challenging because of the lack of effective metrics and standardized benchmarks. For …

While text-to-visual models now produce photo-realistic images and videos they struggle
with compositional text prompts involving attributes relationships and higher-order …

Multimodal Large Language Models (MLLMs) mimic human perception and reasoning
system by integrating powerful Large Language Models (LLMs) with various modality …

Vision language models (VLM) have demonstrated remarkable performance across various
downstream tasks. However understanding fine-grained visual-linguistic concepts such as …

The surge in multi-modal data has propelled cross-modal matching to the forefront of
research interest. However the challenge lies in the laborious and expensive process of …

For multimodal LLMs, the synergy of visual comprehension (textual output) and generation
(visual output) presents an ongoing challenge. This is due to a conflicting objective: for …

Abstract Contrastive Language-Image Pretraining (CLIP) models maximize the mutual
information between text and visual modalities to learn representations. This makes the …

Vision-language models (VLMs) are impactful in part because they can be applied to a
variety of visual understanding tasks in a zero-shot fashion, without any fine-tuning. We …

Fine-grained understanding of objects, attributes, and relationships between objects is
crucial for visual-language models (VLMs). To evaluate VLMs' fine-grained understanding …

Self-supervised contrastive learning models, such as CLIP, have set new benchmarks for
vision-language models in many downstream tasks. However, their dependency on rigid …