Sajid Javed presents Marine Image Analysis, Datasets, and Applications (! Date change !)

The preservation of aquatic biodiversity is critical in mitigating the
effects of climate change. Aquatic scene understanding plays a pivotal
role in aiding marine scientists in their decision-making processes. In
this paper, I will talk about AquaticCLIP, a novel contrastive
language-image pre-training model tailored for aquatic scene
understanding. AquaticCLIP presents a new unsupervised learning framework
that aligns images and texts in aquatic environments, enabling tasks such
as segmentation, classification, detection, and object counting. By
leveraging our large-scale underwater image-text paired dataset without
the need for ground-truth annotations, our model enriches existing
vision-language models in the aquatic domain. For this purpose, we
construct a 2 million underwater image-text paired dataset using
heterogeneous resources including YouTube, Netflix, NatGeo, etc. To
fine-tune AquaticCLIP, we propose a prompt-guided vision encoder that
progressively aggregates patch features via learnable prompts, while a
vision-guided mechanism enhances the language encoder by incorporating
visual context. The model is optimized through a contrastive pre-
training loss to align visual and textual modalities. AquaticCLIP
achieves notable performance improvements in zero-shot settings across
multiple underwater computer vision tasks, outperforming existing methods
in both robustness and interpretability. Our model sets a new benchmark
for vision-language applications in underwater environments. The code and
dataset for AquaticCLIP are publicly available