AI, ML and Computer Vision Meetup - September 24, 2026

Join our virtual meetup on September 24 to hear talks from experts on cutting-edge topics across AI, ML, and computer vision. View more CV events here.

This presentation provides a detailed workflow related to coffee yield estimation in a dense environment. With photos of pre-harvest coffee plants from a couple of coffee estates, details related to pre-processing, annotation to detect regions of interest (ROI), object detection training and inferencing results with various Yolo models and finally segmentation with SAM2 and Yolo*-seg with training and inference results to determine the count of raw, pre-mature, mature and over-mature coffee berries and finally the yield of the entire estate.

All this is based on real world data captured on iPhone and android phones.

Patch-based tokenization has become the default interface between vision encoders and downstream models, yet patches carry no semantic structure and scale poorly with resolution and temporal extent. This talk presents a research program centered on replacing patch tokens with region-level representations — semantically dense tokens grounded in visual entities rather than arbitrary grid crops.

I will describe RELOCATE, REN, and T-REN, a progression of methods that produce region tokens via pooling, train them with region-level objectives, and extend them to video with temporal coherence. I will then present ongoing work integrating region tokens into VLMs to directly expand visual context capacity, and preliminary results on future region trajectory prediction as a foundation for world modeling.

The broader thesis is that region-level tokens are a more natural unit of visual computation than patches, and their advantage compounds as task complexity, resolution, and temporal horizon increase.

Image collections are humans' primary way of capturing the world, yet advances in generative editing remain largely inapplicable to this modality. We address this gap by introducing Match-and-Fuse - a zero-shot, training-free method for consistent set-to-set generation from image collections that share a common visual element but differ in viewpoint, capture time, and surrounding content.

Our key idea is a unified graph-based framework that combines dense correspondences with an emergent prior in text-to-image diffusion models to generate coherent canvases. We achieve state-of-the-art consistency and visual quality, and unlock new creative capabilities for content generation.