Best of 2024 in Vision [LS Live @ NeurIPS]
Dec 22, 2024 · 55:46
Isaac Robinson and Peter Robicheaux from Roboflow and Vik Korrapati from Moondream recap the biggest shifts in computer vision in 2024, naming Sora as the year's most significant paper despite being a blog post, and highlighting its replication efforts (OpenSora) that use MAGViT V2 and diffusion transformers. They detail how SAM 2 extends the SAM approach to video with a memory bank and real-time performance, and how DETR-based detectors (RT-DETR, LW-DETR, DeFine) now surpass YOLOs on COCO by 4.6 AP at the same latency. Peter explains the CLIP-blind phenomenon: MMVP shows leading VLMs (ChatGPT, Gemini, LLaVA) fail at fine-grained tasks like reading clock hands, but models like PaliGemma 2 and AimV2 improve by incorporating pixel-level understanding and autoregressive image reconstruction, with AimV2 approaching 60.2 mAP on COCO. Vik introduces Moondream's 0.5B parameter pruned VLM and demonstrates a grounded chain-of-thought approach that dramatically improves sample efficiency for analog gauge reading, reducing the need for millions of synthetic examples.
