Abaka AI ✈️ CVPR

Human Intelligence Data For Frontier AI | Datasets | Annotation | Evaluation
Daily data insights

[Abaka AI ✈️ CVPR @AbakaAI_Tech]

🚨CVPR 2026 Accepted
ChartNet: A Million-Scale, High-Quality Multimodal Dataset for Robust Chart Understanding
in collaboration with @MIT & @IBM

1.5M-sample open-source dataset for robust chart understanding
Each sample aligns a chart image, plotting code, CSV/table data, natural-language summary, and QA with reasoning

Full breakdown👇:
#CVPR26 #CVPR2026

[Abaka AI ✈️ CVPR @AbakaAI_Tech]

@sainingxie Amazing work! Temporal understanding >>>>> static recognition. Always has been

[Abaka AI ✈️ CVPR @AbakaAI_Tech]

@sihyun_yu Great benchmark! Really solid work! Perception is all we need

[Abaka AI ✈️ CVPR @AbakaAI_Tech]

@jeffclune @CVPR Nice! Will bring a lot of perspective on autonomy and fine‑grained categories
Excited to chat at the conference!

[Abaka AI ✈️ CVPR @AbakaAI_Tech]

✈️We're heading to #CVPR2026

🏄Booth #701 with free swags
📡DataMFM workshop with insights and prizes
🪩A Happy Hour with food, drinks, and relaxed chats

DataMFM workshop
June 3rd, Room 111, 1:00–6:00 PM

Happy Hour (spots are limited)
June 6th

Don’t forget to pass by and bring your perspective
DataMFM: https://t.co/2oC6hkyNVN
Happy hour, register now: https://t.co/OXv5gmlvcg

[Abaka AI ✈️ CVPR @AbakaAI_Tech]

@_catwu Huge!
Quick question: how does verification state survive the fan-out? When a branch returns a partial result, does Claude treat that as a local retry, a dependency update for sibling agents, or a replan of the whole workflow?

Great work! Thanks for sharing!

[Abaka AI ✈️ CVPR @AbakaAI_Tech]

@henrytdowling Got it, makes sense, much easier to trust the run when the tree is part of the system

[Abaka AI ✈️ CVPR @AbakaAI_Tech]

@astrogu_ @lateinteraction @MIT_CSAIL @nlp_mit @samrmadden @qizhengz_alex @Stanford Amazing work! Love the cache policy angle
Chart workflows have this annoying split-brain thing where the plot, CSV, and code all know the truth, but different parts
Curious how PEEK handles contexts that are partly visual and partly executable

[Abaka AI ✈️ CVPR @AbakaAI_Tech]

@Guilherme_PT1 Great job! What happens on chart-to-table or chart-to-code style tasks

[Abaka AI ✈️ CVPR @AbakaAI_Tech]

@venna_kesav @iiit_hyderabad Congrats, Venkata!
segmentation-based grounding is such a useful direction for doc QA

[Abaka AI ✈️ CVPR @AbakaAI_Tech]

@vikataravi bleed suppression loss is very smart! Thanks for sharing!
we’ve hit the chart version of this with legends, axes, and tiny series overlaps. pixel grounding helps, but tying the visual back to CSV/code...
curious how M3Grounder behaves on chart-heavy docs!

[Abaka AI ✈️ CVPR @AbakaAI_Tech]

@_cagarwal @CVPR So we've been paying O(n) for visual depth when O(1) would often do. That's a big efficiency win, huge implications for real-time VLMs!

[Abaka AI ✈️ CVPR @AbakaAI_Tech]

@RXed_EU Totally right, saving this

[Abaka AI ✈️ CVPR @AbakaAI_Tech]

@jerryjliu0 Grid anchoring is very clever! Love the geometry

[Abaka AI ✈️ CVPR @AbakaAI_Tech]

ChartNet: a 1.5M-sample open-source dataset for chart understanding

With an aligned chart image, plotting code, CSV/table data, summary, and QA with reasoning,
Improves chart reconstruction, data extraction, summarization, and chart QA across model sizes.

Huge thanks to respected collaborators @kondic_jovana, @RogerioFeris, @AudeOliva, @ZihanWang123, and everyone involved!

The dataset is out on Hugging Face.
Test it, break it, and we'll see you at #CVPR2026 to discuss!

[Abaka AI ✈️ CVPR @AbakaAI_Tech]

🚨CVPR 2026 Accepted
ChartNet: A Million-Scale, High-Quality Multimodal Dataset for Robust Chart Understanding
in collaboration with @MIT & @IBM

1.5M-sample open-source dataset for robust chart understanding
Each sample aligns a chart image, plotting code, CSV/table data, natural-language summary, and QA with reasoning

Full breakdown👇:
#CVPR26 #CVPR2026

[Abaka AI ✈️ CVPR @AbakaAI_Tech]

@BKLEE_NANO Overthinking is an agent's worst enemy indeed! Great work on AXPO!

[Abaka AI ✈️ CVPR @AbakaAI_Tech]

@adithya_s_k Huge drop! Multilingual OCR data of this size is very rare