Darshan Singh @thought2vec
Research @GoogleDeepMind | @iiit_hyderabad Joined October 2021-
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The goal of any good theory or new knowledge is to reduce uncertainty or entropy of a field. Given how many papers published each year related to machine/artificial intelligence and how many different opinions about this subject, I hope our new book is a denoiser, not a diffuser.
Excited to share I have joined CMU’s Robotics Institute as a Master’s student! The past few months were tough with visa uncertainties, but I’m happy to be finally here 😇 I look forward to working on native visual reasoning + other cool RL and VLM stuff 🚀 If you’re around…
Still can’t believe it. Our work on uncovering plagiarism in AI generated research received the outstanding paper award at ACL!! Effort led by, and envisioned by the amazing @tarungupta360!
Still can’t believe it. Our work on uncovering plagiarism in AI generated research received the outstanding paper award at ACL!! Effort led by, and envisioned by the amazing @tarungupta360!
A large contingent from IIITH’s Computer Vision Lab participated at the Conference on Vision and Pattern Recognition (CVPR) last month in Nashville. Read on about the cutting edge research that was presented and why it’s a big deal in the vision circles. blogs.iiit.ac.in/cvpr-2025/
Checkout this amazing work by my colleagues @Harman26Singh, @Pragya2k et al, on building robust LLM reward models by disentangling true attributes from spurious ones!!
Checkout this amazing work by my colleagues @Harman26Singh, @Pragya2k et al, on building robust LLM reward models by disentangling true attributes from spurious ones!!
Can text-to-image Diffusion models handle surreal compositions beyond their training distribution? 🚨 Introducing ComposeAnything — Composite object priors for diffusion models 📸 More faithful, controllable generations — no retraining required. 🔗zeeshank95.github.io/composeanythin… 1/9
🔔New @CVPR paper evaluating compositional reasoning of Video-LLMs on 10s, action-packed clips! 🥁 VELOCITI features 7 tests to disentangle and assess the comprehension of people, actions, and their associations across multiple events. katha-ai.github.io/projects/veloc… 🧵 1/9 #CVPR2025
Excited for Gemma 3 and to have contributed a bit to the effort! It’s a really strong model with great multilingual performance—including in Indian languages! 👇👇 Report link: storage.googleapis.com/deepmind-media…
No Detail Left Behind: Revisiting Self-Retrieval for Fine-Grained Image Captioning Manu Gaur, Darshan Singh S, Makarand Tapaswi. Action editor: Sanghyuk Chun. openreview.net/forum?id=gqh0y… #captioners #captioning #captioner
Dear all, @vikataravi's student (an only child to his parents) tragically passed away recently. The parents have no source of income. Ravikiran has set up a fundraiser on their behalf. Please donate. No contribution is too small. milaap.org/fundraisers/su…
Join us at 2nd EgoVis workshop @CVPR #CVPR2025 in #Nashville with 28 open challenges [DL 19 May] across datasets @ego4_d @meta_aria @HoloLens and EPIC-KITCHENS, w great keynotes - Abstract DL 2 May Lead organisers @Sid__Bansal @anfurnari @TusharNagarajan
Life update: joining @GoogleDeepMind to work on improving Gemini models and Gemini-powered products for Indian languages
Check out our tech report on proactive T2I agents that ask clarification questions to reduce uncertainty! With this agent, we obtain 2 times higher VQAScore in just 5 turns!🤯 We've open-sourced our agent code powered by #Gemini @GoogleDeepMind! 🚀 Code:github.com/google-deepmin…
Check out our tech report on proactive T2I agents that ask clarification questions to reduce uncertainty! With this agent, we obtain 2 times higher VQAScore in just 5 turns!🤯 We've open-sourced our agent code powered by #Gemini @GoogleDeepMind! 🚀 Code:github.com/google-deepmin… https://t.co/urviKVB2Mt
Here's a pretty cool idea for improving captioning models: RL-finetune the captioner, such that the caption retrieves the correct image within a set of very similar ones like the three shown below. This encourages the captioner to include more salient details.
Here's a pretty cool idea for improving captioning models: RL-finetune the captioner, such that the caption retrieves the correct image within a set of very similar ones like the three shown below. This encourages the captioner to include more salient details.
Can RL fine-tuning endow MLLMs with fine-grained visual understanding? Using our training recipe, we outperform SOTA open-source MLLMs on fine-grained visual discrimination with ClipCap, a mere 200M param simplification of modern MLLMs!!! 🚨Introducing No Detail Left Behind:…
Fun work probing the depths of visual understanding in MLLMs. As image understanding gets better, here is our attempt to evaluate whether models can distinguish fine-grained differences between a pair of images. Congratulations Manu (@gaur_manu) and Darshan (@thought2vec)! 👏🎉
Fun work probing the depths of visual understanding in MLLMs. As image understanding gets better, here is our attempt to evaluate whether models can distinguish fine-grained differences between a pair of images. Congratulations Manu (@gaur_manu) and Darshan (@thought2vec)! 👏🎉
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