Pulkit

166 posts

Pulkit

Pulkit

@pulkitkumar95

CS PhD student @umdcs

가입일 Temmuz 2009
1K 팔로잉270 팔로워
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Connor Dilgren
Connor Dilgren@ConnorDilgren·
Excited to announce my first preprint in LM interpretability! Latent reasoning models are not monitorable by default, since they don't reason in human-readable, natural language text. But can we make progress in understanding their intermediate reasoning steps using mech interp?
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Soumik Mukhopadhyay
Soumik Mukhopadhyay@soumikkanad·
Diffusion models be like: “this image is 97% noise… better process all 256×256 pixels anyway” If very noisy diffusion states contain no more useful information than a tiny downsampled image, Then why run expensive full-res computation on them? 🧵
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Matthew Walmer
Matthew Walmer@MatthewWalmer·
We’re excited to announce UPLiFT, our lightweight, pixel-dense feature upsampler. UPLiFT boosts feature density, preserves semantics, and has better efficiency scaling than recent SOTA methods. See all links in the thread below. Coauthors: @_sakshams_ @AnirudAgg @abhi2610 🧵[1/6]
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Roni Sengupta
Roni Sengupta@SenguptRoni·
🚨 PhD Opening: 3D/4D SLAM & Inverse Rendering for Endoscopy (NIH funded) at @UNC_CS! 🚨 The Project: 🟩 Ideal for students with a strong background in 3D Computer Vision & interested in medical robotics & visualization. 🟩 Highly collaborative work with roboticists, medical imaging researchers, and clinicians (GI, ENT, Pulmonology). 🟩Aim to publish in top-tier CV/ML/Robotics/Medical Imaging venues. 🟩 Build on our prior works: 1⃣Endoscopy depth estimator: PPSNet [ECCV'24 - ppsnet.github.io] 2⃣ 3D SLAM for endoscopy: NFL-BA [NeurIPS'25 -asdunnbe.github.io/NFL-BA/] Apply now! 📧 Email CV to ronisen@cs.unc.edu 📝 Formal UNC CS application required (Deadline soon!) RTs appreciated! 🙏
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Pulkit
Pulkit@pulkitkumar95·
Aloha #ICCV2025! 🌺 Join @ShuaiyiH, @abhi2610 and me today (21 Oct) at poster #334, 11 AM–1 PM! We will be presenting Trokens — our work on point tracking for action recognition. Details of the paper are below 👇
Pulkit@pulkitkumar95

🎉 Excited to share our paper "Trokens: Semantic-Aware Relational Trajectory Tokens for Few-Shot Action Recognition" has been accepted to #ICCV2025! Equally co-led with @ShuaiyiH — we advance few-shot action recognition via smart point tracking. 🔗 trokens-iccv25.github.io 🧵👇

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Ruoshi Liu
Ruoshi Liu@ruoshi_liu·
Everyone says they want general-purpose robots. We actually mean it — and we’ll make it weird, creative, and fun along the way 😎 Recruiting PhD students to work on Computer Vision and Robotics @umdcs for Fall 2026 in the beautiful city of Washington DC!
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Pulkit@pulkitkumar95·
Looking forward to presenting this work and discussing it with fellow researchers. See you in Hawaii! 🌺🌺 #ICCV25 #ICCV2025 #ICCV
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Pulkit@pulkitkumar95·
🎉 Excited to share our paper "Trokens: Semantic-Aware Relational Trajectory Tokens for Few-Shot Action Recognition" has been accepted to #ICCV2025! Equally co-led with @ShuaiyiH — we advance few-shot action recognition via smart point tracking. 🔗 trokens-iccv25.github.io 🧵👇
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Zhenwei
Zhenwei@zenwill_ai·
@ICCVConference In "Camera-Ready Submission Instructions", it says "However, papers that are longer than 8 pages (not including references), will not be processed and will not appear in the conference proceedings or on IEEE Xplore." Does this "8 pages" also not include acknowledgment section?
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Ani Aggarwal
Ani Aggarwal@AnirudAgg·
🧵 Your DiT, faster Introducing ECAD: we reframe diffusion model caching as multi-objective optimization and evolve Pareto-optimal schedules via a genetic algorithm—achieving 4.47 FID gain at 2.58× speedup, with no retraining or tuning. 🔗 aniaggarwal.github.io/ecad #MachineLearning
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Chuong Huynh
Chuong Huynh@RyanHuynh1108·
🌟 CoLLM: A Large Language Model for Composed Image Retrieval (CVPR 2025) ✨A cutting-edge training paradigm using image-caption pairs 📊High-quality synthetic triplets for training & benchmarking 🔗Project: collm-cvpr25.github.io 📄Paper: arxiv.org/abs/2503.19910 #LLM #CIR
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Mara Levy
Mara Levy@mlevy1221·
How can we make Imitation Leaning generalize? In my latest work we show that a key point based representation can generalize to novel instances of an object and is agnostic to background changes.
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