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snehananavati
Sneha Nanavati

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AIcrowd

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IN

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Challenges Entered

Improve RAG with Real-World Benchmarks

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failed 247893
graded 247892

Multi-Agent Dynamics & Mixed-Motive Cooperation

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Small Object Detection and Classification

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failed 235496

Understand semantic segmentation and monocular depth estimation from downward-facing drone images

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A benchmark for image-based food recognition

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What data should you label to get the most value for your money?

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Interactive embodied agents for Human-AI collaboration

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Behavioral Representation Learning from Animal Poses.

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Airborne Object Tracking Challenge

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ASCII-rendered single-player dungeon crawl game

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5 Puzzles 21 Days. Can you solve it all?

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Measure sample efficiency and generalization in reinforcement learning using procedurally generated environments

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5 Puzzles 21 Days. Can you solve it all?

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Self-driving RL on DeepRacer cars - From simulation to real world

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3D Seismic Image Interpretation by Machine Learning

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5 Puzzles 21 Days. Can you solve it all?

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5 Puzzles 21 Days. Can you solve it all?

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5 Puzzles 21 Days. Can you solve it all?

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Multi-Agent Reinforcement Learning on Trains

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A dataset and open-ended challenge for music recommendation research

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A benchmark for image-based food recognition

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Sample-efficient reinforcement learning in Minecraft

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5 Puzzles, 3 Weeks. Can you solve them all? 😉

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Multi-agent RL in game environment. Train your Derklings, creatures with a neural network brain, to fight for you!

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Predicting smell of molecular compounds

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5 Problems 21 Days. Can you solve it all?

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5 Puzzles 21 Days. Can you solve it all?

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5 Puzzles, 3 Weeks | Can you solve them all?

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Grouping/Sorting players into their respective teams

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5 Problems 15 Days. Can you solve it all?

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5 Problems 15 Days. Can you solve it all?

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5 PROBLEMS 3 WEEKS. CAN YOU SOLVE THEM ALL?

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Remove Smoke from Image

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Classify Rotation of F1 Cars

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Can you classify Research Papers into different categories ?

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Can you dock a spacecraft to ISS ?

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Multi-Agent Reinforcement Learning on Trains

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Multi-Class Object Detection on Road Scene Images

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Localization, SLAM, Place Recognition, Visual Navigation, Loop Closure Detection

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Detect Mask From Faces

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Identify Words from silent video inputs.

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A Challenge on Continual Learning using Real-World Imagery

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graded 200977

Music source separation of an audio signal into separate tracks for vocals, bass, drums, and other

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failed 247893
graded 247892

Make Informed Decisions with Shopping Knowledge

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Participant Rating
vrv 0
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Participant Rating

Amazon KDD Cup 2024: Multi-Task Online Shopping Ch

Change Team Name Request

About 23 hours ago

Team name has been updated to Team_NVIDIA

Spotify Sequential Skip Prediction Challeng-e09c52

Is the data not available anymore?

8 days ago

Hi, the data is available. Please sign up for the challenge and visit the resource section to download it.

Generative Interior Design Challenge 2024

The Generative Interior Design Challenge: How Did the Challenge Winners Transform Empty Rooms with AI

19 days ago

The Generative Interior Design Challenge is the latest interdisciplinary competition hosted on AIcrowd that uses Generative AI to enhance Interior Design. Participants were tasked with using AI to transform empty rooms into furnished spaces based on text descriptions. The goal was to develop a model capable of understanding a text prompt and interpreting an image of an empty room to generate a new image that includes furnishings and design prompts.

The Objective

This challenge required participants to blend their technical skills and creativity to produce aesthetically pleasing and functional designs. The goal was to change how designers, architects, and homeowners envision and plan interior spaces, making the process more efficient and accessible.

Participants used LLMs and Stable Diffusion Models to create visually appealing interiors based on text prompts and a reference image of an empty room to create visually appealing interiors based on text prompts. Submissions were evaluated on room layout, realism, functionality, and consistency with the text prompt. Generated images were also manually assessed by a panel of trained experts to ensure that the designs adhered to the original room’s structure and met standards of realism and practicality.

The amalgamation of art and technology in this challenge provided a chance to explore the impact of generative networks in advancing the interior design field and opening new avenues for client-designer interactions.

MEET THE WINNERS

1st Place: Team decem

  • Prize: $7,000
  • Members: Yi Zhang, Chenyue Wang, Xiaoya Liu

Background: Yi Zhang graduated with a bachelor’s degree and currently works at Sichuan Timesnew Technology Co., Ltd. Along with Chenyue Wang, and Xiaoya Liu, students of Peking University, the team blends experience and enthusiasm. They joined this challenge to improve their understanding and practical skills in diffusion and ControlNet.

Approach: The team began by learning and understanding the concepts of diffusion and ControlNet. Applying their theoretical knowledge to real-world scenarios through hands-on projects, they embraced setbacks and saw them as opportunities for growth. They prepared for the challenge with rigour and practice, ultimately topping the leaderboard.


2nd Place: Team StableDesign

  • Prize: $5,000
  • Members: Mykola Lavreniuk, Bartosz Ludwiczuk

Background: Mykola, a PhD in Applied Mathematics and Senior ML/CV Engineer at the Space Research Institute, holds expertise in visual analysis, detection, segmentation, and monocular depth estimation. He has developed state-of-the-art models and has experience in multimodal techniques, generative AI, and satellite image analysis. His teammate, Bartosz Ludwiczuk, is the Computer Vision Tech Lead at SoftServe. He specialises in representation learning techniques in the image domain, with experience in face recognition, image retrieval, and gait recognition. Both Mykola and Bartosz are regular participants in Machine Learning competitions, consistently achieving top ranks in AIcrowd competitions.

Approach: Team StableDesign started by developing a dataset, sourcing approximately 300,000 images from Airbnb and processing them to extract features for interior and outdoor scenes. They filtered out indoor images, generated segmentation masks and depth maps for each, and created empty room images using an inpainting pipeline. They used the llava1.5 model to generate image descriptions and trained two custom Control Nets based on depth and segmentation-conditioned images. During inference, they used an inpainting pipeline based on the Realistic_Vision v5.1 model, along with the two custom Control Nets and the IP Adapter. Their full code is available here, and their final best model can be accessed here.


3rd Place: Team XenonStack

  • Prize: $3,000
  • Members: Akash Pandey, Navdeep Singh Gill, Dr Jagreet Kaur

Background: Akash is an AI Solution Architect at Xenonstack, specialising in providing Generative AI solutions and overseeing AI operations. He is passionate about advancing technology through experimental projects. Xenonstack is a Technology Consulting and Solutions company focusing on platform engineering, data intelligence, and generative and edge AI. The team is led by Navdeep Singh Gill and Dr Jagreet Kaur, who have over 20 years of experience in the field and are recognised as LinkedIn’s Top AI and Data Voices.

Approach: Team XenonStack began by analysing the problem statement and baseline solution and identifying enhancement opportunities. They implemented a pipeline with a base model, exploring various prompt strategies. Their top improvements included integrating dual ControlNet models, advanced segmentation, masking models, and inpainting models to refine detail and realism. Each modification was tested and improved iteratively. Despite challenges, they continuously adapted by enhancing their strategies to meet the design criteria better, leading to their eventual victory.


Thank you to all participants, and congratulations to our winners! Their innovative approaches and hard work helped redefine the impact of AI on interior design.

Meta Comprehensive RAG Benchmark: KDD Cup 2

‼️ ⏰ Select Submission ID before 20th June, 2024 23:55 UTC

25 days ago

Yes, we have extended the deadline till June 22nd 12:00 UTC (noon).

⏰ Deadline Extended for Submission Selection Form (June 22nd 12:00 UTC)

25 days ago

Hello all,

We are extending the submission selection form deadline to June 22nd, 12:00 UTC (noon).

We will use either the top two submissions on the track-specific leaderboard or the submission IDs specified in the form. If you indicate more than two submissions for any track, we will consider the last two submission IDs. If you have already selected submission IDs on the form and wish to change them, you can do so before the new deadline.

Please note, that all submissions on the form must be eligible for the respective track. For instance, you cannot indicate a submission for Track-1 as your intended submission for Track-2 unless it is already on the leaderboard of Track-2.

Please follow the guidelines and fill out the form by 22nd June, 12:00 UTC.

All the best

‼️ ⏰ Select Submission ID before 20th June, 2024 23:55 UTC

26 days ago

Yes, if your code is addressing all three tasks you can submit the same IDs for the 3 tasks.

‼️ ⏰ Select Submission ID before 20th June, 2024 23:55 UTC

27 days ago

You can select 2 submission IDs for each task in the form.

‼️ ⏰ Select Submission ID before 20th June, 2024 23:55 UTC

27 days ago

Hello Participants,

Please select Submission IDs for the private leaderboard evaluation.

:rotating_light: Deadline for Form Submission:
20th June 2024, 23:59 UTC

:clipboard: Individual Participants:
Please submit the form only once. If multiple entries are made, we will consider your last form entry.

:clipboard: Teams:
If multiple team members submit the form, we will consider the Team Organizer’s last form entry as the final one.

Submit your form here: Meta Challenge Form

All the best,
Meta Comprehensive RAG Benchmark Challenge Team

✨ Check out the KG Baseline

About 1 month ago

Hello all,

Check out the baseline and learn how to use the Knowledge Graph through MockAPI.
More details here.

All the best
Team CRAG

Tag “Phase 2: 13 days left” appears today suddenly, and the submission remaining turns to 1?

About 1 month ago

Updates regarding Phase 2 will be shared soon. The leaderboard is visible for Round 1b.

🚨 IMP: Phase 2 Announcement

About 1 month ago

Phase 2 Laucnh Updates: Comprehensive RAG Benchmark (CRAG) 2024

Hello everyone,

Welcome to Phase 2 of the CRAG: Comprehensive RAG Benchmark. Please carefully read the updates below.

Eligibility

  • Teams: All teams with at least one successful submission in Phase 1 can enter Phase 2.

Key Updates for Phase 2

  1. Time Limit: The time limit per example is 30 seconds.
  2. Submission Limit: The submission limit for Phase 2 is 10 per week.
  3. MockAPI Update: The MockAPI has been updated to include a private Knowledge Graph.
  4. Leaderboard: 20% of the private test set will be used to run the leaderboard (same as Phase 1b).
  5. Final Evaluation:
    • As soon as this phase ends, a final exhaustive evaluation will be conducted on the full Phase 2 test set on a submission of your choice (defaulting to your best submission on the leaderboard).
    • The top submission chosen by the team will be used for the final evaluation.
  6. Submission Deadline: The last date to make a submission is June 20, 2024, at 23:55 UTC.

All the best,

Team CRAG 2024

Why am I not qualified for this round.

2 months ago

Can you please check again? The qualifying criteria for Round 1b has now been removed, so you should be able to make a submission now.

Meta KDD Cup 24 - CRAG - Retrieval Summarization

Why am I not eligible to participate?

2 months ago

Have you clicked on the participate button and accepted the challenge rules?

Why am I not eligible to participate?

2 months ago

You should be able to make a submission now.

EPFL-CSS-233-f19e38

CS233-2024-FashionMNIST

2 months ago

We will be competing for who gets the best accuracy and F1 score on the FashionMNIST dataset. Your ranking will not be used for grading, however you are required to participate. Let’s see how your models do compared to the others!

You will be uploading your results file in the “submissions” tab of the challenge. Everyone in the team gets 5 submissions per day. Winners get chocolates!

Note: Before you save test dataset predictions, print out the shape and make sure it is of shape (N,) (and not (N,1) etc.)

Good luck and have fun!

Task 1: Predicting Room Indoor Temperature

snehananavati has not provided any information yet.