3497hyy/evaluation-tables huggingface dataset:
Task: Recommendation Systems
Categories: Knowledge Base
Graphs
Miscellaneous

### **Recommendation System in AI Research**

A **Recommendation System** is a specialized AI-driven model that analyzes user preferences and behaviors to suggest relevant content, products, or services. It is widely used in domains like e-commerce, streaming platforms, social media, and personalized learning.

AI research in recommendation systems focuses on:
- **Collaborative Filtering**: Predicting user preferences based on similar users' choices.
- **Content-Based Filtering**: Recommending items based on user history and item characteristics.
- **Hybrid Models**: Combining multiple techniques for better accuracy.
- **Deep Learning & Transformers**: Using neural networks and self-attention mechanisms for personalized recommendations.
- **Graph-Based Approaches**: Leveraging knowledge graphs for relationship-aware recommendations.

Key challenges include data sparsity, scalability, and bias mitigation. Cutting-edge research explores reinforcement learning, explainability, and privacy-preserving methods to enhance recommendation systems.
Task: Paraphrase Identification
Categories: Natural Language Processing

The goal of **Paraphrase Identification** is to determine whether a pair of sentences have the same meaning.

Source: [Adversarial Examples with Difficult Common Words for Paraphrase Identification ](https://arxiv.org/abs/1909.02560)

Image source: [On Paraphrase Identification Corpora ](http://www.lrec-conf.org/proceedings/lrec2014/pdf/1000_Paper.pdf)
Task: Image Instance Retrieval
Categories: Computer Vision

**Image Instance Retrieval** is the problem of retrieving images from a database representing the same object or scene as the one depicted in a query image.

Source: [Compression of Deep Neural Networks for Image Instance Retrieval ](https://arxiv.org/abs/1701.04923)
Task: Hand Pose Estimation
Categories: Graphs
Computer Vision

Hand pose estimation is the task of finding the joints of the hand from an image or set of video frames.

( Image credit: [Pose-REN](https://github.com/xinghaochen/Pose-REN) )
Task: Dimensionality Reduction
Categories: Methodology
Computer Vision

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: [openTSNE](https://github.com/pavlin-policar/openTSNE) )
Task: MULTI-VIEW LEARNING
Categories: Computer Vision

**Multi-View Learning** is a machine learning framework where data are represented by multiple distinct feature groups, and each feature group is referred to as a particular view.

Source: [Dissimilarity-based representation for radiomics applications ](https://arxiv.org/abs/1803.04460)
Task: Phrase Grounding
Categories: Natural Language Processing

Given an image and a corresponding caption, the **Phrase Grounding** task aims to ground each entity mentioned by a noun phrase in the caption to a region in the image.

Source: [Phrase Grounding by Soft-Label Chain Conditional Random Field ](https://arxiv.org/abs/1909.00301)
Task: Bilevel Optimization
Categories: Methodology

**Bilevel Optimization** is a branch of optimization, which contains a nested optimization problem within the constraints of the outer optimization problem. The outer optimization task is usually referred as the upper level task, and the nested inner optimization task is referred as the lower level task. The lower level problem appears as a constraint, such that only an optimal solution to the lower level optimization problem is a possible feasible candidate to the upper level optimization problem.

Source: [Efficient Evolutionary Algorithm for Single-Objective Bilevel Optimization ](https://arxiv.org/abs/1303.3901)
Task: Extractive Text Summarization
Categories: Natural Language Processing

Given a document, selecting a subset of the words or sentences which best represents a summary of the document.
Task: Face Generation
Categories: Computer Vision

Face generation is the task of generating (or interpolating) new faces from an existing dataset.

The state-of-the-art results for this task are located in the Image Generation parent.

( Image credit: [Progressive Growing of GANs for Improved Quality, Stability, and Variation
](https://arxiv.org/pdf/1710.10196v3.pdf) )
Task: Model Selection
Categories: Methodology

Given a set of candidate models, the goal of **Model Selection** is to select the model that best approximates the observed data and captures its underlying regularities. Model Selection criteria are defined such that they strike a balance between the goodness of fit, and the generalizability or complexity of the models.

Source: [Kernel-based Information Criterion ](https://arxiv.org/abs/1408.5810)
Task: Metal Artifact Reduction
Categories: Medical

Metal artifact reduction aims to remove the artifacts introduced by metallic implants in CT images.
Task: Text Classification
Categories: Natural Language Processing

**Text Classification** is the task of assigning a sentence or document an appropriate category. The categories depend on the chosen dataset and can range from topics.

Text Classification problems include emotion classification, news classification, citation intent classification, among others. Benchmark datasets for evaluating text classification capabilities include GLUE, AGNews, among others.

In recent years, deep learning techniques like XLNet and RoBERTa have attained some of the biggest performance jumps for text classification problems.

( Image credit: [Text Classification Algorithms: A Survey](https://arxiv.org/pdf/1904.08067v4.pdf) )
Task: Abstractive Text Summarization
Categories: Natural Language Processing

**Abstractive Text Summarization** is the task of generating a short and concise summary that captures the salient ideas of the source text. The generated summaries potentially contain new phrases and sentences that may not appear in the source text.

Source: [Generative Adversarial Network for Abstractive Text Summarization ](https://arxiv.org/abs/1711.09357)

Image credit: [Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond](https://arxiv.org/pdf/1602.06023v5.pdf)
Task: Facial Landmark Detection
Categories: Computer Vision

**Facial Landmark Detection** is a computer vision task that involves detecting and localizing specific points or landmarks on a face, such as the eyes, nose, mouth, and chin. The goal is to accurately identify these landmarks in images or videos of faces in real-time and use them for various applications, such as face recognition, facial expression analysis, and head pose estimation.

( Image credit: [Style Aggregated Network for Facial Landmark Detection](https://arxiv.org/pdf/1803.04108v4.pdf) )
Task: Photoplethysmography (PPG)
Categories: Medical

**Photoplethysmography (PPG)** is a non-invasive light-based method that has been used since the 1930s for monitoring cardiovascular activity.

Source: [Non-contact transmittance photoplethysmographic imaging (PPGI) for long-distance cardiovascular monitoring ](https://arxiv.org/abs/1503.06775)
Task: Audio Generation
Categories: Audio
Speech
Music

Audio generation (synthesis) is the task of generating raw audio such as speech.

( Image credit: [MelNet](https://arxiv.org/pdf/1906.01083v1.pdf) )
Task: Gaussian Processes
Categories: Methodology
Miscellaneous

**Gaussian Processes** is a powerful framework for several machine learning tasks such as regression, classification and inference. Given a finite set of input output training data that is generated out of a fixed (but possibly unknown) function, the framework models the unknown function as a stochastic process such that the training outputs are a finite number of jointly Gaussian random variables, whose properties can then be used to infer the statistics (the mean and variance) of the function at test values of input.

Source: [Sequential Randomized Matrix Factorization for Gaussian Processes: Efficient Predictions and Hyper-parameter Optimization ](https://arxiv.org/abs/1711.06989)
Task: Image Classification
Categories: Adversarial
Computer Vision

**Image Classification** is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike [object detection](/task/object-detection), which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as [image retrieval](/task/image-retrieval), which also involves finding similar images in a large database.

Source: [Metamorphic Testing for Object Detection Systems ](https://arxiv.org/abs/1912.12162)
Task: Topic Models
Categories: Natural Language Processing

A topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for the discovery of hidden semantic structures in a text body.
Task: Conversational Response Generation
Categories: Natural Language Processing

Given an input conversation, generate a natural-looking text reply to the last conversation element.

Image credit: [DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation](https://www.aclweb.org/anthology/2020.acl-demos.30.pdf)
Task: Joint NER and Classification
Categories: Natural Language Processing

Joint named entity recognition and classification refers to the combined task of identifying named entitites in a given text and text classification.
Task: Counterspeech Detection
Categories: Natural Language Processing

Counter-speech detection is the task of detecting counter-speech, i.e., a crowd-sourced response that argues, disagrees, or presents an opposing view to extremism or hateful content on social media platforms (Source: Adapted from: https://icsr.info/wp-content/uploads/2018/03/ICSR-Report-Challenging-Hate-Counter-speech-Practices-in-Europe.pdf)
Task: Causal Inference
Categories: Knowledge Base
Miscellaneous

Causal inference is the task of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect.

( Image credit: [Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data](https://arxiv.org/pdf/1605.00391v2.pdf) )
Task: Traffic Prediction
Categories: Time Series

**Traffic Prediction** is a task that involves forecasting traffic conditions, such as the volume of vehicles and travel time, in a specific area or along a particular road. This task is important for optimizing transportation systems and reducing traffic congestion.

( Image credit: [BaiduTraffic](https://github.com/JingqingZ/BaiduTraffic) )
Task: Data Summarization
Categories: Miscellaneous

**Data Summarization** is a central problem in the area of machine learning, where we want to compute a small summary of the data.

Source: [How to Solve Fair k-Center in Massive Data Models ](https://arxiv.org/abs/2002.07682)
Task: Voice Conversion
Categories: Audio
Speech

I remember all the summer days
Drinking wine in the sunshine
I hope it never leaves
And I remember all the summer nights
Staring at you in the moonlight
I hope you never leave 'cause baby
You're so good to me
You have all that all that I ever need
It's easy to love you
So easy to love you
Ooh you know it's true
The best part of being with you
To know you're with me
It's not so hard to say
It's easy to love you
I remember all those winter days frozen
In the cold tryin' to get you home
Should I be moving in, we can be together then
Remember spending all those winter nights
Stayin' inside by the warm fire
Yeah you gotta know that I can never let you go
You and I have the rest of our lives to say
It's easy to love you
So easy to love you
Ooh you know it's true
The best part of being with you
To know you're with me
It's not so hard to say
It's easy to love you
Can anybody else see it?
Mm, can anybody else see what I do?
Can anybody else feel it?
Oh, can anybody else feel the way I do?
But now I'm with you
Hard to forget all the moments when
We'd be sitting there hoping it would never end
'Cause this is meant to be
So baby, will you marry me?
It's easy to love you
So easy to love you
Ooh, you know it's true
The best part of being with you
To know you are with me
It's not so hard to say
It's easy to love you
You and me will be together
I know our love will last forever
You and me will be together
I know our love will last forever
You know it's true
The best part of being with you
You're easy to love

Source: [Joint training framework for text-to-speech and voice conversion using multi-source Tacotron and WaveNet ](https://arxiv.org/abs/1903.12389)
Task: Caricature
Categories: Computer Vision

**Caricature** is a pictorial representation or description that deliberately exaggerates a person’s distinctive features or peculiarities to create an easily identifiable visual likeness with a comic effect. This vivid art form contains the concepts of abstraction, simplification and exaggeration.

Source: [Alive Caricature from 2D to 3D ](https://arxiv.org/abs/1803.06802)
Task: Portfolio Optimization
Categories: Time Series

Portfolio management is the task of obtaining higher excess returns through the flexible allocation of asset weights. In reality, common examples are stock selection and the Enhanced Index Fund (EIF). The general solution of portfolio management is to score the potential of assets, buy assets with upside potential and increase their weighting, and sell assets that are likely to fall or are relatively weak. A large number of strategies have been proposed for portfolio management.
Task: Out-of-Distribution Detection
Categories: Computer Vision

Detect out-of-distribution or anomalous examples.
Task: Cross-Lingual Bitext Mining
Categories: Natural Language Processing

Cross-lingual bitext mining is the task of mining sentence pairs that are translations of each other from large text corpora.
Task: Aspect Category Detection
Categories: Natural Language Processing

Aspect category detection (ACD) in sentiment analysis aims to identify the aspect categories mentioned in a sentence.
Task: Graph Regression
Categories: Graphs

The regression task is similar to graph classification but using different loss function and performance metric.
Task: Continual Semantic Segmentation
Categories: Computer Vision

Continual learning in semantic segmentation.
Task: Code Search
Categories: Computer Code

The goal of **Code Search** is to retrieve code fragments from a large code corpus that most closely match a developer’s intent, which is expressed in natural language.

Source: [When Deep Learning Met Code Search ](https://arxiv.org/abs/1905.03813)
Task: Image Similarity Search
Categories: Computer Vision

Image credit: [The 2021 Image Similarity Dataset and Challenge](https://paperswithcode.com/paper/the-2021-image-similarity-dataset-and)
Task: Face Detection
Categories: Computer Vision

**Face Detection** is a computer vision task that involves automatically identifying and locating human faces within digital images or videos. It is a fundamental technology that underpins many applications such as face recognition, face tracking, and facial analysis.

( Image credit: [insightface](https://github.com/deepinsight/insightface) )
Task: Face Verification
Categories: Computer Vision

**Face Verification** is a machine learning task in computer vision that involves determining whether two facial images belong to the same person or not. The task involves extracting features from the facial images, such as the shape and texture of the face, and then using these features to compare and verify the similarity between the images.

( Image credit: [Pose-Robust Face Recognition via Deep Residual Equivariant Mapping](https://arxiv.org/pdf/1803.00839v1.pdf) )
Task: Motion Forecasting
Categories: Computer Vision

Motion forecasting is the task of predicting the location of a tracked object in the future
Task: Computed Tomography (CT)
Categories: Methodology

The term “computed tomography”, or CT, refers to a computerized x-ray imaging procedure in which a narrow beam of x-rays is aimed at a patient and quickly rotated around the body, producing signals that are processed by the machine's computer to generate cross-sectional images—or “slices”—of the body.

( Image credit: [Liver Lesion Detection from Weakly-labeled Multi-phase CT Volumes with a Grouped Single Shot MultiBox Detector](https://github.com/L0SG/grouped-ssd-pytorch) )
Task: Face Reconstruction
Categories: Computer Vision

Face reconstruction is the task of recovering the facial geometry of a face from an image.

( Image credit: Microsoft [Deep3DFaceReconstruction](https://github.com/Microsoft/Deep3DFaceReconstruction) )
Task: Medical Image Registration
Categories: Medical

Image registration, also known as image fusion or image matching, is the process of aligning two or more images based on image appearances. **Medical Image Registration** seeks to find an optimal spatial transformation that best aligns the underlying anatomical structures. Medical Image Registration is used in many clinical applications such as image guidance, motion tracking, segmentation, dose accumulation, image reconstruction and so on. Medical Image Registration is a broad topic which can be grouped from various perspectives. From input image point of view, registration methods can be divided into unimodal, multimodal, interpatient, intra-patient (e.g. same- or different-day) registration. From deformation model point of view, registration methods can be divided in to rigid, affine and deformable methods. From region of interest (ROI) perspective, registration methods can be grouped according to anatomical sites such as brain, lung registration and so on. From image pair dimension perspective, registration methods can be divided into 3D to 3D, 3D to 2D and 2D to 2D/3D.

Source: [Deep Learning in Medical Image Registration: A Review ](https://arxiv.org/abs/1912.12318)
Task: Graph Generation
Categories: Graphs

**Graph Generation** is an important research area with significant applications in drug and material designs.

Source: [Graph Deconvolutional Generation ](https://arxiv.org/abs/2002.07087)
Task: Video Prediction
Categories: Time Series
Computer Vision

Script for Amee Marketing & Trading Company Short Video
*(Duration: 45-60 seconds)*

---

Opening Scene (0:00-0:05):
- *Visual:* Close-up of fresh organic grains spilling gently into a wooden bowl. Sunlight filters through lush green fields.
- *Text Overlay:* "Nourishing Lives, Naturally."
- *Music:* Uplifting acoustic melody with a traditional touch.

Scene 1: Organic & Natural Offerings (0:05-0:15):
- *Visual:* Rapid montage of vibrant vegetables, ripe fruits, aromatic spices, Ayurvedic herbs, and fresh dairy products.
- *Voiceover:* "At Amee Marketing & Trading, we bring you the purest Vedic organic foods—grains, spices, herbs, and dairy—straight from nature’s bounty."

Scene 2: Engineering & Innovation (0:15-0:25):
- *Visual:* Split-screen transition:
- Left: Engineers working on agriculture machinery and water treatment systems.
- Right: Automation controls, aquaculture systems, and textile equipment in action.
- *Voiceover:* "Pioneering sustainable solutions—agriculture engineering, water and wastewater treatment, automation, and industrial innovations."

Scene 3: Waste & Resource Management (0:25-0:35):
- *Visual:* Waste management systems transforming waste into resources, followed by Roadtech equipment paving roads.
- *Text Overlay:* "Building a greener future."
- *Voiceover:* "From waste management to infrastructure, we engineer tomorrow’s world today."

Scene 4: Services & Global Reach (0:35-0:45):
- *Visual:* Factory assembly line, team meeting, and global map with location pins.
- *Voiceover:* "As manufacturers, traders, and suppliers, we bridge quality and trust worldwide."

Closing Scene (0:45-0:55):
- *Visual:* Amee logo fades in over a backdrop of their factory. Contact details appear.
- *Text Overlay:* "Connect with Us!"
- Phone: +91-8300874712
- Website: www.ameemarketingtredingcompany.in
- Location: Home & Factory (add brief map graphic).
- *Voiceover:* "Your partner in purity and progress. Contact Amee today!"

End Frame (0:55-1:00):
- *Visual:* Sunrise over fields with the tagline: "Amee Marketing & Trading – Where Tradition Meets Technology."

---

Production Notes:
- Music: Blend traditional Indian instruments with modern beats for cross-sector appeal.
- Color Palette: Earthy tones (greens, browns) for organic segments; metallic blues/greys for tech sections.
- Pacing: Quick cuts for energy, but hold 2-3 seconds on contact details.

Perfect for social media ads or website headers! 🌱🚀

Gif credit: [MAGVIT ](https://magvit.cs.cmu.edu/)

Source: [Photo-Realistic Video Prediction on Natural Videos of Largely Changing Frames ](https://arxiv.org/abs/2003.08635)
Task: Face Recognition
Categories: Methodology
Computer Vision

**Facial Recognition** is the task of making a positive identification of a face in a photo or video image against a pre-existing database of faces. It begins with detection - distinguishing human faces from other objects in the image - and then works on identification of those detected faces.

The state of the art tables for this task are contained mainly in the consistent parts of the task : the face verification and face identification tasks.

( Image credit: [Face Verification](https://shuftipro.com/face-verification) )
Task: Facial Expression Recognition (FER)
Categories: Computer Vision

**Facial Expression Recognition (FER)** is a computer vision task aimed at identifying and categorizing emotional expressions depicted on a human face. The goal is to automate the process of determining emotions in real-time, by analyzing the various features of a face such as eyebrows, eyes, mouth, and other features, and mapping them to a set of emotions such as anger, fear, surprise, sadness and happiness.

( Image credit: [DeXpression](https://arxiv.org/pdf/1509.05371v2.pdf) )
Task: Data-to-Text Generation
Categories: Natural Language Processing

A classic problem in natural-language generation (NLG) involves taking structured data, such as a table, as input, and producing text that adequately and fluently describes this data as output. Unlike machine translation, which aims for complete transduction of the sentence to be translated, this form of NLG is usually taken to require addressing (at least) two separate challenges: what to say, the selection of an appropriate subset of the input data to discuss, and how to say it, the surface realization of a generation.

( Image credit: [Data-to-Text Generation with Content Selection and Planning](https://arxiv.org/pdf/1809.00582v2.pdf) )
Task: hypergraph embedding
Categories: Graphs

Compute useful representations of hyperedges and vertices
Task: Active Learning
Categories: Methodology
Natural Language Processing
Computer Vision

**Active Learning** is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model

Source: [Polystore++: Accelerated Polystore System for Heterogeneous Workloads ](https://arxiv.org/abs/1905.10336)
Task: Interpretable Machine Learning
Categories: Methodology

The goal of **Interpretable Machine Learning** is to allow oversight and understanding of machine-learned decisions. Much of the work in Interpretable Machine Learning has come in the form of devising methods to better explain the predictions of machine learning models.

Source: [Assessing the Local Interpretability of Machine Learning Models ](https://arxiv.org/abs/1902.03501)
Task: Texture Synthesis
Categories: Computer Vision

The fundamental goal of example-based **Texture Synthesis** is to generate a texture, usually larger than the input, that faithfully captures all the visual characteristics of the exemplar, yet is neither identical to it, nor exhibits obvious unnatural looking artifacts.

Source: [Non-Stationary Texture Synthesis by Adversarial Expansion ](https://arxiv.org/abs/1805.04487)