3497hyy/evaluation-tables huggingface dataset:
Task: Cross-Modal Information Retrieval
Categories: Miscellaneous

**Cross-Modal Information Retrieval** (CMIR) is the task of finding relevant items across different modalities. For example, given an image, find a text or vice versa. The main challenge in CMIR is known as the *heterogeneity gap*: since items from different modalities have different data types, the similarity between them cannot be measured directly. Therefore, the majority of CMIR methods published to date attempt to bridge this gap by learning a latent representation space, where the similarity between items from different modalities can be measured.

Source: [Scene-centric vs. Object-centric Image-Text Cross-modal Retrieval: A Reproducibility Study](https://arxiv.org/abs/2301.05174)
Task: Workflow Discovery
Categories: Natural Language Processing

Workflow Discovery (WD) was introduced by [Workflow Discovery from Dialogues in the Low Data Regime](https://openreview.net/forum?id=L9othQvPks). WD aims to extract work-flows that have either implicitly or explicitly guided task-oriented dialogues be-
tween two people. Workflows extracted from a conversation consist of a summary of the key actions taken during
the dialogue. These workflows consist of pre-defined terms for actions and slots when possible, but our approach also al-
lows for actions that are not known to be invented by the model online and used as new steps in the generated work-
flow.
Task: Generative Semantic Nursing
Categories: Methodology

**Generative Semantic Nursing** is a task of intervening the generative process on the fly during inference time to improve the faithfulness of the generated images. It works by carefully manipulating of latents during the denoising process of a pre-trained text-to-image diffusion model.

Source: [Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models](https://paperswithcode.com/paper/attend-and-excite-attention-based-semantic)
Task: Class Incremental Learning
Categories: Methodology
Computer Vision

Incremental learning of a sequence of tasks when the task-ID is not available at test time.
Task: Text-to-Video Generation
Categories: Natural Language Processing
Computer Vision

Ma grand-mère m’a raconté que quand elle était étudiante, elle avait un petit-ami. À l’âge de 18 ans, il a dû partir pour le service militaire, elle ne l’a pas attendu et elle a épousé quelqu’un d’autre. Quand ma grand-mère avait 58-59 ans, un homme (son premier amour) lui a envoyé une demande d’amis sur un réseau social, ils ont commencé à parler... En moins de six mois, ils ont décidé de se voir. Le trajet en train a duré deux jours et ils se sont finalement rencontrés. Cela fait maintenant deux ans qu’ils habitent ensemble et qu’ils nous rendent visite de temps en temps. Je réalise maintenant que leur amour l’un envers l’autre n’a jamais cessé.
Task: Compiler Optimization
Categories: Computer Code

Machine learning guided compiler optimization
Task: Data Valuation
Categories: Methodology

Data valuation in machine learning tries to determine the worth of data, or data sets, for downstream tasks. Some methods are task-agnostic and consider datasets as a whole, mostly for decision making in data markets. These look at distributional distances between samples. More often, methods look at how individual points affect performance of specific machine learning models. They assign a scalar to each element of a training set which reflects its contribution to the final performance of some model trained on it. Some concepts of value depend on a specific model of interest, others are model-agnostic.

Concepts of the usefulness of a datum or its influence on the outcome of a prediction have a long history in statistics and ML, in particular through the notion of the influence function. However, it has only been recently that rigorous and practical notions of value for data, and in particular data-sets, have appeared in the ML literature, often based on concepts from collaborative game theory, but also from generalization estimates of neural networks, or optimal transport theory, among others.
Task: Calving Front Delineation In Synthetic Aperture Radar Imagery
Categories: Computer Vision

Delineating the calving front of a marine-terminating glacier in synthetic aperture radar (SAR) imagery. This can, for example, be done through Semantic Segmentation.
Task: Open Vocabulary Object Detection
Categories: Computer Vision

Open-vocabulary detection (OVD) aims to generalize beyond the limited number of base classes labeled during the training phase. The goal is to detect novel classes defined by an unbounded
(open) vocabulary at inference.
Task: Feedback Vertex Set (FVS)
Categories: Graphs

The **Feedback Vertex Set (FVS)** problem is a computational problem in computer science and graph theory that involves finding the smallest possible subset of vertices in an undirected graph such that removing those vertices results in a graph that is acyclic, i.e., a forest. The goal of the FVS problem is to minimize the size of the feedback vertex set, and is considered NP-hard, meaning that finding the optimal solution is computationally difficult. For directed graphs, a feedback vertex set is instead a subset of vertices whose removal results in directed acyclic graph (DAG), not necessarily a forest. This task can refer to either looking for a set of provably minimal size (in as little time as possible), or a heuristic algorithm that produces small solutions quickly (but which may have even smaller sets).
Task: Steiner Tree Problem
Categories: Graphs

The **Steiner tree problem** is a computational problem in computer science and graph theory that involves finding the minimum weight subgraph in an undirected graph that connects a given set of terminal vertices. The goal of the Steiner tree problem is to minimize the total weight of the edges in the subgraph, and it is considered NP-hard, meaning that finding the optimal solution is computationally difficult.
Task: Tree Decomposition
Categories: Graphs

**Tree Decomposition** is a technique in graph theory and computer science for representing a graph as a tree, where each node in the tree represents a set of vertices in the original graph. The goal of tree decomposition is to divide the graph into smaller, more manageable pieces, and to use the tree to represent the relationships between these pieces.
Task: Twinwidth Contraction Sequence
Categories: Graphs

**Twinwidth Contraction Sequence** is a concept in graph theory and computer science that refers to a sequence of graph contractions that transform a graph into a smaller, more manageable graph. The goal of the Twinwidth Contraction Sequence is to find a sequence of contractions that results in a graph with a small "twinwidth", which is a measure of how well the graph can be decomposed into small, connected subgraphs.
Task: Synthetic Image Detection
Categories: Computer Vision

Identify if the image is real or generated/manipulated by any generative models (GAN or Diffusion).
Task: Synthetic Image Attribution
Categories: Computer Vision

Determine the source or origin of a generated image, such as identifying the model or tool used to create it. This information can be useful for detecting copyright infringement or for investigating digital crimes.
Task: Dynamic Point Removal
Categories: Robots

In the field of robotics, the point cloud has become an essential map representation. From the perspective of downstream tasks like localization and global path planning, points corresponding to dynamic objects will adversely affect their performance. Existing methods for removing dynamic points in point clouds often lack clarity in comparative evaluations and comprehensive analysis. Therefore, we propose an easy-to-extend unified benchmarking framework for evaluating techniques for removing dynamic points in maps. It includes refactored state-of-art methods and novel metrics to analyze the limitations of these approaches. This enables researchers to dive deep into the underlying reasons behind these limitations. The benchmark makes use of several datasets with different sensor types. All the code and datasets related to our study are publicly available for further development and utilization.
Task: Models Alignment
Categories: Knowledge Base

**Models Alignment** is the process of ensuring that multiple models used in a machine learning system are consistent with each other and aligned with the goals of the system. This involves defining clear and consistent objectives for each model, identifying and addressing any inconsistencies or biases in the data used to train each model, testing and validating each model to ensure its accuracy, and ensuring that the predictions and decisions made by each model are consistent and aligned with the overall goals of the system.
Task: Repetitive Action Counting
Categories: Computer Vision

Repetitive action counting aims to count the number of repetitive actions in a video.
Task: Singing Voice Synthesis
Categories: Speech
Music

(Verse 1)
Sa bawat hakbang, sa bawat daan
May pangarap kang naghihintay
Westbridge ang gabay, sa iyong paglalakbay
Tungo sa kinabukasan, ng ating bayan

(Chorus)
Lagi kang kasama, sa aking puso
Westbridge Institute, ang nagbibigay ng liwanag
Sa bawat hamon, sa bawat pagsubok
Lagi kang kasama, sa aking puso

(Verse 2)
Kami ang bagong henerasyon
Na may pangarap, na may pag-asa
Westbridge ang nagtuturo, ng mga kasanayan
Tungo sa pag-unlad, ng ating bansa

(Chorus)
Lagi kang kasama, sa aking puso
Westbridge Institute, ang nagbibigay ng liwanag
Sa bawat hamon, sa bawat pagsubok
Lagi kang kasama, sa aking puso

(Bridge)
Tayo ay magkakaisa, sa pagtataguyod
Ng ating mga pangarap, ng ating mga adhikain
Westbridge ang siyang, nagbibigay ng lakas
Tungo sa pag-abot, ng ating mga pangarap

(Chorus)
Lagi kang kasama, sa aking puso
Westbridge Institute, ang nagbibigay ng liwanag
Sa bawat hamon, sa bawat pagsubok
Lagi kang kasama, sa aking puso
Task: Training-free 3D Point Cloud Classification
Categories: Computer Vision

Evaluation on target datasets for 3D Point Cloud Classification without any training
Task: Training-free 3D Part Segmentation
Categories: Computer Vision

Evaluation on target datasets for 3D Part Segmentation without any training
Task: Contact Detection
Categories: Robots

Static-friction contact detection in legged locomotion
Task: Dense Video Captioning
Categories: Computer Vision

Most natural videos contain numerous events. For example, in a video of a “man playing a piano”, the video might also contain “another man dancing” or “a crowd clapping”. The task of dense video captioning involves both detecting and describing events in a video.
Task: Text-to-GQL
Categories: Natural Language Processing

Text-to-QGL is a task in natural language processing (NLP) where the goal is to generate Graph Query Language queries from natural language text automatically.
Task: Video-to-image Affordance Grounding
Categories: Computer Vision

Given a demonstration video V and a target image I, the goal of video-to-image affordance grounding predict an affordance heatmap over the target image according to the hand-interacted region in the video, accompanied by the affordance action (e.g., press, turn).
Task: Class-Incremental Semantic Segmentation
Categories: Computer Vision

Semantic segmentation with continous increments of classes.
Task: 3D Point Cloud Interpolation
Categories: Computer Vision

Point cloud interpolation is a fundamental problem for 3D computer vision. Given a low temporal resolution (frame rate) point cloud sequence, the target of interpolation is to generate a smooth point cloud sequence with high temporal resolution (frame rate).
Task: Text to 3D
Categories: Computer Vision

Task involves generating 3D objects based on the text prompt provided to the system.
Task: Image Augmentation
Categories: Computer Vision

**Image Augmentation** is a data augmentation method that generates more training data from the existing training samples. Image Augmentation is especially useful in domains where training data is limited or expensive to obtain like in biomedical applications.

Source: [Improved Image Augmentation for Convolutional Neural Networks by Copyout and CopyPairing ](https://arxiv.org/abs/1909.00390)

( Image credit: [Kornia](https://github.com/kornia/kornia) )
Task: Camera Relocalization
Categories: Computer Vision

"Camera relocalization, or image-based localization is a fundamental problem in robotics and computer vision. It refers to the process of determining camera pose from the visual scene representation and it is essential for many applications such as navigation of autonomous vehicles, structure from motion (SfM), augmented reality (AR) and simultaneous localization and mapping (SLAM)." ([Source](https://paperswithcode.com/paper/camera-relocalization-by-computing-pairwise))
Task: Motion Magnification
Categories: Computer Vision

Motion magnification is a technique that acts like a microscope for visual motion. It can amplify subtle motions in a video sequence, allowing for visualization of deformations that would otherwise be invisible. To achieve motion magnification, we need to accurately measure visual motions, and group the pixels to be modified.

There are different approaches to motion magnification, such as Lagrangian and Eulerian methods. Lagrangian methods track the trajectories of moving objects and exaggerate them, while Eulerian methods manipulate the motions at fixed positions. Eulerian methods can be further divided into linear and phase-based methods. Linear methods apply a temporal bandpass filter to boost the linear term of a Taylor series expansion of the displacement function, while phase-based methods use complex wavelet transforms to manipulate the phase of the signal.

Motion magnification has various applications, such as measuring the human pulse, visualizing the heat plume of candles, revealing the oscillations of a wine glass, and detecting structural defects.
Task: Aspect-Category-Opinion-Sentiment Quadruple Extraction
Categories: Natural Language Processing

Aspect-Category-Opinion-Sentiment (ACOS) Quadruple Extraction is the task with the goal to extract all aspect-category-opinion-sentiment quadruples in a review sentence. ( and provide full support for aspect-level sentiment analysis with implicit aspects and opinions if possible )
Task: BIG-bench Machine Learning
Categories: Methodology

This branch include most common machine learning fundamental algorithms.
Task: Inverse Rendering
Categories: Computer Vision

**Inverse Rendering** is the task of recovering the properties of a scene, such as shape, material, and lighting, from an image or a video. The goal of inverse rendering is to determine the properties of a scene given an observation of it, and to generate new images or videos based on these properties.
Task: Adversarial Attack Detection
Categories: Knowledge Base

The detection of adversarial attacks.
Task: Speech Emotion Recognition
Categories: Speech

**Speech Emotion Recognition** is a task of speech processing and computational paralinguistics that aims to recognize and categorize the emotions expressed in spoken language. The goal is to determine the emotional state of a speaker, such as happiness, anger, sadness, or frustration, from their speech patterns, such as prosody, pitch, and rhythm.

For multimodal emotion recognition, please upload your result to [Multimodal Emotion Recognition on IEMOCAP](https://paperswithcode.com/sota/multimodal-emotion-recognition-on-iemocap)
Task: Medical Image Deblurring
Categories: Computer Vision

Medical image deblurring aims to remove blurs from medical images
Task: Trajectory Planning
Categories: Playing Games

Trajectory planning for industrial robots consists of moving the tool center point from point A to point B while avoiding body collisions over time.
Trajectory planning is sometimes referred to as motion planning and erroneously as path planning. Trajectory planning is distinct from path planning in that it is parametrized by time. Essentially trajectory planning encompasses path planning in addition to planning how to move based on velocity, time, and kinematics.
Task: Key Information Extraction
Categories: Natural Language Processing
Computer Vision

Key Information Extraction (KIE) is aimed at extracting structured information (e.g. key-value pairs) from form-style documents (e.g. invoices), which makes an important step towards intelligent document understanding.
Task: Muscle-Computer Interfaces (MCIs)
Categories: Robots

An interaction methodology that directly senses and decodes human muscular activity rather than relying on physical device actuation or user actions that are externally visible or audible.
Task: Rolling Shutter Correction

Rolling Shutter Correction
Task: Personalized Segmentation
Categories: Computer Vision

Given a one-shot image with a reference mask, the models are required to segment the indicated target object in any other images.
Task: Medical Image Enhancement
Categories: Computer Vision

Aims to improve the perceptual quality of low-quality medical images
Task: Domain Generalization
Categories: Computer Vision

The idea of **Domain Generalization** is to learn from one or multiple training domains, to extract a domain-agnostic model which can be applied to an unseen domain

Source: [Diagram Image Retrieval using Sketch-Based Deep Learning and Transfer Learning ](https://arxiv.org/abs/2004.10780)
Task: Comment Generation
Categories: Natural Language Processing

Article commenting poses new challenges for machines, as it involves multiple cognitive abilities: understanding the given article, formulating opinions and arguments, and organizing natu ral language for expression.