mteb/BIRCO-DorisMae-Test huggingface dataset:
I am currently engaged in a project that involves the generation of realistic text using large-scale, pre-trained language models. However, I am encountering difficulties in controlling the results of the generation process. Existing methods, such as prompting, do not offer the level of control I require.  I am in search of an innovative approach that can enhance the controllability of text generation while simultaneously preserving the quality of the text produced.  More specifically, I am considering the projection of certain constraints and control conditions into the latent space. By injecting these embeddings into the language model's inputs, I anticipate that it could regulate the output of the language model.  I am particularly interested in applying this method to tasks such as constrained text generation, where explicit constraints are present. My goal is for my proposed method to surpass the performance of other fine-tuned or prompting-based methods.
I am currently developing a chatbot capable of engaging in open-ended conversations with users. However, I am dissatisfied with the conventional temperature-based stochastic sampling decoding procedure, as it results in derivative output that lacks creativity and diversity. I am seeking literature that explores the issue of diversity deficiency in language model decoding procedures. Additionally, I aim to create a decoding scheme that strikes a balance between output diversity and quality. I am also interested in research that examines the phenomenon of high-likelihood sentences exhibiting unexpectedly low quality. I wish to understand this occurrence and prevent it from affecting my decoding scheme. Ultimately, my goal is to develop a model that surpasses existing decoding algorithms in terms of balancing the quality and diversity of generated responses.
In natural language processing tasks, it is essential to accurately represent the meanings of words to achieve optimal performance. However, this task is challenging due to the potential for multiple interpretations of a single word. As a solution, I propose a method that initially learns to identify the smallest unambiguous phrase component within sentences and paragraphs. Following this, I aim to accurately model their latent vectorized representations, taking into account their semantic and structural relationships with neighboring semantic components. My current approach involves capturing the semantic relationship using a pretraining method akin to word2vec. To encapsulate the structural syntactic relationships, I propose modeling each semantic component as a node, with the entire paragraph represented as a sparse graph structure. Subsequently, specialized graph-based neural network algorithms can be applied to obtain latent structural representations. Ultimately, my proposed embedding model should assign a more precise embedding to each text input, potentially enhancing performance in numerous downstream tasks.
In my reinforcement learning (RL) research, I encountered a significant challenge: the inability of a policy learned in one environment to adapt or generalize to another. I am seeking an efficient method to transfer policies between environments without the need to train a new policy from scratch each time the environment changes. Currently, I am considering the use of natural language to describe the entire dynamics of the environment or to outline the anticipated changes in the environment. To implement this, I require a language model capable of encoding these natural language descriptions. The resulting encoded latent representations should aid in facilitating policy adaptation to different environments. It appears crucial to align the text's meaning with the environment's dynamics, including transition probability and reward functions. Ultimately, I aim to assess the effectiveness of this approach in terms of transfer and multi-task scenarios across various environments and compare it with existing models. I am interested in any literature related to the use of textual descriptions in RL and any existing datasets suitable for evaluating my proposed methodology.
I am currently engaged in a project that involves compressing High Dynamic Range (HDR) images to be compatible with older JPEG formats. I have found that the existing JPEG XT standard, which is designed to support high dynamic range imaging, does not fully accommodate HDR images. Consequently, I am in need of a model that can convert HDR images into a JPEG XT version with minimal loss of information. I am interested in learning how to incorporate image compression methods into this task. My objective is to develop a universal model that can effectively handle this task without the need to find the optimal setting for each individual image. I am seeking guidance on how to design and train such a model, including the selection of appropriate training data and suitable performance metrics. I anticipate that a neural network-based compression algorithm could potentially outperform traditional algorithms. Therefore, I would appreciate any insights into the compression methods based on CNN, autoencoders, or GANs.
I am currently working on a project that involves recognizing text in images, utilizing the attention-based encoder-decoder framework. However, I am encountering difficulties when the system misinterprets words with extra or missing characters in the text, which complicates the training process and reduces accuracy. I am in search of a model capable of assigning probabilities to words within the images, specifically regarding their likelihood of containing missing or superfluous characters. Additionally, I require techniques that can be integrated into the existing framework. Ideally, these techniques would enable the framework to identify potential spelling errors and attempt to restore the original, correct word. I anticipate that the implementation of such a model and techniques will enhance the performance of my text recognition framework. To test this, I am also seeking datasets that contain spelling errors. I would appreciate any guidance on how to effectively train the model with these datasets and how to evaluate the models’ performance in a reliable way.
I am currently involved in a project that aims to improve the safety and efficiency of self-driving cars by predicting the paths of surrounding objects in urban driving situations. Current methods, such as multimodal regression and occupancy maps, have shown limitations in accurately predicting an object’s path. My plan is to treat trajectory prediction as a ranking task, sorting through the set of all potential paths. I need algorithms that can generate a diverse set of potential paths, accommodating a wide range of situations while concurrently discarding paths that are physically unfeasible. To enhance this method, I plan to generate these path sets based on the vehicle's current situation. For example, when the vehicle is on a highway, the model should not generate paths for objects on the roadside. I am also interested in integrating real-time traffic data and weather conditions into the model to increase its accuracy. Lastly, I aim to validate my novel approach using real-world autonomous vehicle data and compare its performance with existing trajectory prediction models.
In our security company, we often deal with low-resolution images of faces captured from surveillance cameras, which makes it difficult to identify individuals accurately. We are looking for a solution that can generate high-resolution face images from these low-resolution inputs, especially when the input resolution is extremely low (e.g., only several hundred pixels) and the images are captured under uncontrolled settings with large pose and illumination variations. We hope the solution is based on a deep learning framework that can efficiently utilize global and local constraints defining a face. It would be beneficial if the framework had the following 2-step processes: one for holistic face reconstruction according to global constraints and another for enhancing face-specific details and enforcing local patch statistics. Additionally, we would like the deep network to be optimized using a loss function for super-resolution that combines reconstruction error with a learned face quality measured in an adversarial setting, resulting in robust visual results. We are interested in a solution that has been extensively tested in both controlled and uncontrolled setups and has demonstrated improvements over the current state of the art.
I am currently engaged in a project that involves identifying and isolating concealed objects in videos, with a particular emphasis on tracking their movements. I am encountering difficulties in consistently outlining the edges of camouflaged objects, especially when certain parts of the video exhibit no movement. I require a system capable of aligning video frames based on the background while simultaneously tracking target objects and ensuring consistent tracking. The intended application of this model is in nature reserves, where it will be used to monitor the living conditions of endangered species. Therefore, I am seeking a model that can effectively handle various animal types and accurately label them during video storage. Ideally, the model should be trained on a comprehensive dataset encompassing a broad spectrum of animal species. Furthermore, I am interested in understanding how to apply supervised training models to unsupervised tasks, given that some of the animals may be too rare to be included in the training dataset.
I am currently engaged in a project aimed at identifying critical areas within medical images, such as X-rays, to aid in disease diagnosis. For instance, in lung X-rays, the region with a tumor is of greater significance, while other areas may not be as relevant. I am seeking a method to pinpoint these crucial areas in the images. Subsequently, we can enhance efficiency by capturing high-resolution images of these important areas. I am interested in models that can execute this concept by initially analyzing a low-resolution image, detecting significant features, and then focusing on more localized but detailed aspects. Additionally, I require techniques that can deliver the same performance level but with reduced computational power and storage, considering the vast quantity of medical images. I need a newer and larger dataset to train and test the model. I would also appreciate recommendations for other image analysis models that are more efficient yet still yield accurate results.
Our team is developing street views for a mapping application, and we need to censor any potentially sensitive text information within the images we capture. As such, we require a model capable of detecting and interpreting multi-oriented text in various scenes. Traditional methods are proving too slow to process the volume of images we capture for the street view. We think the most efficient solution would be to use two independent models. The first model should be able to extract text regions from the original image, while the second should interpret the text from the region, regardless of its orientation. We are seeking a suitable structure of models that can identify text from images, and ideally, these structures should support parallel training due to the large training set we have for this task. The second model should also be capable of understanding different languages and local slang. Additionally, we would prefer models that come with commercially licensed code.
My current job involves efficient image retrieval. The primary issue is that images require substantial storage space, and the constant conversion to and from compact code results in unnecessary time expenditure. Therefore, I am seeking a model capable of retrieving images in compact code format. The challenge for such a model is that images in compact code do not always maintain close local information, leading to increased computational costs for local features. Consequently, I am interested in image retrieval algorithms and innovative loss functions that prioritize global features. Additionally, I require algorithms that can add labels or annotations directly to the images’ compact code without necessitating any format conversion of the image. I prefer methods that have demonstrated effective performance on popular image retrieval benchmarks.
I am currently working on a project that involves transforming black-and-white videos into colored versions, with a particular focus on color propagation in videos. I have found that traditional methods of color propagation tend to be computationally demanding. Therefore, I am seeking a more efficient method, especially when additional video information is available. I am interested in a model that can utilize a set of colored key frames from the video and apply color propagation algorithms to colorize other frames based on the provided colored ones. Ideally, this method should incorporate both local and global strategies. The local strategies should ensure color consistency in neighboring regions and frames, thereby maintaining stable color propagation. Simultaneously, from a global perspective, the model should be capable of identifying the keyframes that require colorization, ensuring overall process efficiency. While I value the efficiency of the color propagation methods, I am willing to compromise on quality, provided the performance surpasses the baselines established within the past five years.
I am currently involved in a project focused on developing a night-mode facial recognition application. This technology is primarily used in security systems and driver assistance technologies that operate in dark environments. Our project requires a system capable of efficiently processing and analyzing human facial features, head orientations, and eye gaze under near-infrared illumination. To initiate this project, we are in search of a comprehensive dataset containing near-infrared videos of various subjects exhibiting different facial expressions. This dataset should be meticulously labeled to allow us to validate and evaluate algorithms for face detection, eye detection, head pose, and eye gazing tasks in dim environments.  Additionally, it would be beneficial if the dataset included a diverse range of subjects to ensure the robustness and generalizability of the algorithms we develop based on it.  Moreover, we are also seeking recommendations on human-machine interaction in dim environments since the model needs to provide further instructions to the user.
My objective is to devise a methodology for constructing intelligent robots that mimic human-like behavior. These robots would be capable of operating in real-world environments and executing a range of complex tasks. Such a robotic agent would interact with its surroundings, making decisions and performing actions. More importantly, it would continuously adapt to its environment by learning from signals and cues it receives. I aim to create a unified framework that can integrate a large number of independent modules and models. Each of these would perform a fundamental function of the robotic agent, such as processing visual inputs, decision-making, and robotic arm manipulation. While these sub-models would be independent, each responsible for a different function, they would need to interact with each other to optimize the overall performance of the agent. I currently believe that the transfer of information, such as gradient information and parameter updates, from one module to another should be swift and efficient. I intend to demonstrate the adaptability of my robotic agent to different environments and situations effectively using various datasets.
I am interested in conducting a study to examine the behavior and learning capabilities of both human and artificial intelligence (AI) agents within a simulated psychology laboratory environment. This environment must be computerized to allow AI agents to interact within it. I require a pre-existing platform or a method to construct one that enables the implementation of psychological experiments. This platform must be compatible with both human subjects and AI agents, necessitating a computerized system with a visual user interface. The platform should include an Application Programming Interface (API) that allows for the creation of customized tasks and experiments. Essential built-in functionalities should include but not be limited to object detection, object tracking, and change detection. Upon acquiring or constructing such a platform, I aim to explore how reinforcement learning (RL) agents perceive and interpret visual information. I am also interested in investigating how their learning capabilities can be enhanced through the integration of simpler models, such as a basic central vision model. I hope that this platform and the subsequent research it facilitates can contribute to the advancement of deep RL and strengthen its ties with cognitive science.
I have a basic understanding of a research direction in Question & Answering (Q&A) systems. These systems employ an integrated knowledge base, structured similarly to a database, with relational dependencies among various concepts and pre-existing answers. They interpret user queries and retrieve answers by leveraging the dependencies between concept nodes within the knowledge base. I am interested in employing a deep neural network to model this type of knowledge base, thereby eliminating the need for hard-coded, rule-based relationships between concepts. I am curious to see if such a neural network model could overcome the limitations inherent in rule-based knowledge base models. This is because all relationships and dependencies would have implicit vectorized representations, potentially enhancing computational efficiency. This approach might also augment the model's expressiveness and capacity to answer more complex questions. My goal is to demonstrate, using Q&A datasets, that my proposed model outperforms traditional Q&A models in terms of increased accuracy and faster runtime.
When modeling complex probability distributions of variables with inherent dependencies, I utilize a graph to represent these dependencies and probabilistic models to depict the variables' distributions. However, these models can be large and computationally expensive during inference. Therefore, I am seeking a fast, approximate inference algorithm that can access a highly effective proposal distribution for importance sampling. My current strategy involves identifying non-essential variables and marginalizing them during sampling to enhance the speed of inference. Additionally, I am considering transforming the graphical model into a more efficient format, such as a decision diagram, to facilitate easier interpretation during sampling (inference). To evaluate the effectiveness of my approach, I need to test it against standard benchmarks. Therefore, I would appreciate information on the standard benchmarks applicable to this task.
I find that the primary constraint in modern artificial intelligence applications is the computational resources expended on multilayer perceptrons (MLP) within neural networks. To enhance the efficiency of MLP, I am seeking a method to distill MLP by eliminating non-essential weights. To accomplish this, I require a theoretical understanding of the weights in shallow MLPs and an empirical application of these theories to deeper neural networks. Furthermore, I need a metric that takes into account both the computational resources saved and the performance loss resulting from the distillation. I understand that the concept of sparsity could be beneficial in representing the number of edges in a graph, but I need guidance on how to apply this concept to MLPs, which can be viewed as a specific type of graph.
I am seeking to develop a robotic agent capable of comprehending user input and completing assigned tasks. I believe the agent's functionality can be separated into two segments: understanding commands and executing actions. First, I require a model that can interpret user commands, whether written or verbal, and translate them into a list of real-world actions. I believe a language model would be beneficial at this stage. However, I need advice on how to strike a balance between the size of the language model and the accuracy of its interpretation of the input. Additionally, I require a reinforcement learning framework for the action execution phase. However, I've noticed that users often provide detailed yet suboptimal instructions. Therefore, I am in search of a reward function that incorporates social psychology to enhance service quality. I anticipate an algorithm that empowers the agent to assess user input and carry out the most efficient actions while not straying too far from the original input.
I am seeking to develop a method that yields a range of promising output candidates rather than a single solution, a concept known as conformal prediction, in scenarios where data availability for the target task is limited. I need a model capable of automatically analyzing task features and identifying similar tasks with sufficiently available training data. This model should then discern the differences between tasks and transfer knowledge from similar tasks to new ones. It should also retain the candidate that the user ultimately selects, storing this data under the new task for future reference. I am currently undecided on whether to use a transformer or a convolutional neural network (CNN) for this model's implementation, so I would appreciate a comparison of these two structures. Additionally, I would like examples demonstrating fields where conformal prediction outperforms the conventional best-one prediction.
I am interested in understanding how modern computer programs can emulate human causal logic. To achieve this, I would like to learn about the latest computational models that can predict human judgments and compare their performance with that of humans. Additionally, I am seeking references where human participants are presented with causal structures generated by Bayesian-related models, along with their explanations or feedback based on these model-generated outputs. I am curious about the most common differences between human decision-making and machine-based decisions, as well as the history of the development of models for causal reasoning, particularly how these models draw inspiration from the neural structures of living creatures.
As an education researcher, my goal is to create a system that can generate explanations for preschool children as part of their early education. I require a model that can provide real-time explanations for the problems inputted. Given that the target users are preschool children, the model needs to be capable of accepting verbal inputs, integrating phrases into natural language, and predicting the user's question. One challenge I face is that adults rarely discuss common sense and trivial questions, so I require an additional dataset to address these types of questions. It is crucial that the content in the dataset is thoroughly vetted by authorities, as the model will be used by children. The model also needs the ability to discard any inappropriate knowledge it may have previously learned. If possible, I would also like guidance on how to strike a balance between the response time of the model and the size of the dataset.