pwc-archive/papers-with-abstracts huggingface dataset:
Tri-Modal Motion Retrieval by Learning a Joint Embedding Space
Authors: Kangning Yin, Shihao Zou, Yuxuan Ge, Zheng Tian
Date: 2024-03-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Cross-Modal Retrieval, Information Retrieval, Retrieval

Information retrieval is an ever-evolving and crucial research domain. The substantial demand for high-quality human motion data especially in online acquirement has led to a surge in human motion research works. Prior works have mainly concentrated on dual-modality learning, such as text and motion tasks, but three-modality learning has been rarely explored. Intuitively, an extra introduced modality can enrich a model's application scenario, and more importantly, an adequate choice of the extra modality can also act as an intermediary and enhance the alignment between the other two disparate modalities. In this work, we introduce LAVIMO (LAnguage-VIdeo-MOtion alignment), a novel framework for three-modality learning integrating human-centric videos as an additional modality, thereby effectively bridging the gap between text and motion. Moreover, our approach leverages a specially designed attention mechanism to foster enhanced alignment and synergistic effects among text, video, and motion modalities. Empirically, our results on the HumanML3D and KIT-ML datasets show that LAVIMO achieves state-of-the-art performance in various motion-related cross-modal retrieval tasks, including text-to-motion, motion-to-text, video-to-motion and motion-to-video.
Playing NetHack with LLMs: Potential & Limitations as Zero-Shot Agents
Authors: Dominik Jeurissen, Diego Perez-Liebana, Jeremy Gow, Duygu Cakmak, James Kwan
Date: 2024-03-01 00:00:00
Tasks: Decision Making, Minecraft, NetHack

Large Language Models (LLMs) have shown great success as high-level planners for zero-shot game-playing agents. However, these agents are primarily evaluated on Minecraft, where long-term planning is relatively straightforward. In contrast, agents tested in dynamic robot environments face limitations due to simplistic environments with only a few objects and interactions. To fill this gap in the literature, we present NetPlay, the first LLM-powered zero-shot agent for the challenging roguelike NetHack. NetHack is a particularly challenging environment due to its diverse set of items and monsters, complex interactions, and many ways to die. NetPlay uses an architecture designed for dynamic robot environments, modified for NetHack. Like previous approaches, it prompts the LLM to choose from predefined skills and tracks past interactions to enhance decision-making. Given NetHack's unpredictable nature, NetPlay detects important game events to interrupt running skills, enabling it to react to unforeseen circumstances. While NetPlay demonstrates considerable flexibility and proficiency in interacting with NetHack's mechanics, it struggles with ambiguous task descriptions and a lack of explicit feedback. Our findings demonstrate that NetPlay performs best with detailed context information, indicating the necessity for dynamic methods in supplying context information for complex games such as NetHack.
Know your exceptions: Towards an Ontology of Exceptions in Knowledge Representation
Authors: Gabriele Sacco, Loris Bozzato, Oliver Kutz
Date: 2024-03-01 00:00:00
Tasks: Common Sense Reasoning, valid

Defeasible reasoning is a kind of reasoning where some generalisations may not be valid in all circumstances, that is general conclusions may fail in some cases. Various formalisms have been developed to model this kind of reasoning, which is characteristic of common-sense contexts. However, it is not easy for a modeller to choose among these systems the one that better fits its domain from an ontological point of view. In this paper we first propose a framework based on the notions of exceptionality and defeasibility in order to be able to compare formalisms and reveal their ontological commitments. Then, we apply this framework to compare four systems, showing the differences that may occur from an ontological perspective.
VisRec: A Semi-Supervised Approach to Radio Interferometric Data Reconstruction
Authors: Ruoqi Wang, Haitao Wang, Qiong Luo, Feng Wang, Hejun Wu
Date: 2024-03-01 00:00:00
Tasks: Data Augmentation

Radio telescopes produce visibility data about celestial objects, but these data are sparse and noisy. As a result, images created on raw visibility data are of low quality. Recent studies have used deep learning models to reconstruct visibility data to get cleaner images. However, these methods rely on a substantial amount of labeled training data, which requires significant labeling effort from radio astronomers. Addressing this challenge, we propose VisRec, a model-agnostic semi-supervised learning approach to the reconstruction of visibility data. Specifically, VisRec consists of both a supervised learning module and an unsupervised learning module. In the supervised learning module, we introduce a set of data augmentation functions to produce diverse training examples. In comparison, the unsupervised learning module in VisRec augments unlabeled data and uses reconstructions from non-augmented visibility data as pseudo-labels for training. This hybrid approach allows VisRec to effectively leverage both labeled and unlabeled data. This way, VisRec performs well even when labeled data is scarce. Our evaluation results show that VisRec outperforms all baseline methods in reconstruction quality, robustness against common observation perturbation, and generalizability to different telescope configurations.
Rethinking The Uniformity Metric in Self-Supervised Learning
Authors: Xianghong Fang, Jian Li, Qiang Sun, Benyou Wang
Date: 2024-03-01 00:00:00
Tasks: Self-Supervised Learning

Uniformity plays an important role in evaluating learned representations, providing insights into self-supervised learning. In our quest for effective uniformity metrics, we pinpoint four principled properties that such metrics should possess. Namely, an effective uniformity metric should remain invariant to instance permutations and sample replications while accurately capturing feature redundancy and dimensional collapse. Surprisingly, we find that the uniformity metric proposed by \citet{Wang2020UnderstandingCR} fails to satisfy the majority of these properties. Specifically, their metric is sensitive to sample replications, and can not account for feature redundancy and dimensional collapse correctly. To overcome these limitations, we introduce a new uniformity metric based on the Wasserstein distance, which satisfies all the aforementioned properties. Integrating this new metric in existing self-supervised learning methods effectively mitigates dimensional collapse and consistently improves their performance on downstream tasks involving CIFAR-10 and CIFAR-100 datasets. Code is available at \url{https://github.com/statsle/WassersteinSSL}.
DiaHalu: A Dialogue-level Hallucination Evaluation Benchmark for Large Language Models
Authors: Kedi Chen, Qin Chen, Jie zhou, Yishen He, Liang He
Date: 2024-03-01 00:00:00
Tasks: Hallucination, Hallucination Evaluation, Sentence

Since large language models (LLMs) achieve significant success in recent years, the hallucination issue remains a challenge, numerous benchmarks are proposed to detect the hallucination. Nevertheless, some of these benchmarks are not naturally generated by LLMs but are intentionally induced. Also, many merely focus on the factuality hallucination while ignoring the faithfulness hallucination. Additionally, although dialogue pattern is more widely utilized in the era of LLMs, current benchmarks only concentrate on sentence-level and passage-level hallucination. In this study, we propose DiaHalu, the first dialogue-level hallucination evaluation benchmark to our knowledge. Initially, we integrate the collected topics into system prompts and facilitate a dialogue between two ChatGPT3.5. Subsequently, we manually modify the contents that do not adhere to human language conventions and then have LLMs re-generate, simulating authentic human-machine interaction scenarios. Finally, professional scholars annotate all the samples in the dataset. DiaHalu covers four common multi-turn dialogue domains and five hallucination subtypes, extended from factuality and faithfulness hallucination. Experiments through some well-known LLMs and detection methods on the dataset show that DiaHalu is a challenging benchmark, holding significant value for further research.
End-to-End Graph-Sequential Representation Learning for Accurate Recommendations
Authors: Vladimir Baikalov, Evgeny Frolov
Date: 2024-03-01 00:00:00
Tasks: Recommendation Systems, Representation Learning

Recent recommender system advancements have focused on developing sequence-based and graph-based approaches. Both approaches proved useful in modeling intricate relationships within behavioral data, leading to promising outcomes in personalized ranking and next-item recommendation tasks while maintaining good scalability. However, they capture very different signals from data. While the former approach represents users directly through ordered interactions with recent items, the latter aims to capture indirect dependencies across the interactions graph. This paper presents a novel multi-representational learning framework exploiting these two paradigms' synergies. Our empirical evaluation on several datasets demonstrates that mutual training of sequential and graph components with the proposed framework significantly improves recommendations performance.
Improving Explicit Spatial Relationships in Text-to-Image Generation through an Automatically Derived Dataset
Authors: Ander Salaberria, Gorka Azkune, Oier Lopez de Lacalle, Aitor Soroa, Eneko Agirre, Frank Keller
Date: 2024-03-01 00:00:00
Tasks: Image Captioning, Image Generation, Text to Image Generation, Text-to-Image Generation

Existing work has observed that current text-to-image systems do not accurately reflect explicit spatial relations between objects such as 'left of' or 'below'. We hypothesize that this is because explicit spatial relations rarely appear in the image captions used to train these models. We propose an automatic method that, given existing images, generates synthetic captions that contain 14 explicit spatial relations. We introduce the Spatial Relation for Generation (SR4G) dataset, which contains 9.9 millions image-caption pairs for training, and more than 60 thousand captions for evaluation. In order to test generalization we also provide an 'unseen' split, where the set of objects in the train and test captions are disjoint. SR4G is the first dataset that can be used to spatially fine-tune text-to-image systems. We show that fine-tuning two different Stable Diffusion models (denoted as SD$_{SR4G}$) yields up to 9 points improvements in the VISOR metric. The improvement holds in the 'unseen' split, showing that SD$_{SR4G}$ is able to generalize to unseen objects. SD$_{SR4G}$ improves the state-of-the-art with fewer parameters, and avoids complex architectures. Our analysis shows that improvement is consistent for all relations. The dataset and the code will be publicly available.
Rethinking cluster-conditioned diffusion models for label-free image synthesis
Authors: Nikolas Adaloglou, Tim Kaiser, Felix Michels, Markus Kollmann
Date: 2024-03-01 00:00:00
Tasks: Clustering, Conditional Image Generation, Image Clustering, Image Generation

Diffusion-based image generation models can enhance image quality when conditioned on ground truth labels. Here, we conduct a comprehensive experimental study on image-level conditioning for diffusion models using cluster assignments. We investigate how individual clustering determinants, such as the number of clusters and the clustering method, impact image synthesis across three different datasets. Given the optimal number of clusters with respect to image synthesis, we show that cluster-conditioning can achieve state-of-the-art performance, with an FID of 1.67 for CIFAR10 and 2.17 for CIFAR100, along with a strong increase in training sample efficiency. We further propose a novel empirical method to estimate an upper bound for the optimal number of clusters. Unlike existing approaches, we find no significant association between clustering performance and the corresponding cluster-conditional FID scores. The code is available at https://github.com/HHU-MMBS/cedm-official-wavc2025.
Flatten Long-Range Loss Landscapes for Cross-Domain Few-Shot Learning
Authors: Yixiong Zou, Yicong Liu, Yiman Hu, Yuhua Li, Ruixuan Li
Date: 2024-03-01 00:00:00
Proceeding: CVPR 2024 1
Tasks: Cross-Domain Few-Shot, cross-domain few-shot learning, Few-Shot Learning

Cross-domain few-shot learning (CDFSL) aims to acquire knowledge from limited training data in the target domain by leveraging prior knowledge transferred from source domains with abundant training samples. CDFSL faces challenges in transferring knowledge across dissimilar domains and fine-tuning models with limited training data. To address these challenges, we initially extend the analysis of loss landscapes from the parameter space to the representation space, which allows us to simultaneously interpret the transferring and fine-tuning difficulties of CDFSL models. We observe that sharp minima in the loss landscapes of the representation space result in representations that are hard to transfer and fine-tune. Moreover, existing flatness-based methods have limited generalization ability due to their short-range flatness. To enhance the transferability and facilitate fine-tuning, we introduce a simple yet effective approach to achieve long-range flattening of the minima in the loss landscape. This approach considers representations that are differently normalized as minima in the loss landscape and flattens the high-loss region in the middle by randomly sampling interpolated representations. We implement this method as a new normalization layer that replaces the original one in both CNNs and ViTs. This layer is simple and lightweight, introducing only a minimal number of additional parameters. Experimental results on 8 datasets demonstrate that our approach outperforms state-of-the-art methods in terms of average accuracy. Moreover, our method achieves performance improvements of up to 9\% compared to the current best approaches on individual datasets. Our code will be released.
Predicting UAV Type: An Exploration of Sampling and Data Augmentation for Time Series Classification
Authors: Tarik Crnovrsanin, Calvin Yu, Dane Hankamer, Cody Dunne
Date: 2024-03-01 00:00:00
Tasks: Data Augmentation, Time Series, Time Series Classification

Unmanned aerial vehicles are becoming common and have many productive uses. However, their increased prevalence raises safety concerns -- how can we protect restricted airspace? Knowing the type of unmanned aerial vehicle can go a long way in determining any potential risks it carries. For instance, fixed-wing craft can carry more weight over longer distances, thus potentially posing a more significant threat. This paper presents a machine learning model for classifying unmanned aerial vehicles as quadrotor, hexarotor, or fixed-wing. Our approach effectively applies a Long-Short Term Memory (LSTM) neural network for the purpose of time series classification. We performed experiments to test the effects of changing the timestamp sampling method and addressing the imbalance in the class distribution. Through these experiments, we identified the top-performing sampling and class imbalance fixing methods. Averaging the macro f-scores across 10 folds of data, we found that the majority quadrotor class was predicted well (98.16%), and, despite an extreme class imbalance, the model could also predicted a majority of fixed-wing flights correctly (73.15%). Hexarotor instances were often misclassified as quadrotors due to the similarity of multirotors in general (42.15%). However, results remained relatively stable across certain methods, which prompted us to analyze and report on their tradeoffs. The supplemental material for this paper, including the code and data for running all the experiments and generating the results tables, is available at https://osf.io/mnsgk/.
Comparing large language models and human programmers for generating programming code
Authors: Wenpin Hou, Zhicheng Ji
Date: 2024-03-01 00:00:00
Tasks: Code Generation, Computational Efficiency

We systematically evaluated the performance of seven large language models in generating programming code using various prompt strategies, programming languages, and task difficulties. GPT-4 substantially outperforms other large language models, including Gemini Ultra and Claude 2. The coding performance of GPT-4 varies considerably with different prompt strategies. In most LeetCode and GeeksforGeeks coding contests evaluated in this study, GPT-4 employing the optimal prompt strategy outperforms 85 percent of human participants. Additionally, GPT-4 demonstrates strong capabilities in translating code between different programming languages and in learning from past errors. The computational efficiency of the code generated by GPT-4 is comparable to that of human programmers. These results suggest that GPT-4 has the potential to serve as a reliable assistant in programming code generation and software development.
EfficientZero V2: Mastering Discrete and Continuous Control with Limited Data
Authors: Shengjie Wang, Shaohuai Liu, Weirui Ye, Jiacheng You, Yang Gao
Date: 2024-03-01 00:00:00
Tasks: continuous-control, Continuous Control, Reinforcement Learning (RL)

Sample efficiency remains a crucial challenge in applying Reinforcement Learning (RL) to real-world tasks. While recent algorithms have made significant strides in improving sample efficiency, none have achieved consistently superior performance across diverse domains. In this paper, we introduce EfficientZero V2, a general framework designed for sample-efficient RL algorithms. We have expanded the performance of EfficientZero to multiple domains, encompassing both continuous and discrete actions, as well as visual and low-dimensional inputs. With a series of improvements we propose, EfficientZero V2 outperforms the current state-of-the-art (SOTA) by a significant margin in diverse tasks under the limited data setting. EfficientZero V2 exhibits a notable advancement over the prevailing general algorithm, DreamerV3, achieving superior outcomes in 50 of 66 evaluated tasks across diverse benchmarks, such as Atari 100k, Proprio Control, and Vision Control.
Multi-Task Learning Using Uncertainty to Weigh Losses for Heterogeneous Face Attribute Estimation
Authors: Huaqing Yuan, Yi He, Peng Du, Lu Song
Date: 2024-03-01 00:00:00
Tasks: Attribute, Multi-Task Learning

Face images contain a wide variety of attribute information. In this paper, we propose a generalized framework for joint estimation of ordinal and nominal attributes based on information sharing. We tackle the correlation problem between heterogeneous attributes using hard parameter sharing of shallow features, and trade-off multiple loss functions by considering homoskedastic uncertainty for each attribute estimation task. This leads to optimal estimation of multiple attributes of the face and reduces the training cost of multitask learning. Experimental results on benchmarks with multiple face attributes show that the proposed approach has superior performance compared to state of the art. Finally, we discuss the bias issues arising from the proposed approach in face attribute estimation and validate its feasibility on edge systems.
Imitation Learning Datasets: A Toolkit For Creating Datasets, Training Agents and Benchmarking
Authors: Nathan Gavenski, Michael Luck, Odinaldo Rodrigues
Date: 2024-03-01 00:00:00
Tasks: Benchmarking, Imitation Learning

Imitation learning field requires expert data to train agents in a task. Most often, this learning approach suffers from the absence of available data, which results in techniques being tested on its dataset. Creating datasets is a cumbersome process requiring researchers to train expert agents from scratch, record their interactions and test each benchmark method with newly created data. Moreover, creating new datasets for each new technique results in a lack of consistency in the evaluation process since each dataset can drastically vary in state and action distribution. In response, this work aims to address these issues by creating Imitation Learning Datasets, a toolkit that allows for: (i) curated expert policies with multithreaded support for faster dataset creation; (ii) readily available datasets and techniques with precise measurements; and (iii) sharing implementations of common imitation learning techniques. Demonstration link: https://nathangavenski.github.io/#/il-datasets-video
ROME: Memorization Insights from Text, Logits and Representation
Authors: Bo Li, Qinghua Zhao, Lijie Wen
Date: 2024-03-01 00:00:00
Tasks: Memorization

Previous works have evaluated memorization by comparing model outputs with training corpora, examining how factors such as data duplication, model size, and prompt length influence memorization. However, analyzing these extensive training corpora is highly time-consuming. To address this challenge, this paper proposes an innovative approach named ROME that bypasses direct processing of the training data. Specifically, we select datasets categorized into three distinct types -- context-independent, conventional, and factual -- and redefine memorization as the ability to produce correct answers under these conditions. Our analysis then focuses on disparities between memorized and non-memorized samples by examining the logits and representations of generated texts. Experimental findings reveal that longer words are less likely to be memorized, higher confidence correlates with greater memorization, and representations of the same concepts are more similar across different contexts. Our code and data will be publicly available when the paper is accepted.
Surveying the Dead Minds: Historical-Psychological Text Analysis with Contextualized Construct Representation (CCR) for Classical Chinese
Authors: Yuqi Chen, Sixuan Li, Ying Li, Mohammad Atari
Date: 2024-03-01 00:00:00
Tasks: Contrastive Learning

In this work, we develop a pipeline for historical-psychological text analysis in classical Chinese. Humans have produced texts in various languages for thousands of years; however, most of the computational literature is focused on contemporary languages and corpora. The emerging field of historical psychology relies on computational techniques to extract aspects of psychology from historical corpora using new methods developed in natural language processing (NLP). The present pipeline, called Contextualized Construct Representations (CCR), combines expert knowledge in psychometrics (i.e., psychological surveys) with text representations generated via transformer-based language models to measure psychological constructs such as traditionalism, norm strength, and collectivism in classical Chinese corpora. Considering the scarcity of available data, we propose an indirect supervised contrastive learning approach and build the first Chinese historical psychology corpus (C-HI-PSY) to fine-tune pre-trained models. We evaluate the pipeline to demonstrate its superior performance compared with other approaches. The CCR method outperforms word-embedding-based approaches across all of our tasks and exceeds prompting with GPT-4 in most tasks. Finally, we benchmark the pipeline against objective, external data to further verify its validity.