OpenRaiser/Intern-Atlas huggingface dataset:
The Dynamic Duo of Collaborative Masking and Target for Advanced Masked Autoencoder Learning
Venue: arXiv cs.CV 2024
Year: 2024

Masked autoencoders (MAE) have recently succeeded in self-supervised vision representation learning. Previous work mainly applied custom-designed (e.g., random, block-wise) masking or teacher (e.g., CLIP)-guided masking and targets. However, they ignore the potential role of the self-training (student) model in giving feedback to the teacher for masking and targets. In this work, we present to integrate Collaborative Masking and Targets for boosting Masked AutoEncoders, namely CMT-MAE. Specifically, CMT-MAE leverages a simple collaborative masking mechanism through linear aggregation across attentions from both teacher and student models. We further propose using the output features from those two models as the collaborative target of the decoder. Our simple and effective framework pre-trained on ImageNet-1K achieves state-of-the-art linear probing and fine-tuning performance. In particular, using ViT-base, we improve the fine-tuning results of the vanilla MAE from 83.6% to 85.7%.
Improved Cotton Leaf Disease Classification Using Parameter-Efficient Deep Learning Framework
Venue: arXiv cs.CV 2024
Year: 2024

Cotton crops, often called "white gold," face significant production challenges, primarily due to various leaf-affecting diseases. As a major global source of fiber, timely and accurate disease identification is crucial to ensure optimal yields and maintain crop health. While deep learning and machine learning techniques have been explored to address this challenge, there remains a gap in developing lightweight models with fewer parameters which could be computationally effective for agricultural practitioners. To address this, we propose an innovative deep learning framework integrating a subset of trainable layers from MobileNet, transfer learning, data augmentation, a learning rate decay schedule, model checkpoints, and early stopping mechanisms. Our model demonstrates exceptional performance, accurately classifying seven cotton disease types with an overall accuracy of 98.42% and class-wise precision ranging from 96% to 100%. This results in significantly enhanced efficiency, surpassing recent approaches in accuracy and model complexity. The existing models in the literature have yet to attain such high accuracy, even when tested on data sets with fewer disease types. The substantial performance improvement, combined with the lightweight nature of the model, makes it practically suitable for real-world applications in smart farming. By offering a high-performing and efficient solution, our framework can potentially address challenges in cotton cultivation, contributing to sustainable agricultural practices.
AFANet: Adaptive Frequency-Aware Network for Weakly-Supervised Few-Shot Semantic Segmentation
Venue: arXiv cs.CV 2024
Year: 2024

Few-shot learning aims to recognize novel concepts by leveraging prior knowledge learned from a few samples. However, for visually intensive tasks such as few-shot semantic segmentation, pixel-level annotations are time-consuming and costly. Therefore, in this paper, we utilize the more challenging image-level annotations and propose an adaptive frequency-aware network (AFANet) for weakly-supervised few-shot semantic segmentation (WFSS). Specifically, we first propose a cross-granularity frequency-aware module (CFM) that decouples RGB images into high-frequency and low-frequency distributions and further optimizes semantic structural information by realigning them. Unlike most existing WFSS methods using the textual information from the multi-modal language-vision model, e.g., CLIP, in an offline learning manner, we further propose a CLIP-guided spatial-adapter module (CSM), which performs spatial domain adaptive transformation on textual information through online learning, thus providing enriched cross-modal semantic information for CFM. Extensive experiments on the Pascal-5\textsuperscript{i} and COCO-20\textsuperscript{i} datasets demonstrate that AFANet has achieved state-of-the-art performance. The code is available at https://github.com/jarch-ma/AFANet.
SBS Figures: Pre-training Figure QA from Stage-by-Stage Synthesized Images
Venue: arXiv cs.CV 2024
Year: 2024

Building a large-scale figure QA dataset requires a considerable amount of work, from gathering and selecting figures to extracting attributes like text, numbers, and colors, and generating QAs. Although recent developments in LLMs have led to efforts to synthesize figures, most of these focus primarily on QA generation. Additionally, creating figures directly using LLMs often encounters issues such as code errors, similar-looking figures, and repetitive content in figures. To address this issue, we present SBSFigures (Stage-by-Stage Synthetic Figures), a dataset for pre-training figure QA. Our proposed pipeline enables the creation of chart figures with complete annotations of the visualized data and dense QA annotations without any manual annotation process. Our stage-by-stage pipeline makes it possible to create diverse topic and appearance figures efficiently while minimizing code errors. Our SBSFigures demonstrate a strong pre-training effect, making it possible to achieve efficient training with a limited amount of real-world chart data starting from our pre-trained weights.
Can Stability be Detrimental? Better Generalization through Gradient Descent Instabilities
Venue: arXiv cs.LG 2024
Year: 2024

Traditional analyses of gradient descent optimization show that, when the largest eigenvalue of the loss Hessian - often referred to as the sharpness - is below a critical learning-rate threshold, then training is 'stable' and training loss decreases monotonically. Recent studies, however, have suggested that the majority of modern deep neural networks achieve good performance despite operating outside this stable regime. In this work, we demonstrate that such instabilities, induced by large learning rates, move model parameters toward flatter regions of the loss landscape. Our crucial insight lies in noting that, during these instabilities, the orientation of the Hessian eigenvectors rotate. This, we conjecture, allows the model to explore regions of the loss landscape that display more desirable geometrical properties for generalization, such as flatness. These rotations are a consequence of network depth, and we prove that for any network with depth > 1, unstable growth in parameters cause rotations in the principal components of the Hessian, which promote exploration of the parameter space away from unstable directions. Our empirical studies reveal an implicit regularization effect in gradient descent with large learning rates operating beyond the stability threshold. We find these lead to excellent generalization performance on modern benchmark datasets.
SCBench: A Sports Commentary Benchmark for Video LLMs
Venue: arXiv cs.CV 2024
Year: 2024

Recently, significant advances have been made in Video Large Language Models (Video LLMs) in both academia and industry. However, methods to evaluate and benchmark the performance of different Video LLMs, especially their fine-grained, temporal visual capabilities, remain very limited. On one hand, current benchmarks use relatively simple videos (e.g., subtitled movie clips) where the model can understand the entire video by processing just a few frames. On the other hand, their datasets lack diversity in task format, comprising only QA or multi-choice QA, which overlooks the models' capacity for generating in-depth and precise texts. Sports videos, which feature intricate visual information, sequential events, and emotionally charged commentary, present a critical challenge for Video LLMs, making sports commentary an ideal benchmarking task. Inspired by these challenges, we propose a novel task: sports video commentary generation, developed $\textbf{SCBench}$ for Video LLMs. To construct such a benchmark, we introduce (1) $\textbf{SCORES}$, a six-dimensional metric specifically designed for our task, upon which we propose a GPT-based evaluation method, and (2) $\textbf{CommentarySet}$, a dataset consisting of 5,775 annotated video clips and ground-truth labels tailored to our metric. Based on SCBench, we conduct comprehensive evaluations on multiple Video LLMs (e.g. VILA, Video-LLaVA, etc.) and chain-of-thought baseline methods. Our results found that InternVL-Chat-2 achieves the best performance with 5.44, surpassing the second-best by 1.04. Our work provides a fresh perspective for future research, aiming to enhance models' overall capabilities in complex visual understanding tasks. Our dataset will be released soon.
Advances in Machine Learning Research Using Knowledge Graphs
Venue: arXiv cs.AI 2024
Year: 2024

The study uses CSSCI-indexed literature from the China National Knowledge Infrastructure (CNKI) database as the data source. It utilizes the CiteSpace visualization software to draw knowledge graphs on aspects such as institutional collaboration and keyword co-occurrence. This analysis provides insights into the current state of research and emerging trends in the field of machine learning in China. Additionally, it identifies the challenges faced in the field of machine learning research and offers suggestions that could serve as valuable references for future research.
EPE-P: Evidence-based Parameter-efficient Prompting for Multimodal Learning with Missing Modalities
Venue: arXiv cs.CV 2024
Year: 2024

Missing modalities are a common challenge in real-world multimodal learning scenarios, occurring during both training and testing. Existing methods for managing missing modalities often require the design of separate prompts for each modality or missing case, leading to complex designs and a substantial increase in the number of parameters to be learned. As the number of modalities grows, these methods become increasingly inefficient due to parameter redundancy. To address these issues, we propose Evidence-based Parameter-Efficient Prompting (EPE-P), a novel and parameter-efficient method for pretrained multimodal networks. Our approach introduces a streamlined design that integrates prompting information across different modalities, reducing complexity and mitigating redundant parameters. Furthermore, we propose an Evidence-based Loss function to better handle the uncertainty associated with missing modalities, improving the model's decision-making. Our experiments demonstrate that EPE-P outperforms existing prompting-based methods in terms of both effectiveness and efficiency. The code is released at https://github.com/Boris-Jobs/EPE-P_MLLMs-Robustness.
Robin Hood Reachability Bidding Games
Venue: arXiv cs.GT 2024
Year: 2024

Two-player graph games are a fundamental model for reasoning about the interaction of agents. These games are played between two players who move a token along a graph. In bidding games, the players have some monetary budget, and at each step they bid for the privilege of moving the token. Typically, the winner of the bid either pays the loser or the bank, or a combination thereof. We introduce Robin Hood bidding games, where at the beginning of every step the richer player pays the poorer a fixed fraction of the difference of their wealth. After the bid, the winner pays the loser. Intuitively, this captures the setting where a regulating entity prevents the accumulation of wealth to some degree. We show that the central property of bidding games, namely the existence of a threshold function, is retained in Robin Hood bidding games. We show that finding the threshold can be formulated as a Mixed-Integer Linear Program. Surprisingly, we show that the games are not always determined exactly at the threshold, unlike their standard counterpart.
Chumor 2.0: Towards Benchmarking Chinese Humor Understanding
Venue: arXiv cs.CL 2024
Year: 2024

Existing humor datasets and evaluations predominantly focus on English, leaving limited resources for culturally nuanced humor in non-English languages like Chinese. To address this gap, we construct Chumor, the first Chinese humor explanation dataset that exceeds the size of existing humor datasets. Chumor is sourced from Ruo Zhi Ba, a Chinese Reddit-like platform known for sharing intellectually challenging and culturally specific jokes. We test ten LLMs through direct and chain-of-thought prompting, revealing that Chumor poses significant challenges to existing LLMs, with their accuracy slightly above random and far below human. In addition, our analysis highlights that human-annotated humor explanations are significantly better than those generated by GPT-4o and ERNIE-4-turbo. We release Chumor at https://huggingface.co/datasets/dnaihao/Chumor, our project page is at https://dnaihao.github.io/Chumor-dataset/, our leaderboard is at https://huggingface.co/spaces/dnaihao/Chumor, and our codebase is at https://github.com/dnaihao/Chumor-dataset.
Inductive Linguistic Reasoning with Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Evaluating large language models (LLMs) on their linguistic reasoning capabilities is an important task to understand the gaps in their skills that may surface during large-scale adoption. In this work, we investigate the abilities of such models to perform abstract multilingual reasoning through the lens of linguistic puzzles on extremely low-resource languages. As these translation tasks involve inductive and deductive reasoning from reference instances, we examine whether diverse auxiliary demonstrations can be automatically induced from seed exemplars, through analogical prompting. We employ a two-stage procedure, first generating analogical exemplars with a language model, and then applying them in-context along with provided target language exemplars. Our results on the modeLing dataset show that analogical prompting is effective in eliciting models' knowledge of language grammar similarities, boosting the performance of GPT-4o by as much as 8.1% and Llama-3.1-405B-Instruct by 5.9% over chain-of-thought approaches. These gains are attributable to the analogical demonstrations, both when self-generated as well as when produced by weaker multilingual models. Furthermore, we demonstrate that our method generalizes to other tasks present in Linguistics Olympiad competitions, achieving sizable improvements across all problem types and difficulty levels included in the LINGOLY dataset with GPT-4o. We also report several findings about interesting phenomena which drive linguistic reasoning performance, suggesting that such puzzles are a valuable benchmark for new reasoning methods.
Look Ahead Text Understanding and LLM Stitching
Venue: arXiv cs.CL 2024
Year: 2024

This paper proposes a look ahead text understanding problem with look ahead section identification (LASI) as an example. This problem may appear in generative AI as well as human interactions, where we want to understand the direction of a developing text or conversation. We tackle the problem using transformer-based LLMs. We show that LASI is more challenging than classic section identification (SI). We argue that both bidirectional contextual information (e.g., BERT) and unidirectional predictive ability (e.g., GPT) will benefit the task. We propose two approaches to stitch together BERT and GPT. Experiments show that our approach outperforms the established models, especially when there is noise in the text (which is often the case for developing text in generative AI). Our paper sheds light on other look ahead text understanding tasks that are important to social media, such as look ahead sentiment classification, and points out the opportunities to leverage pre-trained LLMs through stitching.
The Power of Adaptation: Boosting In-Context Learning through Adaptive Prompting
Venue: arXiv cs.CL 2024
Year: 2024

Large Language Models (LLMs) have demonstrated exceptional abilities across a broad range of language-related tasks, including generating solutions to complex reasoning problems. An effective technique to enhance LLM performance is in-context learning, which encourages a step-by-step reasoning process by including explanatory examples to guide the model's responses. However, selecting appropriate exemplars for the model poses a challenge, as each dataset demands a distinct set of exemplars to enable the LLM to learn effectively and perform well on the test set. Current studies often rely on uncertainty- or diversity-based selection strategies to select exemplars for annotation and to improve model learning. However, these studies typically employ a non-adaptive approach, selecting a set of exemplars all at once. We argue that this non-adaptive strategy may result in a set of exemplars with high redundancy in terms of the knowledge covered, ultimately reducing their overall informativeness. To address this limitation, we propose \textsc{Adaptive-Prompt}, a novel method that adaptively selects exemplars by leveraging model feedback from previously chosen exemplars. Experimental results show that \textsc{Adaptive-Prompt} significantly enhances LLM performance across a variety of reasoning tasks.
Asynchronous Training of Mixed-Role Human Actors in a Partially-Observable Environment
Venue: arXiv cs.HC 2024
Year: 2024

In cooperative training, humans within a team coordinate on complex tasks, building mental models of their teammates and learning to adapt to teammates' actions in real-time. To reduce the often prohibitive scheduling constraints associated with cooperative training, this article introduces a paradigm for cooperative asynchronous training of human teams in which trainees practice coordination with autonomous teammates rather than humans. We introduce a novel experimental design for evaluating autonomous teammates for use as training partners in cooperative training. We apply the design to a human-subjects experiment where humans are trained with either another human or an autonomous teammate and are evaluated with a new human subject in a new, partially observable, cooperative game developed for this study. Importantly, we employ a method to cluster teammate trajectories from demonstrations performed in the experiment to form a smaller number of training conditions. This results in a simpler experiment design that enabled us to conduct a complex cooperative training human-subjects study in a reasonable amount of time. Through a demonstration of the proposed experimental design, we provide takeaways and design recommendations for future research in the development of cooperative asynchronous training systems utilizing robot surrogates for human teammates.
Inspiring Women in Technology: Educational Pathways and Impact
Venue: arXiv cs.CY 2024
Year: 2024

This paper presents initiatives aimed at fostering female involvement in the realm of computing and endeavoring to inspire more women to pursue careers in these fields. The Meninas++ Project coordinates activities at both the high school and higher education levels, facilitating dialogue between young women and computing professionals, and promoting female role models within the field. Our study demonstrated the significant impact of these activities on inspiring, empowering, and retaining female students in computing. Furthermore, higher education initiatives have fostered engagement among both women and men, promoting inclusivity, entrepreneurship, and collaboration to enhance women's representation in the computing field.
ICPR 2024 Competition on Domain Adaptation and GEneralization for Character Classification (DAGECC)
Venue: arXiv cs.CV 2024
Year: 2024

In this companion paper for the DAGECC (Domain Adaptation and GEneralization for Character Classification) competition organized within the frame of the ICPR 2024 conference, we present the general context of the tasks we proposed to the community, we introduce the data that were prepared for the competition and we provide a summary of the results along with a description of the top three winning entries. The competition was centered around domain adaptation and generalization, and our core aim is to foster interest and facilitate advancement on these topics by providing a high-quality, lightweight, real world dataset able to support fast prototyping and validation of novel ideas.
Falsification of Autonomous Systems in Rich Environments
Venue: arXiv cs.RO 2024
Year: 2024

Validating the behavior of autonomous Cyber-Physical Systems (CPS) and Artificial Intelligence (AI) agents, which rely on automated controllers, is an objective of great importance. In recent years, Neural-Network (NN) controllers have been demonstrating great promise. Unfortunately, such learned controllers are often not certified and can cause the system to suffer from unpredictable or unsafe behavior. To mitigate this issue, a great effort has been dedicated to automated verification of systems. Specifically, works in the category of ``black-box testing'' rely on repeated system simulations to find a falsifying counterexample of a system run that violates a specification. As running high-fidelity simulations is computationally demanding, the goal of falsification approaches is to minimize the simulation effort (NN inference queries) needed to return a falsifying example. This often proves to be a great challenge, especially when the tested controller is well-trained. This work contributes a novel falsification approach for autonomous systems under formal specification operating in uncertain environments. We are especially interested in CPS operating in rich, semantically-defined, open environments, which yield high-dimensional, simulation-dependent sensor observations. Our approach introduces a novel reformulation of the falsification problem as the problem of planning a trajectory for a ``meta-system,'' which wraps and encapsulates the examined system; we call this approach: meta-planning. This formulation can be solved with standard sampling-based motion-planning techniques (like RRT) and can gradually integrate domain knowledge to improve the search. We support the suggested approach with an experimental study on falsification of an obstacle-avoiding autonomous car with a NN controller, where meta-planning demonstrates superior performance over alternative approaches.
Multi-Agent Path Finding in Continuous Spaces with Projected Diffusion Models
Venue: arXiv cs.RO 2024
Year: 2024

Multi-Agent Path Finding (MAPF) is a fundamental problem in robotics, requiring the computation of collision-free paths for multiple agents moving from their respective start to goal positions. Coordinating multiple agents in a shared environment poses significant challenges, especially in continuous spaces where traditional optimization algorithms struggle with scalability. Moreover, these algorithms often depend on discretized representations of the environment, which can be impractical in image-based or high-dimensional settings. Recently, diffusion models have shown promise in single-agent path planning, capturing complex trajectory distributions and generating smooth paths that navigate continuous, high-dimensional spaces. However, directly extending diffusion models to MAPF introduces new challenges since these models struggle to ensure constraint feasibility, such as inter-agent collision avoidance. To overcome this limitation, this work proposes a novel approach that integrates constrained optimization with diffusion models for MAPF in continuous spaces. This unique combination directly produces feasible multi-agent trajectories that respect collision avoidance and kinematic constraints. The effectiveness of our approach is demonstrated across various challenging simulated scenarios of varying dimensionality.