IKDP: Inverse Kinematics through Diffusion Process

It is a common problem in robotics to specify the position of each joint of the robot so that the endpoint reaches a certain target in space. This can be solved in two ways, forward kinematics method and inverse kinematics method. However, inverse kinematics cannot be solved by an algorithm. The common method is the Jacobian inverse technique, and some people have tried to find the answer by machine learning. In this project, we will show how to use the Conditional Denoising Diffusion Probabilistic Model to integrate the solution of calculating IK. Index Terms: Inverse kinematics, Denoising Diffusion Probabilistic Model, self Attention, Transformer

Traffic Scene Generation from Natural Language Description for Autonomous Vehicles with Large Language Model

Text-to-scene generation, transforming textual descriptions into detailed scenes, typically relies on generating key scenarios along predetermined paths, constraining environmental diversity and limiting customization flexibility. To address these limitations, we propose a novel text-to-traffic scene framework that leverages a large language model to generate diverse traffic scenarios within the Carla simulator based on natural language descriptions. Users can define specific parameters such as weather conditions, vehicle types, and road signals, while our pipeline can autonomously select the starting point and scenario details, generating scenes from scratch without relying on predetermined locations or trajectories. Furthermore, our framework supports both critical and routine traffic scenarios, enhancing its applicability. Experimental results indicate that our approach promotes diverse agent planning and road selection, enhancing the training of autonomous agents in traffic environments. Notably, our methodology has achieved a 16% reduction in average collision rates. Our work is made publicly available at https://basiclab.github.io/TTSG.

ReCorD: Reasoning and Correcting Diffusion for HOI Generation

Diffusion models revolutionize image generation by leveraging natural language to guide the creation of multimedia content. Despite significant advancements in such generative models, challenges persist in depicting detailed human-object interactions, especially regarding pose and object placement accuracy. We introduce a training-free method named Reasoning and Correcting Diffusion (ReCorD) to address these challenges. Our model couples Latent Diffusion Models with Visual Language Models to refine the generation process, ensuring precise depictions of HOIs. We propose an interaction-aware reasoning module to improve the interpretation of the interaction, along with an interaction correcting module to refine the output image for more precise HOI generation delicately. Through a meticulous process of pose selection and object positioning, ReCorD achieves superior fidelity in generated images while efficiently reducing computational requirements. We conduct comprehensive experiments on three benchmarks to demonstrate the significant progress in solving text-to-image generation tasks, showcasing ReCorD's ability to render complex interactions accurately by outperforming existing methods in HOI classification score, as well as FID and Verb CLIP-Score. Project website is available at https://alberthkyhky.github.io/ReCorD/ .

The Fabrication of Reality and Fantasy: Scene Generation with LLM-Assisted Prompt Interpretation

In spite of recent advancements in text-to-image generation, limitations persist in handling complex and imaginative prompts due to the restricted diversity and complexity of training data. This work explores how diffusion models can generate images from prompts requiring artistic creativity or specialized knowledge. We introduce the Realistic-Fantasy Benchmark (RFBench), a novel evaluation framework blending realistic and fantastical scenarios. To address these challenges, we propose the Realistic-Fantasy Network (RFNet), a training-free approach integrating diffusion models with LLMs. Extensive human evaluations and GPT-based compositional assessments demonstrate our approach's superiority over state-of-the-art methods. Our code and dataset is available at https://leo81005.github.io/Reality-and-Fantasy/.

A DeNoising FPN With Transformer R-CNN for Tiny Object Detection

Despite notable advancements in the field of computer vision, the precise detection of tiny objects continues to pose a significant challenge, largely owing to the minuscule pixel representation allocated to these objects in imagery data. This challenge resonates profoundly in the domain of geoscience and remote sensing, where high-fidelity detection of tiny objects can facilitate a myriad of applications ranging from urban planning to environmental monitoring. In this paper, we propose a new framework, namely, DeNoising FPN with Trans R-CNN (DNTR), to improve the performance of tiny object detection. DNTR consists of an easy plug-in design, DeNoising FPN (DN-FPN), and an effective Transformer-based detector, Trans R-CNN. Specifically, feature fusion in the feature pyramid network is important for detecting multiscale objects. However, noisy features may be produced during the fusion process since there is no regularization between the features of different scales. Therefore, we introduce a DN-FPN module that utilizes contrastive learning to suppress noise in each level's features in the top-down path of FPN. Second, based on the two-stage framework, we replace the obsolete R-CNN detector with a novel Trans R-CNN detector to focus on the representation of tiny objects with self-attention. Experimental results manifest that our DNTR outperforms the baselines by at least 17.4% in terms of APvt on the AI-TOD dataset and 9.6% in terms of AP on the VisDrone dataset, respectively. Our code will be available at https://github.com/hoiliu-0801/DNTR.

SocialNLP Fake-EmoReact 2021 Challenge Overview: Predicting Fake Tweets from Their Replies and GIFs

This paper provides an overview of the Fake-EmoReact 2021 Challenge, held at the 9th SocialNLP Workshop, in conjunction with NAACL 2021. The challenge requires predicting the authenticity of tweets using reply context and augmented GIF categories from EmotionGIF dataset. We offer the Fake-EmoReact dataset with more than 453k as the experimental materials, where every tweet is labeled with authenticity. Twenty-four teams registered to participate in this challenge, and 5 submitted their results successfully in the evaluation phase. The best team achieves 93.9 on Fake-EmoReact 2021 dataset using F1 score. In addition, we show the definition of share task, data collection, and the teams' performance that joined this challenge and their approaches.

A Dataset and Baselines for Measuring and Predicting the Music Piece Memorability

Nowadays, humans are constantly exposed to music, whether through voluntary streaming services or incidental encounters during commercial breaks. Despite the abundance of music, certain pieces remain more memorable and often gain greater popularity. Inspired by this phenomenon, we focus on measuring and predicting music memorability. To achieve this, we collect a new music piece dataset with reliable memorability labels using a novel interactive experimental procedure. We then train baselines to predict and analyze music memorability, leveraging both interpretable features and audio mel-spectrograms as inputs. To the best of our knowledge, we are the first to explore music memorability using data-driven deep learning-based methods. Through a series of experiments and ablation studies, we demonstrate that while there is room for improvement, predicting music memorability with limited data is possible. Certain intrinsic elements, such as higher valence, arousal, and faster tempo, contribute to memorable music. As prediction techniques continue to evolve, real-life applications like music recommendation systems and music style transfer will undoubtedly benefit from this new area of research.

EmoVIT: Revolutionizing Emotion Insights with Visual Instruction Tuning

Visual Instruction Tuning represents a novel learning paradigm involving the fine-tuning of pre-trained language models using task-specific instructions. This paradigm shows promising zero-shot results in various natural language processing tasks but is still unexplored in vision emotion understanding. In this work, we focus on enhancing the model's proficiency in understanding and adhering to instructions related to emotional contexts. Initially, we identify key visual clues critical to visual emotion recognition. Subsequently, we introduce a novel GPT-assisted pipeline for generating emotion visual instruction data, effectively addressing the scarcity of annotated instruction data in this domain. Expanding on the groundwork established by InstructBLIP, our proposed EmoVIT architecture incorporates emotion-specific instruction data, leveraging the powerful capabilities of Large Language Models to enhance performance. Through extensive experiments, our model showcases its proficiency in emotion classification, adeptness in affective reasoning, and competence in comprehending humor. The comparative analysis provides a robust benchmark for Emotion Visual Instruction Tuning in the era of LLMs, providing valuable insights and opening avenues for future exploration in this domain. Our code is available at \url{https://github.com/aimmemotion/EmoVIT}.

Training-and-prompt-free General Painterly Harmonization Using Image-wise Attention Sharing

Painterly Image Harmonization aims at seamlessly blending disparate visual elements within a single coherent image. However, previous approaches often encounter significant limitations due to training data constraints, the need for time-consuming fine-tuning, or reliance on additional prompts. To surmount these hurdles, we design a Training-and-prompt-Free General Painterly Harmonization method using image-wise attention sharing (TF-GPH), which integrates a novel "share-attention module". This module redefines the traditional self-attention mechanism by allowing for comprehensive image-wise attention, facilitating the use of a state-of-the-art pretrained latent diffusion model without the typical training data limitations. Additionally, we further introduce "similarity reweighting" mechanism enhances performance by effectively harnessing cross-image information, surpassing the capabilities of fine-tuning or prompt-based approaches. At last, we recognize the deficiencies in existing benchmarks and propose the "General Painterly Harmonization Benchmark", which employs range-based evaluation metrics to more accurately reflect real-world application. Extensive experiments demonstrate the superior efficacy of our method across various benchmarks. The code and web demo are available at https://github.com/BlueDyee/TF-GPH.

Lightweight Deep Learning for Resource-Constrained Environments: A Survey

Over the past decade, the dominance of deep learning has prevailed across various domains of artificial intelligence, including natural language processing, computer vision, and biomedical signal processing. While there have been remarkable improvements in model accuracy, deploying these models on lightweight devices, such as mobile phones and microcontrollers, is constrained by limited resources. In this survey, we provide comprehensive design guidance tailored for these devices, detailing the meticulous design of lightweight models, compression methods, and hardware acceleration strategies. The principal goal of this work is to explore methods and concepts for getting around hardware constraints without compromising the model's accuracy. Additionally, we explore two notable paths for lightweight deep learning in the future: deployment techniques for TinyML and Large Language Models. Although these paths undoubtedly have potential, they also present significant challenges, encouraging research into unexplored areas.