pwc-archive/papers-with-abstracts huggingface dataset:
Speeding Up Image Classifiers with Little Companions
Authors: Yang Liu, Kowshik Thopalli, Jayaraman Thiagarajan
Date: 2024-06-24 00:00:00
Tasks: image-classification, Image Classification, Model Compression

Scaling up neural networks has been a key recipe to the success of large language and vision models. However, in practice, up-scaled models can be disproportionately costly in terms of computations, providing only marginal improvements in performance; for example, EfficientViT-L3-384 achieves <2% improvement on ImageNet-1K accuracy over the base L1-224 model, while requiring $14\times$ more multiply-accumulate operations (MACs). In this paper, we investigate scaling properties of popular families of neural networks for image classification, and find that scaled-up models mostly help with "difficult" samples. Decomposing the samples by difficulty, we develop a simple model-agnostic two-pass Little-Big algorithm that first uses a light-weight "little" model to make predictions of all samples, and only passes the difficult ones for the "big" model to solve. Good little companion achieve drastic MACs reduction for a wide variety of model families and scales. Without loss of accuracy or modification of existing models, our Little-Big models achieve MACs reductions of 76% for EfficientViT-L3-384, 81% for EfficientNet-B7-600, 71% for DeiT3-L-384 on ImageNet-1K. Little-Big also speeds up the InternImage-G-512 model by 62% while achieving 90% ImageNet-1K top-1 accuracy, serving both as a strong baseline and as a simple practical method for large model compression.
GMT: Guided Mask Transformer for Leaf Instance Segmentation
Authors: Feng Chen, Sotirios A. Tsaftaris, Mario Valerio Giuffrida
Date: 2024-06-24 00:00:00
Tasks: Instance Segmentation, Segmentation, Semantic Segmentation

Leaf instance segmentation is a challenging multi-instance segmentation task, aiming to separate and delineate each leaf in an image of a plant. Accurate segmentation of each leaf is crucial for plant-related applications such as the fine-grained monitoring of plant growth and crop yield estimation. This task is challenging because of the high similarity (in shape and colour), great size variation, and heavy occlusions among leaf instances. Furthermore, the typically small size of annotated leaf datasets makes it more difficult to learn the distinctive features needed for precise segmentation. We hypothesise that the key to overcoming the these challenges lies in the specific spatial patterns of leaf distribution. In this paper, we propose the Guided Mask Transformer (GMT), which leverages and integrates leaf spatial distribution priors into a Transformer-based segmentor. These spatial priors are embedded in a set of guide functions that map leaves at different positions into a more separable embedding space. Our GMT consistently outperforms the state-of-the-art on three public plant datasets. Our code is available at https://github.com/vios-s/gmt-leaf-ins-seg.
FaceScore: Benchmarking and Enhancing Face Quality in Human Generation
Authors: Zhenyi Liao, Qingsong Xie, Chen Chen, Hannan Lu, Zhijie Deng
Date: 2024-06-24 00:00:00
Tasks: Benchmarking, Denoising, Face Generation, Image Generation, Text to Image Generation, Text-to-Image Generation

Diffusion models (DMs) have achieved significant success in generating imaginative images given textual descriptions. However, they are likely to fall short when it comes to real-life scenarios with intricate details. The low-quality, unrealistic human faces in text-to-image generation are one of the most prominent issues, hindering the wide application of DMs in practice. Targeting addressing such an issue, we first assess the face quality of generations from popular pre-trained DMs with the aid of human annotators and then evaluate the alignment between existing metrics with human judgments. Observing that existing metrics can be unsatisfactory for quantifying face quality, we develop a novel metric named FaceScore (FS) by fine-tuning the widely used ImageReward on a dataset of (win, loss) face pairs cheaply crafted by an inpainting pipeline of DMs. Extensive studies reveal FS enjoys a superior alignment with humans. On the other hand, FS opens up the door for enhancing DMs for better face generation. With FS offering image ratings, we can easily perform preference learning algorithms to refine DMs like SDXL. Comprehensive experiments verify the efficacy of our approach for improving face quality. The code is released at https://github.com/OPPO-Mente-Lab/FaceScore.
Multi-Aperture Fusion of Transformer-Convolutional Network (MFTC-Net) for 3D Medical Image Segmentation and Visualization
Authors: Siyavash Shabani, Muhammad Sohaib, Sahar A. Mohammed, Bahram Parvin
Date: 2024-06-24 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Semantic Segmentation

Vision Transformers have shown superior performance to the traditional convolutional-based frameworks in many vision applications, including but not limited to the segmentation of 3D medical images. To further advance this area, this study introduces the Multi-Aperture Fusion of Transformer-Convolutional Network (MFTC-Net), which integrates the output of Swin Transformers and their corresponding convolutional blocks using 3D fusion blocks. The Multi-Aperture incorporates each image patch at its original resolutions with its pyramid representation to better preserve minute details. The proposed architecture has demonstrated a score of 89.73 and 7.31 for Dice and HD95, respectively, on the Synapse multi-organs dataset an improvement over the published results. The improved performance also comes with the added benefits of the reduced complexity of approximately 40 million parameters. Our code is available at https://github.com/Siyavashshabani/MFTC-Net
Reducing the Memory Footprint of 3D Gaussian Splatting
Authors: Panagiotis Papantonakis, Georgios Kopanas, Bernhard Kerbl, Alexandre Lanvin, George Drettakis
Date: 2024-06-24 00:00:00
Tasks: Novel View Synthesis, Quantization

3D Gaussian splatting provides excellent visual quality for novel view synthesis, with fast training and real-time rendering; unfortunately, the memory requirements of this method for storing and transmission are unreasonably high. We first analyze the reasons for this, identifying three main areas where storage can be reduced: the number of 3D Gaussian primitives used to represent a scene, the number of coefficients for the spherical harmonics used to represent directional radiance, and the precision required to store Gaussian primitive attributes. We present a solution to each of these issues. First, we propose an efficient, resolution-aware primitive pruning approach, reducing the primitive count by half. Second, we introduce an adaptive adjustment method to choose the number of coefficients used to represent directional radiance for each Gaussian primitive, and finally a codebook-based quantization method, together with a half-float representation for further memory reduction. Taken together, these three components result in a 27 reduction in overall size on disk on the standard datasets we tested, along with a 1.7 speedup in rendering speed. We demonstrate our method on standard datasets and show how our solution results in significantly reduced download times when using the method on a mobile device.
Leveraging Knowledge Distillation for Lightweight Skin Cancer Classification: Balancing Accuracy and Computational Efficiency
Authors: Niful Islam, Khan Md Hasib, Fahmida Akter Joti, Asif Karim, Sami Azam
Date: 2024-06-24 00:00:00
Tasks: Cancer Classification, Computational Efficiency, Data Augmentation, Knowledge Distillation, Quantization, Skin Cancer Classification

Skin cancer is a major concern to public health, accounting for one-third of the reported cancers. If not detected early, the cancer has the potential for severe consequences. Recognizing the critical need for effective skin cancer classification, we address the limitations of existing models, which are often too large to deploy in areas with limited computational resources. In response, we present a knowledge distillation based approach for creating a lightweight yet high-performing classifier. The proposed solution involves fusing three models, namely ResNet152V2, ConvNeXtBase, and ViT Base, to create an effective teacher model. The teacher model is then employed to guide a lightweight student model of size 2.03 MB. This student model is further compressed to 469.77 KB using 16-bit quantization, enabling smooth incorporation into edge devices. With six-stage image preprocessing, data augmentation, and a rigorous ablation study, the model achieves an impressive accuracy of 98.75% on the HAM10000 dataset and 98.94% on the Kaggle dataset in classifying benign and malignant skin cancers. With its high accuracy and compact size, our model appears to be a potential choice for accurate skin cancer classification, particularly in resource-constrained settings.
Enhancing Scientific Figure Captioning Through Cross-modal Learning
Authors: Mateo Alejandro Rojas, Rafael Carranza
Date: 2024-06-24 00:00:00
Tasks: Diversity, Image Captioning, Information Retrieval, Text Summarization

Scientific charts are essential tools for effectively communicating research findings, serving as a vital medium for conveying information and revealing data patterns. With the rapid advancement of science and technology, coupled with the advent of the big data era, the volume and diversity of scientific research data have surged, leading to an increase in the number and variety of charts. This trend presents new challenges for researchers, particularly in efficiently and accurately generating appropriate titles for these charts to better convey their information and results. Automatically generated chart titles can enhance information retrieval systems by providing precise data for detailed chart classification. As research in image captioning and text summarization matures, the automatic generation of scientific chart titles has gained significant attention. By leveraging natural language processing, machine learning, and multimodal techniques, it is possible to automatically extract key information from charts and generate accurate, concise titles that better serve the needs of researchers. This paper presents a novel approach to scientific chart title generation, demonstrating its effectiveness in improving the clarity and accessibility of research data.
DWARF: Disease-weighted network for attention map refinement
Authors: Haozhe Luo, Aurélie Pahud de Mortanges, Oana Inel, Abraham Bernstein, Mauricio Reyes
Date: 2024-06-24 00:00:00
Tasks: Diagnostic, Medical Image Analysis

The interpretability of deep learning is crucial for evaluating the reliability of medical imaging models and reducing the risks of inaccurate patient recommendations. This study addresses the "human out of the loop" and "trustworthiness" issues in medical image analysis by integrating medical professionals into the interpretability process. We propose a disease-weighted attention map refinement network (DWARF) that leverages expert feedback to enhance model relevance and accuracy. Our method employs cyclic training to iteratively improve diagnostic performance, generating precise and interpretable feature maps. Experimental results demonstrate significant improvements in interpretability and diagnostic accuracy across multiple medical imaging datasets. This approach fosters effective collaboration between AI systems and healthcare professionals, ultimately aiming to improve patient outcomes
PVUW 2024 Challenge on Complex Video Understanding: Methods and Results
Authors: Henghui Ding, Chang Liu, Yunchao Wei, Nikhila Ravi, Shuting He, Song Bai, Philip Torr, Deshui Miao, Xin Li, Zhenyu He, YaoWei Wang, Ming-Hsuan Yang, Zhensong Xu, Jiangtao Yao, Chengjing Wu, Ting Liu, Luoqi Liu, Xinyu Liu, Jing Zhang, Kexin Zhang, Yuting Yang, Licheng Jiao, Shuyuan Yang, Mingqi Gao, Jingnan Luo, Jinyu Yang, Jungong Han, Feng Zheng, Bin Cao, Yisi Zhang, Xuanxu Lin, Xingjian He, Bo Zhao, Jing Liu, Feiyu Pan, Hao Fang, Xiankai Lu
Date: 2024-06-24 00:00:00
Tasks: Segmentation, Semantic Segmentation, valid, Video Object Segmentation, Video Segmentation, Video Semantic Segmentation, Video Understanding

Pixel-level Video Understanding in the Wild Challenge (PVUW) focus on complex video understanding. In this CVPR 2024 workshop, we add two new tracks, Complex Video Object Segmentation Track based on MOSE dataset and Motion Expression guided Video Segmentation track based on MeViS dataset. In the two new tracks, we provide additional videos and annotations that feature challenging elements, such as the disappearance and reappearance of objects, inconspicuous small objects, heavy occlusions, and crowded environments in MOSE. Moreover, we provide a new motion expression guided video segmentation dataset MeViS to study the natural language-guided video understanding in complex environments. These new videos, sentences, and annotations enable us to foster the development of a more comprehensive and robust pixel-level understanding of video scenes in complex environments and realistic scenarios. The MOSE challenge had 140 registered teams in total, 65 teams participated the validation phase and 12 teams made valid submissions in the final challenge phase. The MeViS challenge had 225 registered teams in total, 50 teams participated the validation phase and 5 teams made valid submissions in the final challenge phase.
Are Vision xLSTM Embedded UNet More Reliable in Medical 3D Image Segmentation?
Authors: Pallabi Dutta, Soham Bose, Swalpa Kumar Roy, Sushmita Mitra
Date: 2024-06-24 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

The development of efficient segmentation strategies for medical images has evolved from its initial dependence on Convolutional Neural Networks (CNNs) to the current investigation of hybrid models that combine CNNs with Vision Transformers. There is an increasing focus on creating architectures that are both high-performance and computationally efficient, able to be deployed on remote systems with limited resources. Although transformers can capture global dependencies in the input space, they face challenges from the corresponding high computational and storage expenses involved. This paper investigates the integration of CNNs with Vision Extended Long Short-Term Memory (Vision-xLSTM)s by introducing the novel {\it \textbf{U-VixLSTM}}. The Vision-xLSTM blocks capture temporal and global relationships within the patches, as extracted from the CNN feature maps. The convolutional feature reconstruction path upsamples the output volume from the Vision-xLSTM blocks, to produce the segmentation output. Our primary objective is to propose that Vision-xLSTM forms an appropriate backbone for medical image segmentation, offering excellent performance with reduced computational costs. The U-VixLSTM exhibits superior performance, compared to the state-of-the-art networks in the publicly available Synapse, ISIC and ACDC datasets. Code provided: https://github.com/duttapallabi2907/U-VixLSTM
Mitigating Noisy Supervision Using Synthetic Samples with Soft Labels
Authors: Yangdi Lu, Wenbo He
Date: 2024-06-22 00:00:00

Noisy labels are ubiquitous in real-world datasets, especially in the large-scale ones derived from crowdsourcing and web searching. It is challenging to train deep neural networks with noisy datasets since the networks are prone to overfitting the noisy labels during training, resulting in poor generalization performance. During an early learning phase, deep neural networks have been observed to fit the clean samples before memorizing the mislabeled samples. In this paper, we dig deeper into the representation distributions in the early learning phase and find that, regardless of their noisy labels, learned representations of images from the same category still congregate together. Inspired by it, we propose a framework that trains the model with new synthetic samples to mitigate the impact of noisy labels. Specifically, we propose a mixing strategy to create the synthetic samples by aggregating original samples with their top-K nearest neighbours, wherein the weights are calculated using a mixture model learning from the per-sample loss distribution. To enhance the performance in the presence of extreme label noise, we estimate the soft targets by gradually correcting the noisy labels. Furthermore, we demonstrate that the estimated soft targets yield a more accurate approximation to ground truth labels and the proposed method produces a superior quality of learned representations with more separated and clearly bounded clusters. The extensive experiments in two benchmarks (CIFAR-10 and CIFAR-100) and two larg-scale real-world datasets (Clothing1M and Webvision) demonstrate that our approach outperforms the state-of-the-art methods and robustness of the learned representation.
SRViT: Vision Transformers for Estimating Radar Reflectivity from Satellite Observations at Scale
Authors: Jason Stock, Kyle Hilburn, Imme Ebert-Uphoff, Charles Anderson
Date: 2024-06-20 00:00:00

We introduce a transformer-based neural network to generate high-resolution (3km) synthetic radar reflectivity fields at scale from geostationary satellite imagery. This work aims to enhance short-term convective-scale forecasts of high-impact weather events and aid in data assimilation for numerical weather prediction over the United States. Compared to convolutional approaches, which have limited receptive fields, our results show improved sharpness and higher accuracy across various composite reflectivity thresholds. Additional case studies over specific atmospheric phenomena support our quantitative findings, while a novel attribution method is introduced to guide domain experts in understanding model outputs.
Interpreting Attention Layer Outputs with Sparse Autoencoders
Authors: Connor Kissane, Robert Krzyzanowski, Joseph Isaac Bloom, Arthur Conmy, Neel Nanda
Date: 2024-06-25 00:00:00

Decomposing model activations into interpretable components is a key open problem in mechanistic interpretability. Sparse autoencoders (SAEs) are a popular method for decomposing the internal activations of trained transformers into sparse, interpretable features, and have been applied to MLP layers and the residual stream. In this work we train SAEs on attention layer outputs and show that also here SAEs find a sparse, interpretable decomposition. We demonstrate this on transformers from several model families and up to 2B parameters. We perform a qualitative study of the features computed by attention layers, and find multiple families: long-range context, short-range context and induction features. We qualitatively study the role of every head in GPT-2 Small, and estimate that at least 90% of the heads are polysemantic, i.e. have multiple unrelated roles. Further, we show that Sparse Autoencoders are a useful tool that enable researchers to explain model behavior in greater detail than prior work. For example, we explore the mystery of why models have so many seemingly redundant induction heads, use SAEs to motivate the hypothesis that some are long-prefix whereas others are short-prefix, and confirm this with more rigorous analysis. We use our SAEs to analyze the computation performed by the Indirect Object Identification circuit (Wang et al.), validating that the SAEs find causally meaningful intermediate variables, and deepening our understanding of the semantics of the circuit. We open-source the trained SAEs and a tool for exploring arbitrary prompts through the lens of Attention Output SAEs.
A New Perspective on Shampoo's Preconditioner
Authors: Depen Morwani, Itai Shapira, Nikhil Vyas, Eran Malach, Sham Kakade, Lucas Janson
Date: 2024-06-25 00:00:00

Shampoo, a second-order optimization algorithm which uses a Kronecker product preconditioner, has recently garnered increasing attention from the machine learning community. The preconditioner used by Shampoo can be viewed either as an approximation of the Gauss--Newton component of the Hessian or the covariance matrix of the gradients maintained by Adagrad. We provide an explicit and novel connection between the $\textit{optimal}$ Kronecker product approximation of these matrices and the approximation made by Shampoo. Our connection highlights a subtle but common misconception about Shampoo's approximation. In particular, the $\textit{square}$ of the approximation used by the Shampoo optimizer is equivalent to a single step of the power iteration algorithm for computing the aforementioned optimal Kronecker product approximation. Across a variety of datasets and architectures we empirically demonstrate that this is close to the optimal Kronecker product approximation. Additionally, for the Hessian approximation viewpoint, we empirically study the impact of various practical tricks to make Shampoo more computationally efficient (such as using the batch gradient and the empirical Fisher) on the quality of Hessian approximation.
Probing the effects of broken symmetries in machine learning
Authors: Marcel F. Langer, Sergey N. Pozdnyakov, Michele Ceriotti
Date: 2024-06-25 00:00:00
Tasks: Inductive Bias

Symmetry is one of the most central concepts in physics, and it is no surprise that it has also been widely adopted as an inductive bias for machine-learning models applied to the physical sciences. This is especially true for models targeting the properties of matter at the atomic scale. Both established and state-of-the-art approaches, with almost no exceptions, are built to be exactly equivariant to translations, permutations, and rotations of the atoms. Incorporating symmetries -- rotations in particular -- constrains the model design space and implies more complicated architectures that are often also computationally demanding. There are indications that non-symmetric models can easily learn symmetries from data, and that doing so can even be beneficial for the accuracy of the model. We put a model that obeys rotational invariance only approximately to the test, in realistic scenarios involving simulations of gas-phase, liquid, and solid water. We focus specifically on physical observables that are likely to be affected -- directly or indirectly -- by symmetry breaking, finding negligible consequences when the model is used in an interpolative, bulk, regime. Even for extrapolative gas-phase predictions, the model remains very stable, even though symmetry artifacts are noticeable. We also discuss strategies that can be used to systematically reduce the magnitude of symmetry breaking when it occurs, and assess their impact on the convergence of observables.