pwc-archive/papers-with-abstracts huggingface dataset:
The evolution of volumetric video: A survey of smart transcoding and compression approaches
Authors: Preetish Kakkar, Hariharan Ragothaman
Date: 2024-11-04 00:00:00
Tasks: Video Compression

Volumetric video, the capture and display of three-dimensional (3D) imagery, has emerged as a revolutionary technology poised to transform the media landscape, enabling immersive experiences that transcend the limitations of traditional 2D video. One of the key challenges in this domain is the efficient delivery of these high-bandwidth, data-intensive volumetric video streams, which requires innovative transcoding and compression techniques. This research paper explores the state-of-the-art in volumetric video compression and delivery, with a focus on the potential of AI-driven solutions to address the unique challenges posed by this emerging medium.
GraphVL: Graph-Enhanced Semantic Modeling via Vision-Language Models for Generalized Class Discovery
Authors: Bhupendra Solanki, Ashwin Nair, Mainak Singha, Souradeep Mukhopadhyay, Ankit Jha, Biplab Banerjee
Date: 2024-11-04 00:00:00
Tasks: Language Modeling, Language Modelling

Generalized Category Discovery (GCD) aims to cluster unlabeled images into known and novel categories using labeled images from known classes. To address the challenge of transferring features from known to unknown classes while mitigating model bias, we introduce GraphVL, a novel approach for vision-language modeling in GCD, leveraging CLIP. Our method integrates a graph convolutional network (GCN) with CLIP's text encoder to preserve class neighborhood structure. We also employ a lightweight visual projector for image data, ensuring discriminative features through margin-based contrastive losses for image-text mapping. This neighborhood preservation criterion effectively regulates the semantic space, making it less sensitive to known classes. Additionally, we learn textual prompts from known classes and align them to create a more contextually meaningful semantic feature space for the GCN layer using a contextual similarity loss. Finally, we represent unlabeled samples based on their semantic distance to class prompts from the GCN, enabling semi-supervised clustering for class discovery and minimizing errors. Our experiments on seven benchmark datasets consistently demonstrate the superiority of GraphVL when integrated with the CLIP backbone.
Model Integrity when Unlearning with T2I Diffusion Models
Authors: Andrea Schioppa, Emiel Hoogeboom, Jonathan Heek
Date: 2024-11-04 00:00:00
Tasks: Machine Unlearning, model

The rapid advancement of text-to-image Diffusion Models has led to their widespread public accessibility. However these models, trained on large internet datasets, can sometimes generate undesirable outputs. To mitigate this, approximate Machine Unlearning algorithms have been proposed to modify model weights to reduce the generation of specific types of images, characterized by samples from a ``forget distribution'', while preserving the model's ability to generate other images, characterized by samples from a ``retain distribution''. While these methods aim to minimize the influence of training data in the forget distribution without extensive additional computation, we point out that they can compromise the model's integrity by inadvertently affecting generation for images in the retain distribution. Recognizing the limitations of FID and CLIPScore in capturing these effects, we introduce a novel retention metric that directly assesses the perceptual difference between outputs generated by the original and the unlearned models. We then propose unlearning algorithms that demonstrate superior effectiveness in preserving model integrity compared to existing baselines. Given their straightforward implementation, these algorithms serve as valuable benchmarks for future advancements in approximate Machine Unlearning for Diffusion Models.
AM Flow: Adapters for Temporal Processing in Action Recognition
Authors: Tanay Agrawal, Abid Ali, Antitza Dantcheva, Francois Bremond
Date: 2024-11-04 00:00:00
Tasks: Action Classification, Action Recognition, Transfer Learning, Video Classification

Deep learning models, in particular \textit{image} models, have recently gained generalisability and robustness. %are becoming more general and robust by the day. In this work, we propose to exploit such advances in the realm of \textit{video} classification. Video foundation models suffer from the requirement of extensive pretraining and a large training time. Towards mitigating such limitations, we propose "\textit{Attention Map (AM) Flow}" for image models, a method for identifying pixels relevant to motion in each input video frame. In this context, we propose two methods to compute AM flow, depending on camera motion. AM flow allows the separation of spatial and temporal processing, while providing improved results over combined spatio-temporal processing (as in video models). Adapters, one of the popular techniques in parameter efficient transfer learning, facilitate the incorporation of AM flow into pretrained image models, mitigating the need for full-finetuning. We extend adapters to "\textit{temporal processing adapters}" by incorporating a temporal processing unit into the adapters. Our work achieves faster convergence, therefore reducing the number of epochs needed for training. Moreover, we endow an image model with the ability to achieve state-of-the-art results on popular action recognition datasets. This reduces training time and simplifies pretraining. We present experiments on Kinetics-400, Something-Something v2, and Toyota Smarthome datasets, showcasing state-of-the-art or comparable results.
Exploiting Unlabeled Data with Multiple Expert Teachers for Open Vocabulary Aerial Object Detection and Its Orientation Adaptation
Authors: Yan Li, Weiwei Guo, Xue Yang, Ning Liao, Shaofeng Zhang, Yi Yu, Wenxian Yu, Junchi Yan
Date: 2024-11-04 00:00:00
Tasks: Earth Observation, Object, object-detection, Object Detection, Pseudo Label

In recent years, aerial object detection has been increasingly pivotal in various earth observation applications. However, current algorithms are limited to detecting a set of pre-defined object categories, demanding sufficient annotated training samples, and fail to detect novel object categories. In this paper, we put forth a novel formulation of the aerial object detection problem, namely open-vocabulary aerial object detection (OVAD), which can detect objects beyond training categories without costly collecting new labeled data. We propose CastDet, a CLIP-activated student-teacher detection framework that serves as the first OVAD detector specifically designed for the challenging aerial scenario, where objects often exhibit weak appearance features and arbitrary orientations. Our framework integrates a robust localization teacher along with several box selection strategies to generate high-quality proposals for novel objects. Additionally, the RemoteCLIP model is adopted as an omniscient teacher, which provides rich knowledge to enhance classification capabilities for novel categories. A dynamic label queue is devised to maintain high-quality pseudo-labels during training. By doing so, the proposed CastDet boosts not only novel object proposals but also classification. Furthermore, we extend our approach from horizontal OVAD to oriented OVAD with tailored algorithm designs to effectively manage bounding box representation and pseudo-label generation. Extensive experiments for both tasks on multiple existing aerial object detection datasets demonstrate the effectiveness of our approach. The code is available at https://github.com/lizzy8587/CastDet.
Tree level change detection over Ahmedabad city using very high resolution satellite images and Deep Learning
Authors: Jai G Singla, Gautam Jaiswal
Date: 2024-11-04 00:00:00
Tasks: Change Detection, Instance Segmentation, regression, Segmentation, Semantic Segmentation

In this study, 0.5m high resolution satellite datasets over Indian urban region was used to demonstrate the applicability of deep learning models over Ahmedabad, India. Here, YOLOv7 instance segmentation model was trained on well curated trees canopy dataset (6500 images) in order to carry out the change detection. During training, evaluation metrics such as bounding box regression and mask regression loss, mean average precision (mAP) and stochastic gradient descent algorithm were used for evaluating and optimizing the performance of model. After the 500 epochs, the mAP of 0.715 and 0.699 for individual tree detection and tree canopy mask segmentation were obtained. However, by further tuning hyper parameters of the model, maximum accuracy of 80 % of trees detection with false segmentation rate of 2% on data was obtained.
QCS: Feature Refining from Quadruplet Cross Similarity for Facial Expression Recognition
Authors: Chengpeng Wang, Li Chen, Lili Wang, Zhaofan Li, Xuebin Lv
Date: 2024-11-04 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER)

Facial expression recognition faces challenges where labeled significant features in datasets are mixed with unlabeled redundant ones. In this paper, we introduce Cross Similarity Attention (CSA) to mine richer intrinsic information from image pairs, overcoming a limitation when the Scaled Dot-Product Attention of ViT is directly applied to calculate the similarity between two different images. Based on CSA, we simultaneously minimize intra-class differences and maximize inter-class differences at the fine-grained feature level through interactions among multiple branches. Contrastive residual distillation is utilized to transfer the information learned in the cross module back to the base network. We ingeniously design a four-branch centrally symmetric network, named Quadruplet Cross Similarity (QCS), which alleviates gradient conflicts arising from the cross module and achieves balanced and stable training. It can adaptively extract discriminative features while isolating redundant ones. The cross-attention modules exist during training, and only one base branch is retained during inference, resulting in no increase in inference time. Extensive experiments show that our proposed method achieves state-of-the-art performance on several FER datasets.
Typicalness-Aware Learning for Failure Detection
Authors: Yijun Liu, Jiequan Cui, Zhuotao Tian, Senqiao Yang, Qingdong He, Xiaoling Wang, Jingyong Su
Date: 2024-11-04 00:00:00

Deep neural networks (DNNs) often suffer from the overconfidence issue, where incorrect predictions are made with high confidence scores, hindering the applications in critical systems. In this paper, we propose a novel approach called Typicalness-Aware Learning (TAL) to address this issue and improve failure detection performance. We observe that, with the cross-entropy loss, model predictions are optimized to align with the corresponding labels via increasing logit magnitude or refining logit direction. However, regarding atypical samples, the image content and their labels may exhibit disparities. This discrepancy can lead to overfitting on atypical samples, ultimately resulting in the overconfidence issue that we aim to address. To tackle the problem, we have devised a metric that quantifies the typicalness of each sample, enabling the dynamic adjustment of the logit magnitude during the training process. By allowing atypical samples to be adequately fitted while preserving reliable logit direction, the problem of overconfidence can be mitigated. TAL has been extensively evaluated on benchmark datasets, and the results demonstrate its superiority over existing failure detection methods. Specifically, TAL achieves a more than 5% improvement on CIFAR100 in terms of the Area Under the Risk-Coverage Curve (AURC) compared to the state-of-the-art. Code is available at https://github.com/liuyijungoon/TAL.
SPECTRUM: Semantic Processing and Emotion-informed video-Captioning Through Retrieval and Understanding Modalities
Authors: Ehsan Faghihi, Mohammedreza Zarenejad, Ali-Asghar Beheshti Shirazi
Date: 2024-11-04 00:00:00
Tasks: Attribute, Descriptive, Retrieval, Text Retrieval, Video Captioning, Video to Text Retrieval

Capturing a video's meaning and critical concepts by analyzing the subtle details is a fundamental yet challenging task in video captioning. Identifying the dominant emotional tone in a video significantly enhances the perception of its context. Despite a strong emphasis on video captioning, existing models often need to adequately address emotional themes, resulting in suboptimal captioning results. To address these limitations, this paper proposes a novel Semantic Processing and Emotion-informed video-Captioning Through Retrieval and Understanding Modalities (SPECTRUM) framework to empower the generation of emotionally and semantically credible captions. Leveraging our pioneering structure, SPECTRUM discerns multimodal semantics and emotional themes using Visual Text Attribute Investigation (VTAI) and determines the orientation of descriptive captions through a Holistic Concept-Oriented Theme (HCOT), expressing emotionally-informed and field-acquainted references. They exploit video-to-text retrieval capabilities and the multifaceted nature of video content to estimate the emotional probabilities of candidate captions. Then, the dominant theme of the video is determined by appropriately weighting embedded attribute vectors and applying coarse- and fine-grained emotional concepts, which define the video's contextual alignment. Furthermore, using two loss functions, SPECTRUM is optimized to integrate emotional information and minimize prediction errors. Extensive experiments on the EmVidCap, MSVD, and MSRVTT video captioning datasets demonstrate that our model significantly surpasses state-of-the-art methods. Quantitative and qualitative evaluations highlight the model's ability to accurately capture and convey video emotions and multimodal attributes.
UnSegMedGAT: Unsupervised Medical Image Segmentation using Graph Attention Networks Clustering
Authors: A. Mudit Adityaja, Saurabh J. Shigwan, Nitin Kumar
Date: 2024-11-04 00:00:00
Tasks: Clustering, Graph Attention, Image Segmentation, Medical Image Analysis, Medical Image Segmentation, Segmentation, Semantic Segmentation

The data-intensive nature of supervised classification drives the interest of the researchers towards unsupervised approaches, especially for problems such as medical image segmentation, where labeled data is scarce. Building on the recent advancements of Vision transformers (ViT) in computer vision, we propose an unsupervised segmentation framework using a pre-trained Dino-ViT. In the proposed method, we leverage the inherent graph structure within the image to realize a significant performance gain for segmentation in medical images. For this, we introduce a modularity-based loss function coupled with a Graph Attention Network (GAT) to effectively capture the inherent graph topology within the image. Our method achieves state-of-the-art performance, even significantly surpassing or matching that of existing (semi)supervised technique such as MedSAM which is a Segment Anything Model in medical images. We demonstrate this using two challenging medical image datasets ISIC-2018 and CVC-ColonDB. This work underscores the potential of unsupervised approaches in advancing medical image analysis in scenarios where labeled data is scarce. The github repository of the code is available on [https://github.com/mudit-adityaja/UnSegMedGAT].
Deep Learning for Leopard Individual Identification: An Adaptive Angular Margin Approach
Authors: David Colomer Matachana
Date: 2024-11-04 00:00:00
Tasks: Deep Learning, Edge Detection, Open Set Learning, Triplet

Accurate identification of individual leopards across camera trap images is critical for population monitoring and ecological studies. This paper introduces a deep learning framework to distinguish between individual leopards based on their unique spot patterns. This approach employs a novel adaptive angular margin method in the form of a modified CosFace architecture. In addition, I propose a preprocessing pipeline that combines RGB channels with an edge detection channel to underscore the critical features learned by the model. This approach significantly outperforms the Triplet Network baseline, achieving a Dynamic Top-5 Average Precision of 0.8814 and a Top-5 Rank Match Detection of 0.9533, demonstrating its potential for open-set learning in wildlife identification. While not surpassing the performance of the SIFT-based Hotspotter algorithm, this method represents a substantial advancement in applying deep learning to patterned wildlife identification. This research contributes to the field of computer vision and provides a valuable tool for biologists aiming to study and protect leopard populations. It also serves as a stepping stone for applying the power of deep learning in Capture-Recapture studies for other patterned species.
Robust plug-and-play methods for highly accelerated non-Cartesian MRI reconstruction
Authors: Pierre-Antoine Comby, Benjamin Lapostolle, Matthieu Terris, Philippe Ciuciu
Date: 2024-11-04 00:00:00
Tasks: compressed sensing, Denoising, Hallucination, MRI Reconstruction

Achieving high-quality Magnetic Resonance Imaging (MRI) reconstruction at accelerated acquisition rates remains challenging due to the inherent ill-posed nature of the inverse problem. Traditional Compressed Sensing (CS) methods, while robust across varying acquisition settings, struggle to maintain good reconstruction quality at high acceleration factors ($\ge$ 8). Recent advances in deep learning have improved reconstruction quality, but purely data-driven methods are prone to overfitting and hallucination effects, notably when the acquisition setting is varying. Plug-and-Play (PnP) approaches have been proposed to mitigate the pitfalls of both frameworks. In a nutshell, PnP algorithms amount to replacing suboptimal handcrafted CS priors with powerful denoising deep neural network (DNNs). However, in MRI reconstruction, existing PnP methods often yield suboptimal results due to instabilities in the proximal gradient descent (PGD) schemes and the lack of curated, noiseless datasets for training robust denoisers. In this work, we propose a fully unsupervised preprocessing pipeline to generate clean, noiseless complex MRI signals from multicoil data, enabling training of a high-performance denoising DNN. Furthermore, we introduce an annealed Half-Quadratic Splitting (HQS) algorithm to address the instability issues, leading to significant improvements over existing PnP algorithms. When combined with preconditioning techniques, our approach achieves state-of-the-art results, providing a robust and efficient solution for high-quality MRI reconstruction.
3D Equivariant Pose Regression via Direct Wigner-D Harmonics Prediction
Authors: Jongmin Lee, Minsu Cho
Date: 2024-11-01 00:00:00
Tasks: Computational Efficiency, Pose Estimation, regression

Determining the 3D orientations of an object in an image, known as single-image pose estimation, is a crucial task in 3D vision applications. Existing methods typically learn 3D rotations parametrized in the spatial domain using Euler angles or quaternions, but these representations often introduce discontinuities and singularities. SO(3)-equivariant networks enable the structured capture of pose patterns with data-efficient learning, but the parametrizations in spatial domain are incompatible with their architecture, particularly spherical CNNs, which operate in the frequency domain to enhance computational efficiency. To overcome these issues, we propose a frequency-domain approach that directly predicts Wigner-D coefficients for 3D rotation regression, aligning with the operations of spherical CNNs. Our SO(3)-equivariant pose harmonics predictor overcomes the limitations of spatial parameterizations, ensuring consistent pose estimation under arbitrary rotations. Trained with a frequency-domain regression loss, our method achieves state-of-the-art results on benchmarks such as ModelNet10-SO(3) and PASCAL3D+, with significant improvements in accuracy, robustness, and data efficiency.
Learning Where to Edit Vision Transformers
Authors: Yunqiao Yang, Long-Kai Huang, Shengzhuang Chen, Kede Ma, Ying WEI
Date: 2024-11-04 00:00:00
Tasks: Meta-Learning, Model Editing, Object Recognition

Model editing aims to data-efficiently correct predictive errors of large pre-trained models while ensuring generalization to neighboring failures and locality to minimize unintended effects on unrelated examples. While significant progress has been made in editing Transformer-based large language models, effective strategies for editing vision Transformers (ViTs) in computer vision remain largely untapped. In this paper, we take initial steps towards correcting predictive errors of ViTs, particularly those arising from subpopulation shifts. Taking a locate-then-edit approach, we first address the where-to-edit challenge by meta-learning a hypernetwork on CutMix-augmented data generated for editing reliability. This trained hypernetwork produces generalizable binary masks that identify a sparse subset of structured model parameters, responsive to real-world failure samples. Afterward, we solve the how-to-edit problem by simply fine-tuning the identified parameters using a variant of gradient descent to achieve successful edits. To validate our method, we construct an editing benchmark that introduces subpopulation shifts towards natural underrepresented images and AI-generated images, thereby revealing the limitations of pre-trained ViTs for object recognition. Our approach not only achieves superior performance on the proposed benchmark but also allows for adjustable trade-offs between generalization and locality. Our code is available at https://github.com/hustyyq/Where-to-Edit.
Exploiting Contextual Uncertainty of Visual Data for Efficient Training of Deep Models
Authors: Sharat Agarwal
Date: 2024-11-04 00:00:00
Tasks: Active Learning, image-classification, Image Classification, Image Retrieval, Semantic Segmentation

Objects, in the real world, rarely occur in isolation and exhibit typical arrangements governed by their independent utility, and their expected interaction with humans and other objects in the context. For example, a chair is expected near a table, and a computer is expected on top. Humans use this spatial context and relative placement as an important cue for visual recognition in case of ambiguities. Similar to human's, DNN's exploit contextual information from data to learn representations. Our research focuses on harnessing the contextual aspects of visual data to optimize data annotation and enhance the training of deep networks. Our contributions can be summarized as follows: (1) We introduce the notion of contextual diversity for active learning CDAL and show its applicability in three different visual tasks semantic segmentation, object detection and image classification, (2) We propose a data repair algorithm to curate contextually fair data to reduce model bias, enabling the model to detect objects out of their obvious context, (3) We propose Class-based annotation, where contextually relevant classes are selected that are complementary for model training under domain shift. Understanding the importance of well-curated data, we also emphasize the necessity of involving humans in the loop to achieve accurate annotations and to develop novel interaction strategies that allow humans to serve as fact-checkers. In line with this we are working on developing image retrieval system for wildlife camera trap images and reliable warning system for poor quality rural roads. For large-scale annotation, we are employing a strategic combination of human expertise and zero-shot models, while also integrating human input at various stages for continuous feedback.