pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic and Compressive Adaptation of Transformers From Images to Videos
Authors: Guozhen Zhang, Jingyu Liu, Shengming Cao, Xiaotong Zhao, Kevin Zhao, Kai Ma, LiMin Wang
Date: 2024-08-13 00:00:00
Tasks: Image-text matching, Text Matching

Recently, the remarkable success of pre-trained Vision Transformers (ViTs) from image-text matching has sparked an interest in image-to-video adaptation. However, most current approaches retain the full forward pass for each frame, leading to a high computation overhead for processing entire videos. In this paper, we present InTI, a novel approach for compressive image-to-video adaptation using dynamic Inter-frame Token Interpolation. InTI aims to softly preserve the informative tokens without disrupting their coherent spatiotemporal structure. Specifically, each token pair at identical positions within neighbor frames is linearly aggregated into a new token, where the aggregation weights are generated by a multi-scale context-aware network. In this way, the information of neighbor frames can be adaptively compressed in a point-by-point manner, thereby effectively reducing the number of processed frames by half each time. Importantly, InTI can be seamlessly integrated with existing adaptation methods, achieving strong performance without extra-complex design. On Kinetics-400, InTI reaches a top-1 accuracy of 87.1 with a remarkable 37.5% reduction in GFLOPs compared to naive adaptation. When combined with additional temporal modules, InTI achieves a top-1 accuracy of 87.6 with a 37% reduction in GFLOPs. Similar conclusions have been verified in other common datasets.
GLGait: A Global-Local Temporal Receptive Field Network for Gait Recognition in the Wild
Authors: Guozhen Peng, Yunhong Wang, Yuwei Zhao, Shaoxiong Zhang, Annan Li
Date: 2024-08-13 00:00:00
Tasks: Gait Recognition, Gait Recognition in the Wild, Triplet

Gait recognition has attracted increasing attention from academia and industry as a human recognition technology from a distance in non-intrusive ways without requiring cooperation. Although advanced methods have achieved impressive success in lab scenarios, most of them perform poorly in the wild. Recently, some Convolution Neural Networks (ConvNets) based methods have been proposed to address the issue of gait recognition in the wild. However, the temporal receptive field obtained by convolution operations is limited for long gait sequences. If directly replacing convolution blocks with visual transformer blocks, the model may not enhance a local temporal receptive field, which is important for covering a complete gait cycle. To address this issue, we design a Global-Local Temporal Receptive Field Network (GLGait). GLGait employs a Global-Local Temporal Module (GLTM) to establish a global-local temporal receptive field, which mainly consists of a Pseudo Global Temporal Self-Attention (PGTA) and a temporal convolution operation. Specifically, PGTA is used to obtain a pseudo global temporal receptive field with less memory and computation complexity compared with a multi-head self-attention (MHSA). The temporal convolution operation is used to enhance the local temporal receptive field. Besides, it can also aggregate pseudo global temporal receptive field to a true holistic temporal receptive field. Furthermore, we also propose a Center-Augmented Triplet Loss (CTL) in GLGait to reduce the intra-class distance and expand the positive samples in the training stage. Extensive experiments show that our method obtains state-of-the-art results on in-the-wild datasets, $i.e.$, Gait3D and GREW. The code is available at https://github.com/bgdpgz/GLGait.
FlatFusion: Delving into Details of Sparse Transformer-based Camera-LiDAR Fusion for Autonomous Driving
Authors: Yutao Zhu, Xiaosong Jia, Xinyu Yang, Junchi Yan
Date: 2024-08-13 00:00:00
Tasks: Autonomous Driving, Image to 3D

The integration of data from diverse sensor modalities (e.g., camera and LiDAR) constitutes a prevalent methodology within the ambit of autonomous driving scenarios. Recent advancements in efficient point cloud transformers have underscored the efficacy of integrating information in sparse formats. When it comes to fusion, since image patches are dense in pixel space with ambiguous depth, it necessitates additional design considerations for effective fusion. In this paper, we conduct a comprehensive exploration of design choices for Transformer-based sparse cameraLiDAR fusion. This investigation encompasses strategies for image-to-3D and LiDAR-to-2D mapping, attention neighbor grouping, single modal tokenizer, and micro-structure of Transformer. By amalgamating the most effective principles uncovered through our investigation, we introduce FlatFusion, a carefully designed framework for sparse camera-LiDAR fusion. Notably, FlatFusion significantly outperforms state-of-the-art sparse Transformer-based methods, including UniTR, CMT, and SparseFusion, achieving 73.7 NDS on the nuScenes validation set with 10.1 FPS with PyTorch.
PIR: Photometric Inverse Rendering with Shading Cues Modeling and Surface Reflectance Regularization
Authors: Jingzhi Bao, GuanYing Chen, Shuguang Cui
Date: 2024-08-13 00:00:00
Tasks: Inverse Rendering, Position

This paper addresses the problem of inverse rendering from photometric images. Existing approaches for this problem suffer from the effects of self-shadows, inter-reflections, and lack of constraints on the surface reflectance, leading to inaccurate decomposition of reflectance and illumination due to the ill-posed nature of inverse rendering. In this work, we propose a new method for neural inverse rendering. Our method jointly optimizes the light source position to account for the self-shadows in images, and computes indirect illumination using a differentiable rendering layer and an importance sampling strategy. To enhance surface reflectance decomposition, we introduce a new regularization by distilling DINO features to foster accurate and consistent material decomposition. Extensive experiments on synthetic and real datasets demonstrate that our method outperforms the state-of-the-art methods in reflectance decomposition.
Membership Inference Attack Against Masked Image Modeling
Authors: Zheng Li, Xinlei He, Ning Yu, Yang Zhang
Date: 2024-08-13 00:00:00
Tasks: Inference Attack, Membership Inference Attack, Self-Supervised Learning

Masked Image Modeling (MIM) has achieved significant success in the realm of self-supervised learning (SSL) for visual recognition. The image encoder pre-trained through MIM, involving the masking and subsequent reconstruction of input images, attains state-of-the-art performance in various downstream vision tasks. However, most existing works focus on improving the performance of MIM.In this work, we take a different angle by studying the pre-training data privacy of MIM. Specifically, we propose the first membership inference attack against image encoders pre-trained by MIM, which aims to determine whether an image is part of the MIM pre-training dataset. The key design is to simulate the pre-training paradigm of MIM, i.e., image masking and subsequent reconstruction, and then obtain reconstruction errors. These reconstruction errors can serve as membership signals for achieving attack goals, as the encoder is more capable of reconstructing the input image in its training set with lower errors. Extensive evaluations are conducted on three model architectures and three benchmark datasets. Empirical results show that our attack outperforms baseline methods. Additionally, we undertake intricate ablation studies to analyze multiple factors that could influence the performance of the attack.
Structure-preserving Planar Simplification for Indoor Environments
Authors: Bishwash Khanal, Sanjay Rijal, Manish Awale, Vaghawan Ojha
Date: 2024-08-13 00:00:00
Tasks: Surface Reconstruction

This paper presents a novel approach for structure-preserving planar simplification of indoor scene point clouds for both simulated and real-world environments. Initially, the scene point cloud undergoes preprocessing steps, including noise reduction and Manhattan world alignment, to ensure robustness and coherence in subsequent analyses. We segment each captured scene into structured (walls-ceiling-floor) and non-structured (indoor objects) scenes. Leveraging a RANSAC algorithm, we extract primitive planes from the input point cloud, facilitating the segmentation and simplification of the structured scene. The best-fitting wall meshes are then generated from the primitives, followed by adjacent mesh merging with the vertex-translation algorithm which preserves the mesh layout. To accurately represent ceilings and floors, we employ the mesh clipping algorithm which clips the ceiling and floor meshes with respect to wall normals. In the case of indoor scenes, we apply a surface reconstruction technique to enhance the fidelity. This paper focuses on the intricate steps of the proposed scene simplification methodology, addressing complex scenarios such as multi-story and slanted walls and ceilings. We also conduct qualitative and quantitative performance comparisons against popular surface reconstruction, shape approximation, and floorplan generation approaches.
Oracle Bone Script Similiar Character Screening Approach Based on Simsiam Contrastive Learning and Supervised Learning
Authors: Xinying Weng, YiFan Li, Shuaidong Hao, Jialiang Hou
Date: 2024-08-13 00:00:00
Tasks: Contrastive Learning

This project proposes a new method that uses fuzzy comprehensive evaluation method to integrate ResNet-50 self-supervised and RepVGG supervised learning. The source image dataset HWOBC oracle is taken as input, the target image is selected, and finally the most similar image is output in turn without any manual intervention. The same feature encoding method is not used for images of different modalities. Before the model training, the image data is preprocessed, and the image is enhanced by random rotation processing, self-square graph equalization theory algorithm, and gamma transform, which effectively enhances the key feature learning. Finally, the fuzzy comprehensive evaluation method is used to combine the results of supervised training and unsupervised training, which can better solve the "most similar" problem that is difficult to quantify. At present, there are many unknown oracle-bone inscriptions waiting for us to crack. Contacting with the glyphs can provide new ideas for cracking.
Token Compensator: Altering Inference Cost of Vision Transformer without Re-Tuning
Authors: Shibo Jie, Yehui Tang, Jianyuan Guo, Zhi-Hong Deng, Kai Han, Yunhe Wang
Date: 2024-08-13 00:00:00
Tasks: Fine-Grained Image Classification

Token compression expedites the training and inference of Vision Transformers (ViTs) by reducing the number of the redundant tokens, e.g., pruning inattentive tokens or merging similar tokens. However, when applied to downstream tasks, these approaches suffer from significant performance drop when the compression degrees are mismatched between training and inference stages, which limits the application of token compression on off-the-shelf trained models. In this paper, we propose a model arithmetic framework to decouple the compression degrees between the two stages. In advance, we additionally perform a fast parameter-efficient self-distillation stage on the pre-trained models to obtain a small plugin, called Token Compensator (ToCom), which describes the gap between models across different compression degrees. During inference, ToCom can be directly inserted into any downstream off-the-shelf models with any mismatched training and inference compression degrees to acquire universal performance improvements without further training. Experiments on over 20 downstream tasks demonstrate the effectiveness of our framework. On CIFAR100, fine-grained visual classification, and VTAB-1k, ToCom can yield up to a maximum improvement of 2.3%, 1.5%, and 2.0% in the average performance of DeiT-B, respectively. Code: https://github.com/JieShibo/ToCom
Visual Neural Decoding via Improved Visual-EEG Semantic Consistency
Authors: Hongzhou Chen, Lianghua He, Yihang Liu, Longzhen Yang
Date: 2024-08-13 00:00:00
Tasks: EEG, Metric Learning

Visual neural decoding refers to the process of extracting and interpreting original visual experiences from human brain activity. Recent advances in metric learning-based EEG visual decoding methods have delivered promising results and demonstrated the feasibility of decoding novel visual categories from brain activity. However, methods that directly map EEG features to the CLIP embedding space may introduce mapping bias and cause semantic inconsistency among features, thereby degrading alignment and impairing decoding performance. To further explore the semantic consistency between visual and neural signals. In this work, we construct a joint semantic space and propose a Visual-EEG Semantic Decouple Framework that explicitly extracts the semantic-related features of these two modalities to facilitate optimal alignment. Specifically, a cross-modal information decoupling module is introduced to guide the extraction of semantic-related information from modalities. Then, by quantifying the mutual information between visual image and EEG features, we observe a strong positive correlation between the decoding performance and the magnitude of mutual information. Furthermore, inspired by the mechanisms of visual object understanding from neuroscience, we propose an intra-class geometric consistency approach during the alignment process. This strategy maps visual samples within the same class to consistent neural patterns, which further enhances the robustness and the performance of EEG visual decoding. Experiments on a large Image-EEG dataset show that our method achieves state-of-the-art results in zero-shot neural decoding tasks.
Enhancing Diabetic Retinopathy Diagnosis: A Lightweight CNN Architecture for Efficient Exudate Detection in Retinal Fundus Images
Authors: Mujadded Al Rabbani Alif
Date: 2024-08-13 00:00:00
Tasks: Computational Efficiency, Diagnostic

Retinal fundus imaging plays an essential role in diagnosing various stages of diabetic retinopathy, where exudates are critical markers of early disease onset. Prompt detection of these exudates is pivotal for enabling optometrists to arrest or significantly decelerate the disease progression. This paper introduces a novel, lightweight convolutional neural network architecture tailored for automated exudate detection, designed to identify these markers efficiently and accurately. To address the challenge of limited training data, we have incorporated domain-specific data augmentations to enhance the model's generalizability. Furthermore, we applied a suite of regularization techniques within our custom architecture to boost diagnostic accuracy while optimizing computational efficiency. Remarkably, this streamlined model contains only 4.73 million parameters a reduction of nearly 60% compared to the standard ResNet-18 model, which has 11.69 million parameters. Despite its reduced complexity, our model achieves an impressive F1 score of 90%, demonstrating its efficacy in the early detection of diabetic retinopathy through fundus imaging.
Do Vision-Language Foundational models show Robust Visual Perception?
Authors: Shivam Chandhok, Pranav Tandon
Date: 2024-08-13 00:00:00
Tasks: image-classification, Image Classification, Zero-Shot Image Classification

Recent advances in vision-language foundational models have enabled development of systems that can perform visual understanding and reasoning tasks. However, it is unclear if these models are robust to distribution shifts, and how their performance and generalization capabilities vary under changes in data distribution. In this project we strive to answer the question "Are vision-language foundational models robust to distribution shifts like human perception?" Specifically, we consider a diverse range of vision-language models and compare how the performance of these systems is affected by corruption based distribution shifts (such as \textit{motion blur, fog, snow, gaussian noise}) commonly found in practical real-world scenarios. We analyse the generalization capabilities qualitatively and quantitatively on zero-shot image classification task under aforementioned distribution shifts. Our code will be avaible at \url{https://github.com/shivam-chandhok/CPSC-540-Project}
ED$^4$: Explicit Data-level Debiasing for Deepfake Detection
Authors: Jikang Cheng, Ying Zhang, Qin Zou, Zhiyuan Yan, Chao Liang, Zhongyuan Wang, Chen Li
Date: 2024-08-13 00:00:00
Tasks: DeepFake Detection, Disentanglement, Face Swapping

Learning intrinsic bias from limited data has been considered the main reason for the failure of deepfake detection with generalizability. Apart from the discovered content and specific-forgery bias, we reveal a novel spatial bias, where detectors inertly anticipate observing structural forgery clues appearing at the image center, also can lead to the poor generalization of existing methods. We present ED$^4$, a simple and effective strategy, to address aforementioned biases explicitly at the data level in a unified framework rather than implicit disentanglement via network design. In particular, we develop ClockMix to produce facial structure preserved mixtures with arbitrary samples, which allows the detector to learn from an exponentially extended data distribution with much more diverse identities, backgrounds, local manipulation traces, and the co-occurrence of multiple forgery artifacts. We further propose the Adversarial Spatial Consistency Module (AdvSCM) to prevent extracting features with spatial bias, which adversarially generates spatial-inconsistent images and constrains their extracted feature to be consistent. As a model-agnostic debiasing strategy, ED$^4$ is plug-and-play: it can be integrated with various deepfake detectors to obtain significant benefits. We conduct extensive experiments to demonstrate its effectiveness and superiority over existing deepfake detection approaches.
Sumotosima: A Framework and Dataset for Classifying and Summarizing Otoscopic Images
Authors: Eram Anwarul Khan, Anas Anwarul Haq Khan
Date: 2024-08-13 00:00:00
Tasks: Diagnostic, Triplet

Otoscopy is a diagnostic procedure to examine the ear canal and eardrum using an otoscope. It identifies conditions like infections, foreign bodies, ear drum perforations and ear abnormalities. We propose a novel resource efficient deep learning and transformer based framework, Sumotosima (Summarizer for otoscopic images), an end-to-end pipeline for classification followed by summarization. Our framework works on combination of triplet and cross-entropy losses. Additionally, we use Knowledge Enhanced Multimodal BART whose input is fused textual and image embedding. The objective is to provide summaries that are well-suited for patients, ensuring clarity and efficiency in understanding otoscopic images. Given the lack of existing datasets, we have curated our own OCASD (Otoscopic Classification And Summary Dataset), which includes 500 images with 5 unique categories annotated with their class and summaries by Otolaryngologists. Sumotosima achieved a result of 98.03%, which is 7.00%, 3.10%, 3.01% higher than K-Nearest Neighbors, Random Forest and Support Vector Machines, respectively, in classification tasks. For summarization, Sumotosima outperformed GPT-4o and LLaVA by 88.53% and 107.57% in ROUGE scores, respectively. We have made our code and dataset publicly available at https://github.com/anas2908/Sumotosima
Detecting Audio-Visual Deepfakes with Fine-Grained Inconsistencies
Authors: Marcella Astrid, Enjie Ghorbel, Djamila Aouada
Date: 2024-08-13 00:00:00
Tasks: DeepFake Detection, Face Swapping

Existing methods on audio-visual deepfake detection mainly focus on high-level features for modeling inconsistencies between audio and visual data. As a result, these approaches usually overlook finer audio-visual artifacts, which are inherent to deepfakes. Herein, we propose the introduction of fine-grained mechanisms for detecting subtle artifacts in both spatial and temporal domains. First, we introduce a local audio-visual model capable of capturing small spatial regions that are prone to inconsistencies with audio. For that purpose, a fine-grained mechanism based on a spatially-local distance coupled with an attention module is adopted. Second, we introduce a temporally-local pseudo-fake augmentation to include samples incorporating subtle temporal inconsistencies in our training set. Experiments on the DFDC and the FakeAVCeleb datasets demonstrate the superiority of the proposed method in terms of generalization as compared to the state-of-the-art under both in-dataset and cross-dataset settings.
ReCLIP++: Learn to Rectify the Bias of CLIP for Unsupervised Semantic Segmentation
Authors: Jingyun Wang, Guoliang Kang
Date: 2024-08-13 00:00:00
Tasks: Segmentation, Semantic Segmentation, Unsupervised Semantic Segmentation

Recent works utilize CLIP to perform the challenging unsupervised semantic segmentation task where only images without annotations are available. However, we observe that when adopting CLIP to such a pixel-level understanding task, unexpected bias (including class-preference bias and space-preference bias) occurs. Previous works don't explicitly model the bias, which largely constrains the segmentation performance. In this paper, we propose to explicitly model and rectify the bias existing in CLIP to facilitate the unsupervised semantic segmentation task. Specifically, we design a learnable "Reference" prompt to encode class-preference bias and a projection of the positional embedding in the vision transformer to encode space-preference bias respectively. To avoid interference, two kinds of biases are firstly independently encoded into different features, i.e., the Reference feature and the positional feature. Via a matrix multiplication between the Reference feature and the positional feature, a bias logit map is generated to explicitly represent two kinds of biases. Then we rectify the logits of CLIP via a simple element-wise subtraction. To make the rectified results smoother and more contextual, we design a mask decoder which takes the feature of CLIP and the rectified logits as input and outputs a rectified segmentation mask with the help of Gumbel-Softmax operation. A contrastive loss based on the masked visual features and the text features of different classes is imposed, which makes the bias modeling and rectification process meaningful and effective. Extensive experiments on various benchmarks including PASCAL VOC, PASCAL Context, ADE20K, Cityscapes, and COCO Stuff demonstrate that our method performs favorably against previous state-of-the-arts. The implementation is available at: https://github.com/dogehhh/ReCLIP.