pwc-archive/papers-with-abstracts huggingface dataset:
MSCPT: Few-shot Whole Slide Image Classification with Multi-scale and Context-focused Prompt Tuning
Authors: Minghao Han, Linhao Qu, Dingkang Yang, Xukun Zhang, Xiaoying Wang, Lihua Zhang
Date: 2024-08-21 00:00:00
Tasks: image-classification, Image Classification, Language Modelling, Large Language Model, Multiple Instance Learning, Weakly Supervised Classification, whole slide images

Multiple instance learning (MIL) has become a standard paradigm for weakly supervised classification of whole slide images (WSI). However, this paradigm relies on the use of a large number of labelled WSIs for training. The lack of training data and the presence of rare diseases present significant challenges for these methods. Prompt tuning combined with the pre-trained Vision-Language models (VLMs) is an effective solution to the Few-shot Weakly Supervised WSI classification (FSWC) tasks. Nevertheless, applying prompt tuning methods designed for natural images to WSIs presents three significant challenges: 1) These methods fail to fully leverage the prior knowledge from the VLM's text modality; 2) They overlook the essential multi-scale and contextual information in WSIs, leading to suboptimal results; and 3) They lack exploration of instance aggregation methods. To address these problems, we propose a Multi-Scale and Context-focused Prompt Tuning (MSCPT) method for FSWC tasks. Specifically, MSCPT employs the frozen large language model to generate pathological visual language prior knowledge at multi-scale, guiding hierarchical prompt tuning. Additionally, we design a graph prompt tuning module to learn essential contextual information within WSI, and finally, a non-parametric cross-guided instance aggregation module has been introduced to get the WSI-level features. Based on two VLMs, extensive experiments and visualizations on three datasets demonstrated the powerful performance of our MSCPT.
XDT-CXR: Investigating Cross-Disease Transferability in Zero-Shot Binary Classification of Chest X-Rays
Authors: Umaima Rahman, Abhishek Basu, Muhammad Uzair Khattak, Aniq Ur Rahman
Date: 2024-08-21 00:00:00
Tasks: Binary Classification, Diagnostic, zero-shot-classification, Zero-Shot Learning

This study explores the concept of cross-disease transferability (XDT) in medical imaging, focusing on the potential of binary classifiers trained on one disease to perform zero-shot classification on another disease affecting the same organ. Utilizing chest X-rays (CXR) as the primary modality, we investigate whether a model trained on one pulmonary disease can make predictions about another novel pulmonary disease, a scenario with significant implications for medical settings with limited data on emerging diseases. The XDT framework leverages the embedding space of a vision encoder, which, through kernel transformation, aids in distinguishing between diseased and non-diseased classes in the latent space. This capability is especially beneficial in resource-limited environments or in regions with low prevalence of certain diseases, where conventional diagnostic practices may fail. However, the XDT framework is currently limited to binary classification, determining only the presence or absence of a disease rather than differentiating among multiple diseases. This limitation underscores the supplementary role of XDT to traditional diagnostic tests in clinical settings. Furthermore, results show that XDT-CXR as a framework is able to make better predictions compared to other zero-shot learning (ZSL) baselines.
VE-Bench: Subjective-Aligned Benchmark Suite for Text-Driven Video Editing Quality Assessment
Authors: Shangkun Sun, Xiaoyu Liang, Songlin Fan, Wenxu Gao, Wei Gao
Date: 2024-08-21 00:00:00
Tasks: Video Alignment, Video Editing, Video Quality Assessment, Visual Question Answering (VQA)

Text-driven video editing has recently experienced rapid development. Despite this, evaluating edited videos remains a considerable challenge. Current metrics tend to fail to align with human perceptions, and effective quantitative metrics for video editing are still notably absent. To address this, we introduce VE-Bench, a benchmark suite tailored to the assessment of text-driven video editing. This suite includes VE-Bench DB, a video quality assessment (VQA) database for video editing. VE-Bench DB encompasses a diverse set of source videos featuring various motions and subjects, along with multiple distinct editing prompts, editing results from 8 different models, and the corresponding Mean Opinion Scores (MOS) from 24 human annotators. Based on VE-Bench DB, we further propose VE-Bench QA, a quantitative human-aligned measurement for the text-driven video editing task. In addition to the aesthetic, distortion, and other visual quality indicators that traditional VQA methods emphasize, VE-Bench QA focuses on the text-video alignment and the relevance modeling between source and edited videos. It proposes a new assessment network for video editing that attains superior performance in alignment with human preferences. To the best of our knowledge, VE-Bench introduces the first quality assessment dataset for video editing and an effective subjective-aligned quantitative metric for this domain. All data and code will be publicly available at https://github.com/littlespray/VE-Bench.
OAPT: Offset-Aware Partition Transformer for Double JPEG Artifacts Removal
Authors: Qiao Mo, Yukang Ding, Jinhua Hao, Qiang Zhu, Ming Sun, Chao Zhou, Feiyu Chen, Shuyuan Zhu
Date: 2024-08-21 00:00:00
Tasks: Image Restoration

Deep learning-based methods have shown remarkable performance in single JPEG artifacts removal task. However, existing methods tend to degrade on double JPEG images, which are prevalent in real-world scenarios. To address this issue, we propose Offset-Aware Partition Transformer for double JPEG artifacts removal, termed as OAPT. We conduct an analysis of double JPEG compression that results in up to four patterns within each 8x8 block and design our model to cluster the similar patterns to remedy the difficulty of restoration. Our OAPT consists of two components: compression offset predictor and image reconstructor. Specifically, the predictor estimates pixel offsets between the first and second compression, which are then utilized to divide different patterns. The reconstructor is mainly based on several Hybrid Partition Attention Blocks (HPAB), combining vanilla window-based self-attention and sparse attention for clustered pattern features. Extensive experiments demonstrate that OAPT outperforms the state-of-the-art method by more than 0.16dB in double JPEG image restoration task. Moreover, without increasing any computation cost, the pattern clustering module in HPAB can serve as a plugin to enhance other transformer-based image restoration methods. The code will be available at https://github.com/QMoQ/OAPT.git .
LAKD-Activation Mapping Distillation Based on Local Learning
Authors: Yaoze Zhang, Yuming Zhang, Yu Zhao, Yue Zhang, Feiyu Zhu
Date: 2024-08-21 00:00:00
Tasks: Knowledge Distillation

Knowledge distillation is widely applied in various fundamental vision models to enhance the performance of compact models. Existing knowledge distillation methods focus on designing different distillation targets to acquire knowledge from teacher models. However, these methods often overlook the efficient utilization of distilled information, crudely coupling different types of information, making it difficult to explain how the knowledge from the teacher network aids the student network in learning. This paper proposes a novel knowledge distillation framework, Local Attention Knowledge Distillation (LAKD), which more efficiently utilizes the distilled information from teacher networks, achieving higher interpretability and competitive performance. The framework establishes an independent interactive training mechanism through a separation-decoupling mechanism and non-directional activation mapping. LAKD decouples the teacher's features and facilitates progressive interaction training from simple to complex. Specifically, the student network is divided into local modules with independent gradients to decouple the knowledge transferred from the teacher. The non-directional activation mapping helps the student network integrate knowledge from different local modules by learning coarse-grained feature knowledge. We conducted experiments on the CIFAR-10, CIFAR-100, and ImageNet datasets, and the results show that our LAKD method significantly outperforms existing methods, consistently achieving state-of-the-art performance across different datasets.
TrackGo: A Flexible and Efficient Method for Controllable Video Generation
Authors: Haitao Zhou, Chuang Wang, Rui Nie, Jinlin Liu, Dongdong Yu, Qian Yu, Changhu Wang
Date: 2024-08-21 00:00:00
Tasks: Video Generation

Recent years have seen substantial progress in diffusion-based controllable video generation. However, achieving precise control in complex scenarios, including fine-grained object parts, sophisticated motion trajectories, and coherent background movement, remains a challenge. In this paper, we introduce TrackGo, a novel approach that leverages free-form masks and arrows for conditional video generation. This method offers users with a flexible and precise mechanism for manipulating video content. We also propose the TrackAdapter for control implementation, an efficient and lightweight adapter designed to be seamlessly integrated into the temporal self-attention layers of a pretrained video generation model. This design leverages our observation that the attention map of these layers can accurately activate regions corresponding to motion in videos. Our experimental results demonstrate that our new approach, enhanced by the TrackAdapter, achieves state-of-the-art performance on key metrics such as FVD, FID, and ObjMC scores.
MeTTA: Single-View to 3D Textured Mesh Reconstruction with Test-Time Adaptation
Authors: Kim Yu-Ji, Hyunwoo Ha, Kim Youwang, Jaeheung Surh, Hyowon Ha, Tae-Hyun Oh
Date: 2024-08-21 00:00:00
Tasks: 3D geometry, 3D Reconstruction, Test-time Adaptation

Reconstructing 3D from a single view image is a long-standing challenge. One of the popular approaches to tackle this problem is learning-based methods, but dealing with the test cases unfamiliar with training data (Out-of-distribution; OoD) introduces an additional challenge. To adapt for unseen samples in test time, we propose MeTTA, a test-time adaptation (TTA) exploiting generative prior. We design joint optimization of 3D geometry, appearance, and pose to handle OoD cases with only a single view image. However, the alignment between the reference image and the 3D shape via the estimated viewpoint could be erroneous, which leads to ambiguity. To address this ambiguity, we carefully design learnable virtual cameras and their self-calibration. In our experiments, we demonstrate that MeTTA effectively deals with OoD scenarios at failure cases of existing learning-based 3D reconstruction models and enables obtaining a realistic appearance with physically based rendering (PBR) textures.
Low-Light Object Tracking: A Benchmark
Authors: Pengzhi Zhong, XIAOYU GUO, Defeng Huang, Xiaojun Peng, Yian Li, Qijun Zhao, Shuiwang Li
Date: 2024-08-21 00:00:00
Tasks: Object, Object Tracking, Visual Object Tracking, Visual Tracking

In recent years, the field of visual tracking has made significant progress with the application of large-scale training datasets. These datasets have supported the development of sophisticated algorithms, enhancing the accuracy and stability of visual object tracking. However, most research has primarily focused on favorable illumination circumstances, neglecting the challenges of tracking in low-ligh environments. In low-light scenes, lighting may change dramatically, targets may lack distinct texture features, and in some scenarios, targets may not be directly observable. These factors can lead to a severe decline in tracking performance. To address this issue, we introduce LLOT, a benchmark specifically designed for Low-Light Object Tracking. LLOT comprises 269 challenging sequences with a total of over 132K frames, each carefully annotated with bounding boxes. This specially designed dataset aims to promote innovation and advancement in object tracking techniques for low-light conditions, addressing challenges not adequately covered by existing benchmarks. To assess the performance of existing methods on LLOT, we conducted extensive tests on 39 state-of-the-art tracking algorithms. The results highlight a considerable gap in low-light tracking performance. In response, we propose H-DCPT, a novel tracker that incorporates historical and darkness clue prompts to set a stronger baseline. H-DCPT outperformed all 39 evaluated methods in our experiments, demonstrating significant improvements. We hope that our benchmark and H-DCPT will stimulate the development of novel and accurate methods for tracking objects in low-light conditions. The LLOT and code are available at https://github.com/OpenCodeGithub/H-DCPT.
GaussianOcc: Fully Self-supervised and Efficient 3D Occupancy Estimation with Gaussian Splatting
Authors: Wanshui Gan, Fang Liu, Hongbin Xu, Ningkai Mo, Naoto Yokoya
Date: 2024-08-21 00:00:00
Tasks: Representation Learning

We introduce GaussianOcc, a systematic method that investigates the two usages of Gaussian splatting for fully self-supervised and efficient 3D occupancy estimation in surround views. First, traditional methods for self-supervised 3D occupancy estimation still require ground truth 6D poses from sensors during training. To address this limitation, we propose Gaussian Splatting for Projection (GSP) module to provide accurate scale information for fully self-supervised training from adjacent view projection. Additionally, existing methods rely on volume rendering for final 3D voxel representation learning using 2D signals (depth maps, semantic maps), which is both time-consuming and less effective. We propose Gaussian Splatting from Voxel space (GSV) to leverage the fast rendering properties of Gaussian splatting. As a result, the proposed GaussianOcc method enables fully self-supervised (no ground truth pose) 3D occupancy estimation in competitive performance with low computational cost (2.7 times faster in training and 5 times faster in rendering). The relevant code is available in https://github.com/GANWANSHUI/GaussianOcc.git.
BAdd: Bias Mitigation through Bias Addition
Authors: Ioannis Sarridis, Christos Koutlis, Symeon Papadopoulos, Christos Diou
Date: 2024-08-21 00:00:00
Tasks: Attribute

Computer vision (CV) datasets often exhibit biases that are perpetuated by deep learning models. While recent efforts aim to mitigate these biases and foster fair representations, they fail in complex real-world scenarios. In particular, existing methods excel in controlled experiments involving benchmarks with single-attribute injected biases, but struggle with multi-attribute biases being present in well-established CV datasets. Here, we introduce BAdd, a simple yet effective method that allows for learning fair representations invariant to the attributes introducing bias by incorporating features representing these attributes into the backbone. BAdd is evaluated on seven benchmarks and exhibits competitive performance, surpassing state-of-the-art methods on both single- and multi-attribute benchmarks. Notably, BAdd achieves +27.5% and +5.5% absolute accuracy improvements on the challenging multi-attribute benchmarks, FB-Biased-MNIST and CelebA, respectively.
A Benchmark for AI-based Weather Data Assimilation
Authors: Wuxin Wang, Weicheng Ni, Tao Han, Taikang Yuan, Xiaoyong Li, Lei Bai, Boheng Duan, Kaijun Ren
Date: 2024-08-21 00:00:00
Tasks: Weather Forecasting

Recent advancements in Artificial Intelligence (AI) have led to the development of several Large Weather Models (LWMs) that rival State-Of-The-Art (SOTA) Numerical Weather Prediction (NWP) systems. Until now, these models have still relied on traditional NWP-generated analysis fields as input and are far from autonomous. Currently, scientists are increasingly focusing on developing data-driven data assimilation (DA) models for LWMs. To expedite advancements in this field and facilitate the operationalization of data-driven end-to-end weather forecasting systems, we propose DABench, a benchmark constructed by simulated observations, real-world observations, and ERA5 reanalysis. DABench contributes four standard features: (1) sparse and noisy observations provided for both simulated and real-world experiments; (2) a Skillful pre-trained Transformer-based weather prediction model, Sformer, designed to generate background fields while rigorously assessing the impact of assimilation outcomes on predictions; (3) standardized evaluation metrics for the model comparison; (4) a strong DA baseline, 4DVarFormerV2. Our experimental results demonstrate that the end-to-end weather forecasting system, integrating 4DVarFormerV2 and Sformer, can assimilate real-world observations, thereby facilitating a stable DA cycle lasting one year and achieving a skillful forecasting lead time of up to 7 days. The proposed DABench will significantly advance research in AI-based DA, AI-based weather forecasting, and related domains.
T2VIndexer: A Generative Video Indexer for Efficient Text-Video Retrieval
Authors: Yili Li, Jing Yu, Keke Gai, Bang Liu, Gang Xiong, Qi Wu
Date: 2024-08-21 00:00:00
Tasks: Retrieval, Video Retrieval

Current text-video retrieval methods mainly rely on cross-modal matching between queries and videos to calculate their similarity scores, which are then sorted to obtain retrieval results. This method considers the matching between each candidate video and the query, but it incurs a significant time cost and will increase notably with the increase of candidates. Generative models are common in natural language processing and computer vision, and have been successfully applied in document retrieval, but their application in multimodal retrieval remains unexplored. To enhance retrieval efficiency, in this paper, we introduce a model-based video indexer named T2VIndexer, which is a sequence-to-sequence generative model directly generating video identifiers and retrieving candidate videos with constant time complexity. T2VIndexer aims to reduce retrieval time while maintaining high accuracy. To achieve this goal, we propose video identifier encoding and query-identifier augmentation approaches to represent videos as short sequences while preserving their semantic information. Our method consistently enhances the retrieval efficiency of current state-of-the-art models on four standard datasets. It enables baselines with only 30\%-50\% of the original retrieval time to achieve better retrieval performance on MSR-VTT (+1.0%), MSVD (+1.8%), ActivityNet (+1.5%), and DiDeMo (+0.2%). The code is available at https://github.com/Lilidamowang/T2VIndexer-generativeSearch.
EMO-LLaMA: Enhancing Facial Emotion Understanding with Instruction Tuning
Authors: Bohao Xing, Zitong Yu, Xin Liu, Kaishen Yuan, Qilang Ye, Weicheng Xie, Huanjing Yue, Jingyu Yang, Heikki Kälviäinen
Date: 2024-08-21 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER)

Facial expression recognition (FER) is an important research topic in emotional artificial intelligence. In recent decades, researchers have made remarkable progress. However, current FER paradigms face challenges in generalization, lack semantic information aligned with natural language, and struggle to process both images and videos within a unified framework, making their application in multimodal emotion understanding and human-computer interaction difficult. Multimodal Large Language Models (MLLMs) have recently achieved success, offering advantages in addressing these issues and potentially overcoming the limitations of current FER paradigms. However, directly applying pre-trained MLLMs to FER still faces several challenges. Our zero-shot evaluations of existing open-source MLLMs on FER indicate a significant performance gap compared to GPT-4V and current supervised state-of-the-art (SOTA) methods. In this paper, we aim to enhance MLLMs' capabilities in understanding facial expressions. We first generate instruction data for five FER datasets with Gemini. We then propose a novel MLLM, named EMO-LLaMA, which incorporates facial priors from a pretrained facial analysis network to enhance human facial information. Specifically, we design a Face Info Mining module to extract both global and local facial information. Additionally, we utilize a handcrafted prompt to introduce age-gender-race attributes, considering the emotional differences across different human groups. Extensive experiments show that EMO-LLaMA achieves SOTA-comparable or competitive results across both static and dynamic FER datasets. The instruction dataset and code are available at https://github.com/xxtars/EMO-LLaMA.
Pano2Room: Novel View Synthesis from a Single Indoor Panorama
Authors: Guo Pu, Yiming Zhao, Zhouhui Lian
Date: 2024-08-21 00:00:00
Tasks: Novel View Synthesis

Recent single-view 3D generative methods have made significant advancements by leveraging knowledge distilled from extensive 3D object datasets. However, challenges persist in the synthesis of 3D scenes from a single view, primarily due to the complexity of real-world environments and the limited availability of high-quality prior resources. In this paper, we introduce a novel approach called Pano2Room, designed to automatically reconstruct high-quality 3D indoor scenes from a single panoramic image. These panoramic images can be easily generated using a panoramic RGBD inpainter from captures at a single location with any camera. The key idea is to initially construct a preliminary mesh from the input panorama, and iteratively refine this mesh using a panoramic RGBD inpainter while collecting photo-realistic 3D-consistent pseudo novel views. Finally, the refined mesh is converted into a 3D Gaussian Splatting field and trained with the collected pseudo novel views. This pipeline enables the reconstruction of real-world 3D scenes, even in the presence of large occlusions, and facilitates the synthesis of photo-realistic novel views with detailed geometry. Extensive qualitative and quantitative experiments have been conducted to validate the superiority of our method in single-panorama indoor novel synthesis compared to the state-of-the-art. Our code and data are available at \url{https://github.com/TrickyGo/Pano2Room}.