pwc-archive/papers-with-abstracts huggingface dataset:
DriveMLLM: A Benchmark for Spatial Understanding with Multimodal Large Language Models in Autonomous Driving
Authors: Xianda Guo, Ruijun Zhang, Yiqun Duan, Yuhang He, Chenming Zhang, Shuai Liu, Long Chen
Date: 2024-11-20 00:00:00
Tasks: Autonomous Driving, motion prediction, Spatial Reasoning

Autonomous driving requires a comprehensive understanding of 3D environments to facilitate high-level tasks such as motion prediction, planning, and mapping. In this paper, we introduce DriveMLLM, a benchmark specifically designed to evaluate the spatial understanding capabilities of multimodal large language models (MLLMs) in autonomous driving. DriveMLLM includes 880 front-facing camera images and introduces both absolute and relative spatial reasoning tasks, accompanied by linguistically diverse natural language questions. To measure MLLMs' performance, we propose novel evaluation metrics focusing on spatial understanding. We evaluate several state-of-the-art MLLMs on DriveMLLM, and our results reveal the limitations of current models in understanding complex spatial relationships in driving contexts. We believe these findings underscore the need for more advanced MLLM-based spatial reasoning methods and highlight the potential for DriveMLLM to drive further research in autonomous driving. Code will be available at \url{https://github.com/XiandaGuo/Drive-MLLM}.
Demonstrating the Suitability of Neuromorphic, Event-Based, Dynamic Vision Sensors for In Process Monitoring of Metallic Additive Manufacturing and Welding
Authors: David Mascareñas, Andre Green, Ashlee Liao, Michael Torrez, Alessandro Cattaneo, Amber Black, John Bernardin, Garrett Kenyon
Date: 2024-11-20 00:00:00
Tasks: 3D geometry, Anomaly Detection

We demonstrate the suitability of high dynamic range, high-speed, neuromorphic event-based, dynamic vision sensors for metallic additive manufacturing and welding for in-process monitoring applications. In-process monitoring to enable quality control of mission critical components produced using metallic additive manufacturing is of high interest. However, the extreme light environment and high speed dynamics of metallic melt pools have made this a difficult environment in which to make measurements. Event-based sensing is an alternative measurement paradigm where data is only transmitted/recorded when a measured quantity exceeds a threshold resolution. The result is that event-based sensors consume less power and less memory/bandwidth, and they operate across a wide range of timescales and dynamic ranges. Event-driven driven imagers stand out from conventional imager technology in that they have a very high dynamic range of approximately 120 dB. Conventional 8 bit imagers only have a dynamic range of about 48 dB. This high dynamic range makes them a good candidate for monitoring manufacturing processes that feature high intensity light sources/generation such as metallic additive manufacturing and welding. In addition event based imagers are able to capture data at timescales on the order of 100 {\mu}s, which makes them attractive to capturing fast dynamics in a metallic melt pool. In this work we demonstrate that event-driven imagers have been shown to be able to observe tungsten inert gas (TIG) and laser welding melt pools. The results of this effort suggest that with additional engineering effort, neuromorphic event imagers should be capable of 3D geometry measurements of the melt pool, and anomaly detection/classification/prediction.
Superpixel Cost Volume Excitation for Stereo Matching
Authors: Shanglong Liu, Lin Qi, Junyu Dong, Wenxiang Gu, Liyi Xu
Date: 2024-11-20 00:00:00
Tasks: Stereo Matching, Superpixels

In this work, we concentrate on exciting the intrinsic local consistency of stereo matching through the incorporation of superpixel soft constraints, with the objective of mitigating inaccuracies at the boundaries of predicted disparity maps. Our approach capitalizes on the observation that neighboring pixels are predisposed to belong to the same object and exhibit closely similar intensities within the probability volume of superpixels. By incorporating this insight, our method encourages the network to generate consistent probability distributions of disparity within each superpixel, aiming to improve the overall accuracy and coherence of predicted disparity maps. Experimental evalua tions on widely-used datasets validate the efficacy of our proposed approach, demonstrating its ability to assist cost volume-based matching networks in restoring competitive performance.
ESARM: 3D Emotional Speech-to-Animation via Reward Model from Automatically-Ranked Demonstrations
Authors: xulong Zhang, Xiaoyang Qu, Haoxiang Shi, Chunguang Xiao, Jianzong Wang
Date: 2024-11-20 00:00:00

This paper proposes a novel 3D speech-to-animation (STA) generation framework designed to address the shortcomings of existing models in producing diverse and emotionally resonant animations. Current STA models often generate animations that lack emotional depth and variety, failing to align with human expectations. To overcome these limitations, we introduce a novel STA model coupled with a reward model. This combination enables the decoupling of emotion and content under audio conditions through a cross-coupling training approach. Additionally, we develop a training methodology that leverages automatic quality evaluation of generated facial animations to guide the reinforcement learning process. This methodology encourages the STA model to explore a broader range of possibilities, resulting in the generation of diverse and emotionally expressive facial animations of superior quality. We conduct extensive empirical experiments on a benchmark dataset, and the results validate the effectiveness of our proposed framework in generating high-quality, emotionally rich 3D animations that are better aligned with human preferences.
Practical Compact Deep Compressed Sensing
Authors: Bin Chen, Jian Zhang
Date: 2024-11-20 00:00:00
Tasks: compressed sensing

Recent years have witnessed the success of deep networks in compressed sensing (CS), which allows for a significant reduction in sampling cost and has gained growing attention since its inception. In this paper, we propose a new practical and compact network dubbed PCNet for general image CS. Specifically, in PCNet, a novel collaborative sampling operator is designed, which consists of a deep conditional filtering step and a dual-branch fast sampling step. The former learns an implicit representation of a linear transformation matrix into a few convolutions and first performs adaptive local filtering on the input image, while the latter then uses a discrete cosine transform and a scrambled block-diagonal Gaussian matrix to generate under-sampled measurements. Our PCNet is equipped with an enhanced proximal gradient descent algorithm-unrolled network for reconstruction. It offers flexibility, interpretability, and strong recovery performance for arbitrary sampling rates once trained. Additionally, we provide a deployment-oriented extraction scheme for single-pixel CS imaging systems, which allows for the convenient conversion of any linear sampling operator to its matrix form to be loaded onto hardware like digital micro-mirror devices. Extensive experiments on natural image CS, quantized CS, and self-supervised CS demonstrate the superior reconstruction accuracy and generalization ability of PCNet compared to existing state-of-the-art methods, particularly for high-resolution images. Code is available at https://github.com/Guaishou74851/PCNet.
Hints of Prompt: Enhancing Visual Representation for Multimodal LLMs in Autonomous Driving
Authors: Hao Zhou, Zhanning Gao, Maosheng Ye, Zhili Chen, Qifeng Chen, Tongyi Cao, Honggang Qi
Date: 2024-11-20 00:00:00
Tasks: Autonomous Driving, Multimodal Reasoning, Visual Question Answering (VQA)

In light of the dynamic nature of autonomous driving environments and stringent safety requirements, general MLLMs combined with CLIP alone often struggle to represent driving-specific scenarios accurately, particularly in complex interactions and long-tail cases. To address this, we propose the Hints of Prompt (HoP) framework, which introduces three key enhancements: Affinity hint to emphasize instance-level structure by strengthening token-wise connections, Semantic hint to incorporate high-level information relevant to driving-specific cases, such as complex interactions among vehicles and traffic signs, and Question hint to align visual features with the query context, focusing on question-relevant regions. These hints are fused through a Hint Fusion module, enriching visual representations and enhancing multimodal reasoning for autonomous driving VQA tasks. Extensive experiments confirm the effectiveness of the HoP framework, showing it significantly outperforms previous state-of-the-art methods across all key metrics.
Improving OOD Generalization of Pre-trained Encoders via Aligned Embedding-Space Ensembles
Authors: Shuman Peng, Arash Khoeini, Sharan Vaswani, Martin Ester
Date: 2024-11-20 00:00:00

The quality of self-supervised pre-trained embeddings on out-of-distribution (OOD) data is poor without fine-tuning. A straightforward and simple approach to improving the generalization of pre-trained representation to OOD data is the use of deep ensembles. However, obtaining an effective ensemble in the embedding space with only unlabeled data remains an unsolved problem. We first perform a theoretical analysis that reveals the relationship between individual hyperspherical embedding spaces in an ensemble. We then design a principled method to align these embedding spaces in an unsupervised manner. Experimental results on the MNIST dataset show that our embedding-space ensemble method improves pre-trained embedding quality on in-distribution and OOD data compared to single encoders.
Automatic marker-free registration based on similar tetrahedras for single-tree point clouds
Authors: Jing Ren, Pei Wang, Hanlong Li, Yuhan Wu, Yuhang Gao, Wenxin Chen, Mingtai Zhang, Lingyun Zhang
Date: 2024-11-20 00:00:00
Tasks: Point Cloud Registration

In recent years, terrestrial laser scanning technology has been widely used to collect tree point cloud data, aiding in measurements of diameter at breast height, biomass, and other forestry survey data. Since a single scan from terrestrial laser systems captures data from only one angle, multiple scans must be registered and fused to obtain complete tree point cloud data. This paper proposes a marker-free automatic registration method for single-tree point clouds based on similar tetrahedras. First, two point clouds from two scans of the same tree are used to generate tree skeletons, and key point sets are constructed from these skeletons. Tetrahedra are then filtered and matched according to similarity principles, with the vertices of these two matched tetrahedras selected as matching point pairs, thus completing the coarse registration of the point clouds from the two scans. Subsequently, the ICP method is applied to the coarse-registered leaf point clouds to obtain fine registration parameters, completing the precise registration of the two tree point clouds. Experiments were conducted using terrestrial laser scanning data from eight trees, each from different species and with varying shapes. The proposed method was evaluated using RMSE and Hausdorff distance, compared against the traditional ICP and NDT methods. The experimental results demonstrate that the proposed method significantly outperforms both ICP and NDT in registration accuracy, achieving speeds up to 593 times and 113 times faster than ICP and NDT, respectively. In summary, the proposed method shows good robustness in single-tree point cloud registration, with significant advantages in accuracy and speed compared to traditional ICP and NDT methods, indicating excellent application prospects in practical registration scenarios.
Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation
Authors: Rohith Peddi, Saurabh, Ayush Abhay Shrivastava, Parag Singla, Vibhav Gogate
Date: 2024-11-20 00:00:00
Proceeding: CVPR 2025 1
Tasks: Graph Generation, Scene Graph Generation, Spatio-temporal Scene Graphs, Video scene graph generation

Spatio-Temporal Scene Graphs (STSGs) provide a concise and expressive representation of dynamic scenes by modelling objects and their evolving relationships over time. However, real-world visual relationships often exhibit a long-tailed distribution, causing existing methods for tasks like Video Scene Graph Generation (VidSGG) and Scene Graph Anticipation (SGA) to produce biased scene graphs. To this end, we propose ImparTail, a novel training framework that leverages curriculum learning and loss masking to mitigate bias in the generation and anticipation of spatio-temporal scene graphs. Our approach gradually decreases the dominance of the head relationship classes during training and focuses more on tail classes, leading to more balanced training. Furthermore, we introduce two new tasks, Robust Spatio-Temporal Scene Graph Generation and Robust Scene Graph Anticipation, designed to evaluate the robustness of STSG models against distribution shifts. Extensive experiments on the Action Genome dataset demonstrate that our framework significantly enhances the unbiased performance and robustness of STSG models compared to existing methods.
Efficient Masked AutoEncoder for Video Object Counting and A Large-Scale Benchmark
Authors: Bing Cao, Quanhao Lu, Jiekang Feng, Pengfei Zhu, QinGhua Hu, Qilong Wang
Date: 2024-11-20 00:00:00
Tasks: Object Counting, Optical Flow Estimation, Representation Learning

The dynamic imbalance of the fore-background is a major challenge in video object counting, which is usually caused by the sparsity of foreground objects. This often leads to severe under- and over-prediction problems and has been less studied in existing works. To tackle this issue in video object counting, we propose a density-embedded Efficient Masked Autoencoder Counting (E-MAC) framework in this paper. To effectively capture the dynamic variations across frames, we utilize an optical flow-based temporal collaborative fusion that aligns features to derive multi-frame density residuals. The counting accuracy of the current frame is boosted by harnessing the information from adjacent frames. More importantly, to empower the representation ability of dynamic foreground objects for intra-frame, we first take the density map as an auxiliary modality to perform $\mathtt{D}$ensity-$\mathtt{E}$mbedded $\mathtt{M}$asked m$\mathtt{O}$deling ($\mathtt{DEMO}$) for multimodal self-representation learning to regress density map. However, as $\mathtt{DEMO}$ contributes effective cross-modal regression guidance, it also brings in redundant background information and hard to focus on foreground regions. To handle this dilemma, we further propose an efficient spatial adaptive masking derived from density maps to boost efficiency. In addition, considering most existing datasets are limited to human-centric scenarios, we first propose a large video bird counting dataset $\textit{DroneBird}$, in natural scenarios for migratory bird protection. Extensive experiments on three crowd datasets and our $\textit{DroneBird}$ validate our superiority against the counterparts.
Bounding-box Watermarking: Defense against Model Extraction Attacks on Object Detectors
Authors: Satoru Koda, Ikuya Morikawa
Date: 2024-11-20 00:00:00
Tasks: Model extraction, object-detection, Object Detection

Deep neural networks (DNNs) deployed in a cloud often allow users to query models via the APIs. However, these APIs expose the models to model extraction attacks (MEAs). In this attack, the attacker attempts to duplicate the target model by abusing the responses from the API. Backdoor-based DNN watermarking is known as a promising defense against MEAs, wherein the defender injects a backdoor into extracted models via API responses. The backdoor is used as a watermark of the model; if a suspicious model has the watermark (i.e., backdoor), it is verified as an extracted model. This work focuses on object detection (OD) models. Existing backdoor attacks on OD models are not applicable for model watermarking as the defense against MEAs on a realistic threat model. Our proposed approach involves inserting a backdoor into extracted models via APIs by stealthily modifying the bounding-boxes (BBs) of objects detected in queries while keeping the OD capability. In our experiments on three OD datasets, the proposed approach succeeded in identifying the extracted models with 100% accuracy in a wide variety of experimental scenarios.
Attentive Contextual Attention for Cloud Removal
Authors: Wenli Huang, Ye Deng, Yang Wu, Jinjun Wang
Date: 2024-11-20 00:00:00
Tasks: Cloud Removal, Earth Observation, Image Reconstruction

Cloud cover can significantly hinder the use of remote sensing images for Earth observation, prompting urgent advancements in cloud removal technology. Recently, deep learning strategies have shown strong potential in restoring cloud-obscured areas. These methods utilize convolution to extract intricate local features and attention mechanisms to gather long-range information, improving the overall comprehension of the scene. However, a common drawback of these approaches is that the resulting images often suffer from blurriness, artifacts, and inconsistencies. This is partly because attention mechanisms apply weights to all features based on generalized similarity scores, which can inadvertently introduce noise and irrelevant details from cloud-covered areas. To overcome this limitation and better capture relevant distant context, we introduce a novel approach named Attentive Contextual Attention (AC-Attention). This method enhances conventional attention mechanisms by dynamically learning data-driven attentive selection scores, enabling it to filter out noise and irrelevant features effectively. By integrating the AC-Attention module into the DSen2-CR cloud removal framework, we significantly improve the model's ability to capture essential distant information, leading to more effective cloud removal. Our extensive evaluation of various datasets shows that our method outperforms existing ones regarding image reconstruction quality. Additionally, we conducted ablation studies by integrating AC-Attention into multiple existing methods and widely used network architectures. These studies demonstrate the effectiveness and adaptability of AC-Attention and reveal its ability to focus on relevant features, thereby improving the overall performance of the networks. The code is available at \url{https://github.com/huangwenwenlili/ACA-CRNet}.
RobustFormer: Noise-Robust Pre-training for images and videos
Authors: Ashish Bastola, Nishant Luitel, Hao Wang, Danda Pani Paudel, Roshani Poudel, Abolfazl Razi
Date: 2024-11-20 00:00:00

While deep learning models are powerful tools that revolutionized many areas, they are also vulnerable to noise as they rely heavily on learning patterns and features from the exact details of the clean data. Transformers, which have become the backbone of modern vision models, are no exception. Current Discrete Wavelet Transforms (DWT) based methods do not benefit from masked autoencoder (MAE) pre-training since the inverse DWT (iDWT) introduced in these approaches is computationally inefficient and lacks compatibility with video inputs in transformer architectures. In this work, we present RobustFormer, a method that overcomes these limitations by enabling noise-robust pre-training for both images and videos; improving the efficiency of DWT-based methods by removing the need for computationally iDWT steps and simplifying the attention mechanism. To our knowledge, the proposed method is the first DWT-based method compatible with video inputs and masked pre-training. Our experiments show that MAE-based pre-training allows us to bypass the iDWT step, greatly reducing computation. Through extensive tests on benchmark datasets, RobustFormer achieves state-of-the-art results for both image and video tasks.
LMM-driven Semantic Image-Text Coding for Ultra Low-bitrate Learned Image Compression
Authors: Shimon Murai, Heming Sun, Jiro Katto
Date: 2024-11-20 00:00:00
Tasks: Image Captioning, Image Compression

Supported by powerful generative models, low-bitrate learned image compression (LIC) models utilizing perceptual metrics have become feasible. Some of the most advanced models achieve high compression rates and superior perceptual quality by using image captions as sub-information. This paper demonstrates that using a large multi-modal model (LMM), it is possible to generate captions and compress them within a single model. We also propose a novel semantic-perceptual-oriented fine-tuning method applicable to any LIC network, resulting in a 41.58\% improvement in LPIPS BD-rate compared to existing methods. Our implementation and pre-trained weights are available at https://github.com/tokkiwa/ImageTextCoding.
X as Supervision: Contending with Depth Ambiguity in Unsupervised Monocular 3D Pose Estimation
Authors: Yuchen Yang, Xuanyi Liu, Xing Gao, Zhihang Zhong, Xiao Sun
Date: 2024-11-20 00:00:00
Tasks: 3D Pose Estimation, Pose Estimation

Recent unsupervised methods for monocular 3D pose estimation have endeavored to reduce dependence on limited annotated 3D data, but most are solely formulated in 2D space, overlooking the inherent depth ambiguity issue. Due to the information loss in 3D-to-2D projection, multiple potential depths may exist, yet only some of them are plausible in human structure. To tackle depth ambiguity, we propose a novel unsupervised framework featuring a multi-hypothesis detector and multiple tailored pretext tasks. The detector extracts multiple hypotheses from a heatmap within a local window, effectively managing the multi-solution problem. Furthermore, the pretext tasks harness 3D human priors from the SMPL model to regularize the solution space of pose estimation, aligning it with the empirical distribution of 3D human structures. This regularization is partially achieved through a GCN-based discriminator within the discriminative learning, and is further complemented with synthetic images through rendering, ensuring plausible estimations. Consequently, our approach demonstrates state-of-the-art unsupervised 3D pose estimation performance on various human datasets. Further evaluations on data scale-up and one animal dataset highlight its generalization capabilities. Code will be available at https://github.com/Charrrrrlie/X-as-Supervision.
ORID: Organ-Regional Information Driven Framework for Radiology Report Generation
Authors: Tiancheng Gu, Kaicheng Yang, Xiang An, Ziyong Feng, Dongnan Liu, Weidong Cai
Date: 2024-11-20 00:00:00
Tasks: Decoder, Graph Neural Network

The objective of Radiology Report Generation (RRG) is to automatically generate coherent textual analyses of diseases based on radiological images, thereby alleviating the workload of radiologists. Current AI-based methods for RRG primarily focus on modifications to the encoder-decoder model architecture. To advance these approaches, this paper introduces an Organ-Regional Information Driven (ORID) framework which can effectively integrate multi-modal information and reduce the influence of noise from unrelated organs. Specifically, based on the LLaVA-Med, we first construct an RRG-related instruction dataset to improve organ-regional diagnosis description ability and get the LLaVA-Med-RRG. After that, we propose an organ-based cross-modal fusion module to effectively combine the information from the organ-regional diagnosis description and radiology image. To further reduce the influence of noise from unrelated organs on the radiology report generation, we introduce an organ importance coefficient analysis module, which leverages Graph Neural Network (GNN) to examine the interconnections of the cross-modal information of each organ region. Extensive experiments an1d comparisons with state-of-the-art methods across various evaluation metrics demonstrate the superior performance of our proposed method.