pwc-archive/papers-with-abstracts huggingface dataset:
Online Knowledge Integration for 3D Semantic Mapping: A Survey
Authors: Felix Igelbrink, Marian Renz, Martin Günther, Piper Powell, Lennart Niecksch, Oscar Lima, Martin Atzmueller, Joachim Hertzberg
Date: 2024-11-27 00:00:00
Tasks: Common Sense Reasoning, Knowledge Graphs, Survey

Semantic mapping is a key component of robots operating in and interacting with objects in structured environments. Traditionally, geometric and knowledge representations within a semantic map have only been loosely integrated. However, recent advances in deep learning now allow full integration of prior knowledge, represented as knowledge graphs or language concepts, into sensor data processing and semantic mapping pipelines. Semantic scene graphs and language models enable modern semantic mapping approaches to incorporate graph-based prior knowledge or to leverage the rich information in human language both during and after the mapping process. This has sparked substantial advances in semantic mapping, leading to previously impossible novel applications. This survey reviews these recent developments comprehensively, with a focus on online integration of knowledge into semantic mapping. We specifically focus on methods using semantic scene graphs for integrating symbolic prior knowledge and language models for respective capture of implicit common-sense knowledge and natural language concepts
CHOICE: Benchmarking the Remote Sensing Capabilities of Large Vision-Language Models
Authors: Xiao An, Jiaxing Sun, Zihan Gui, wei he
Date: 2024-11-27 00:00:00
Tasks: Benchmarking, Earth Observation, Multiple-choice

The rapid advancement of Large Vision-Language Models (VLMs), both general-domain models and those specifically tailored for remote sensing, has demonstrated exceptional perception and reasoning capabilities in Earth observation tasks. However, a benchmark for systematically evaluating their capabilities in this domain is still lacking. To bridge this gap, we propose CHOICE, an extensive benchmark designed to objectively evaluate the hierarchical remote sensing capabilities of VLMs. Focusing on 2 primary capability dimensions essential to remote sensing: perception and reasoning, we further categorize 6 secondary dimensions and 23 leaf tasks to ensure a well-rounded assessment coverage. CHOICE guarantees the quality of all 10,507 problems through a rigorous process of data collection from 50 globally distributed cities, question construction and quality control. The newly curated data and the format of multiple-choice questions with definitive answers allow for an objective and straightforward performance assessment. Our evaluation of 3 proprietary and 21 open-source VLMs highlights their critical limitations within this specialized context. We hope that CHOICE will serve as a valuable resource and offer deeper insights into the challenges and potential of VLMs in the field of remote sensing. We will release CHOICE at https://github.com/ShawnAn-WHU/CHOICE.
Enhancing Visual Reasoning with Autonomous Imagination in Multimodal Large Language Models
Authors: Jingming Liu, Yumeng Li, Boyuan Xiao, Yichang Jian, Ziang Qin, Tianjia Shao, Yao-Xiang Ding, Kun Zhou
Date: 2024-11-27 00:00:00
Tasks: Visual Reasoning

There have been recent efforts to extend the Chain-of-Thought (CoT) paradigm to Multimodal Large Language Models (MLLMs) by finding visual clues in the input scene, advancing the visual reasoning ability of MLLMs. However, current approaches are specially designed for the tasks where clue finding plays a major role in the whole reasoning process, leading to the difficulty in handling complex visual scenes where clue finding does not actually simplify the whole reasoning task. To deal with this challenge, we propose a new visual reasoning paradigm enabling MLLMs to autonomously modify the input scene to new ones based on its reasoning status, such that CoT is reformulated as conducting simple closed-loop decision-making and reasoning steps under a sequence of imagined visual scenes, leading to natural and general CoT construction. To implement this paradigm, we introduce a novel plug-and-play imagination space, where MLLMs conduct visual modifications through operations like focus, ignore, and transform based on their native reasoning ability without specific training. We validate our approach through a benchmark spanning dense counting, simple jigsaw puzzle solving, and object placement, challenging the reasoning ability beyond clue finding. The results verify that while existing techniques fall short, our approach enables MLLMs to effectively reason step by step through autonomous imagination. Project page: https://future-item.github.io/autoimagine-site.
ModeDreamer: Mode Guiding Score Distillation for Text-to-3D Generation using Reference Image Prompts
Authors: Uy Dieu Tran, Minh Luu, Phong Ha Nguyen, Khoi Nguyen, Binh-Son Hua
Date: 2024-11-27 00:00:00
Tasks: 3D Generation, Text to 3D

Existing Score Distillation Sampling (SDS)-based methods have driven significant progress in text-to-3D generation. However, 3D models produced by SDS-based methods tend to exhibit over-smoothing and low-quality outputs. These issues arise from the mode-seeking behavior of current methods, where the scores used to update the model oscillate between multiple modes, resulting in unstable optimization and diminished output quality. To address this problem, we introduce a novel image prompt score distillation loss named ISD, which employs a reference image to direct text-to-3D optimization toward a specific mode. Our ISD loss can be implemented by using IP-Adapter, a lightweight adapter for integrating image prompt capability to a text-to-image diffusion model, as a mode-selection module. A variant of this adapter, when not being prompted by a reference image, can serve as an efficient control variate to reduce variance in score estimates, thereby enhancing both output quality and optimization stability. Our experiments demonstrate that the ISD loss consistently achieves visually coherent, high-quality outputs and improves optimization speed compared to prior text-to-3D methods, as demonstrated through both qualitative and quantitative evaluations on the T3Bench benchmark suite.
Towards Cross-device and Training-free Robotic Grasping in 3D Open World
Authors: Weiguang Zhao, Chenru Jiang, Chengrui Zhang, Jie Sun, Yuyao Yan, Rui Zhang, Kaizhu Huang
Date: 2024-11-27 00:00:00
Tasks: Point Cloud Segmentation, Robotic Grasping, Segmentation

Robotic grasping in the open world is a critical component of manufacturing and automation processes. While numerous existing approaches depend on 2D segmentation output to facilitate the grasping procedure, accurately determining depth from 2D imagery remains a challenge, often leading to limited performance in complex stacking scenarios. In contrast, techniques utilizing 3D point cloud data inherently capture depth information, thus enabling adeptly navigating and manipulating a diverse range of complex stacking scenes. However, such efforts are considerably hindered by the variance in data capture devices and the unstructured nature of the data, which limits their generalizability. Consequently, much research is narrowly concentrated on managing designated objects within specific settings, which confines their real-world applicability. This paper presents a novel pipeline capable of executing object grasping tasks in open-world scenarios even on previously unseen objects without the necessity for training. Additionally, our pipeline supports the flexible use of different 3D point cloud segmentation models across a variety of scenes. Leveraging the segmentation results, we propose to engage a training-free binary clustering algorithm that not only improves segmentation precision but also possesses the capability to cluster and localize unseen objects for executing grasping operations. In our experiments, we investigate a range of open-world scenarios, and the outcomes underscore the remarkable robustness and generalizability of our pipeline, consistent across various environments, robots, cameras, and objects. The code will be made available upon acceptance of the paper.
Spectral-Spatial Transformer with Active Transfer Learning for Hyperspectral Image Classification
Authors: Muhammad Ahmad, Manuel Mazzara, Salvatore Distefano
Date: 2024-11-27 00:00:00
Tasks: Active Learning, Classification Of Hyperspectral Images, Diversity, Hyperspectral Image Classification, image-classification, Image Classification, Transfer Learning

The classification of hyperspectral images (HSI) is a challenging task due to the high spectral dimensionality and limited labeled data typically available for training. In this study, we propose a novel multi-stage active transfer learning (ATL) framework that integrates a Spatial-Spectral Transformer (SST) with an active learning process for efficient HSI classification. Our approach leverages a pre-trained (initially trained) SST model, fine-tuned iteratively on newly acquired labeled samples using an uncertainty-diversity (Spatial-Spectral Neighborhood Diversity) querying mechanism. This mechanism identifies the most informative and diverse samples, thereby optimizing the transfer learning process to reduce both labeling costs and model uncertainty. We further introduce a dynamic freezing strategy, selectively freezing layers of the SST model to minimize computational overhead while maintaining adaptability to spectral variations in new data. One of the key innovations in our work is the self-calibration of spectral and spatial attention weights, achieved through uncertainty-guided active learning. This not only enhances the model's robustness in handling dynamic and disjoint spectral profiles but also improves generalization across multiple HSI datasets. Additionally, we present a diversity-promoting sampling strategy that ensures the selected samples span distinct spectral regions, preventing overfitting to particular spectral classes. Experiments on benchmark HSI datasets demonstrate that the SST-ATL framework significantly outperforms existing CNN and SST-based methods, offering superior accuracy, efficiency, and computational performance. The source code can be accessed at \url{https://github.com/mahmad000/ATL-SST}.
When Large Vision-Language Models Meet Person Re-Identification
Authors: Qizao Wang, Bin Li, xiangyang xue
Date: 2024-11-27 00:00:00
Tasks: Person Re-Identification

Large Vision-Language Models (LVLMs) that incorporate visual models and Large Language Models (LLMs) have achieved impressive results across various cross-modal understanding and reasoning tasks. In recent years, person re-identification (ReID) has also started to explore cross-modal semantics to improve the accuracy of identity recognition. However, effectively utilizing LVLMs for ReID remains an open challenge. While LVLMs operate under a generative paradigm by predicting the next output word, ReID requires the extraction of discriminative identity features to match pedestrians across cameras. In this paper, we propose LVLM-ReID, a novel framework that harnesses the strengths of LVLMs to promote ReID. Specifically, we employ instructions to guide the LVLM in generating one pedestrian semantic token that encapsulates key appearance semantics from the person image. This token is further refined through our Semantic-Guided Interaction (SGI) module, establishing a reciprocal interaction between the semantic token and visual tokens. Ultimately, the reinforced semantic token serves as the pedestrian identity representation. Our framework integrates the semantic understanding and generation capabilities of LVLMs into end-to-end ReID training, allowing LVLMs to capture rich semantic cues from pedestrian images during both training and inference. Our method achieves competitive results on multiple benchmarks without additional image-text annotations, demonstrating the potential of LVLM-generated semantics to advance person ReID and offering a promising direction for future research.
Training Data Synthesis with Difficulty Controlled Diffusion Model
Authors: Zerun Wang, Jiafeng Mao, Xueting Wang, Toshihiko Yamasaki
Date: 2024-11-27 00:00:00
Tasks: model

Semi-supervised learning (SSL) can improve model performance by leveraging unlabeled images, which can be collected from public image sources with low costs. In recent years, synthetic images have become increasingly common in public image sources due to rapid advances in generative models. Therefore, it is becoming inevitable to include existing synthetic images in the unlabeled data for SSL. How this kind of contamination will affect SSL remains unexplored. In this paper, we introduce a new task, Real-Synthetic Hybrid SSL (RS-SSL), to investigate the impact of unlabeled data contaminated by synthetic images for SSL. First, we set up a new RS-SSL benchmark to evaluate current SSL methods and found they struggled to improve by unlabeled synthetic images, sometimes even negatively affected. To this end, we propose RSMatch, a novel SSL method specifically designed to handle the challenges of RS-SSL. RSMatch effectively identifies unlabeled synthetic data and further utilizes them for improvement. Extensive experimental results show that RSMatch can transfer synthetic unlabeled data from `obstacles' to `resources.' The effectiveness is further verified through ablation studies and visualization.
Aligning Knowledge Concepts to Whole Slide Images for Precise Histopathology Image Analysis
Authors: Weiqin Zhao, Ziyu Guo, Yinshuang Fan, Yuming Jiang, Maximus Yeung, Lequan Yu
Date: 2024-11-27 00:00:00
Tasks: Language Modeling, Language Modelling, Multiple Instance Learning, whole slide images

Due to the large size and lack of fine-grained annotation, Whole Slide Images (WSIs) analysis is commonly approached as a Multiple Instance Learning (MIL) problem. However, previous studies only learn from training data, posing a stark contrast to how human clinicians teach each other and reason about histopathologic entities and factors. Here we present a novel knowledge concept-based MIL framework, named ConcepPath to fill this gap. Specifically, ConcepPath utilizes GPT-4 to induce reliable diseasespecific human expert concepts from medical literature, and incorporate them with a group of purely learnable concepts to extract complementary knowledge from training data. In ConcepPath, WSIs are aligned to these linguistic knowledge concepts by utilizing pathology vision-language model as the basic building component. In the application of lung cancer subtyping, breast cancer HER2 scoring, and gastric cancer immunotherapy-sensitive subtyping task, ConcepPath significantly outperformed previous SOTA methods which lack the guidance of human expert knowledge.
Training Noise Token Pruning
Authors: Mingxing Rao, Bohan Jiang, Daniel Moyer
Date: 2024-11-27 00:00:00

In the present work we present Training Noise Token (TNT) Pruning for vision transformers. Our method relaxes the discrete token dropping condition to continuous additive noise, providing smooth optimization in training, while retaining discrete dropping computational gains in deployment settings. We provide theoretical connections to Rate-Distortion literature, and empirical evaluations on the ImageNet dataset using ViT and DeiT architectures demonstrating TNT's advantages over previous pruning methods.
Dual-view X-ray Detection: Can AI Detect Prohibited Items from Dual-view X-ray Images like Humans?
Authors: Renshuai Tao, Haoyu Wang, Yuzhe Guo, Hairong Chen, Li Zhang, Xianglong Liu, Yunchao Wei, Yao Zhao
Date: 2024-11-27 00:00:00
Proceeding: CVPR 2025 1

To detect prohibited items in challenging categories, human inspectors typically rely on images from two distinct views (vertical and side). Can AI detect prohibited items from dual-view X-ray images in the same way humans do? Existing X-ray datasets often suffer from limitations, such as single-view imaging or insufficient sample diversity. To address these gaps, we introduce the Large-scale Dual-view X-ray (LDXray), which consists of 353,646 instances across 12 categories, providing a diverse and comprehensive resource for training and evaluating models. To emulate human intelligence in dual-view detection, we propose the Auxiliary-view Enhanced Network (AENet), a novel detection framework that leverages both the main and auxiliary views of the same object. The main-view pipeline focuses on detecting common categories, while the auxiliary-view pipeline handles more challenging categories using ``expert models" learned from the main view. Extensive experiments on the LDXray dataset demonstrate that the dual-view mechanism significantly enhances detection performance, e.g., achieving improvements of up to 24.7% for the challenging category of umbrellas. Furthermore, our results show that AENet exhibits strong generalization across seven different detection models for X-ray Inspection
Dual-Level Boost Network for Long-Tail Prohibited Items Detection in X-ray Security Inspection
Authors: Renshuai Tao, Haoyu Wang, Wei Wang, Yunchao Wei, Yao Zhao
Date: 2024-11-27 00:00:00
Tasks: Data Augmentation

The detection of prohibited items in X-ray security inspections is vital for ensuring public safety. However, the long-tail distribution of item categories, where certain prohibited items are far less common, poses a big challenge for detection models, as rare categories often lack sufficient training data. Existing methods struggle to classify these rare items accurately due to this imbalance. In this paper, we propose a Dual-level Boost Network (DBNet) specifically designed to overcome these challenges in X-ray security screening. Our approach introduces two key innovations: (1) a specific data augmentation strategy employing Poisson blending, inspired by the characteristics of X-ray images, to generate realistic synthetic instances of rare items which can effectively mitigate data imbalance; and (2) a context-aware feature enhancement module that captures the spatial and semantic interactions between objects and their surroundings, enhancing classification accuracy for underrepresented categories. Extensive experimental results demonstrate that DBNet improves detection performance for tail categories, outperforming sota methods in X-ray security inspection scenarios by a large margin 17.2%, thereby ensuring enhanced public safety.
SmileSplat: Generalizable Gaussian Splats for Unconstrained Sparse Images
Authors: Yanyan Li, Yixin Fang, Federico Tombari, Gim Hee Lee
Date: 2024-11-27 00:00:00
Tasks: Decoder, Novel View Synthesis

Sparse Multi-view Images can be Learned to predict explicit radiance fields via Generalizable Gaussian Splatting approaches, which can achieve wider application prospects in real-life when ground-truth camera parameters are not required as inputs. In this paper, a novel generalizable Gaussian Splatting method, SmileSplat, is proposed to reconstruct pixel-aligned Gaussian surfels for diverse scenarios only requiring unconstrained sparse multi-view images. First, Gaussian surfels are predicted based on the multi-head Gaussian regression decoder, which can are represented with less degree-of-freedom but have better multi-view consistency. Furthermore, the normal vectors of Gaussian surfel are enhanced based on high-quality of normal priors. Second, the Gaussians and camera parameters (both extrinsic and intrinsic) are optimized to obtain high-quality Gaussian radiance fields for novel view synthesis tasks based on the proposed Bundle-Adjusting Gaussian Splatting module. Extensive experiments on novel view rendering and depth map prediction tasks are conducted on public datasets, demonstrating that the proposed method achieves state-of-the-art performance in various 3D vision tasks. More information can be found on our project page (https://yanyan-li.github.io/project/gs/smilesplat)
Large Scale Evaluation of Deep Learning-based Explainable Solar Flare Forecasting Models with Attribution-based Proximity Analysis
Authors: Temitope Adeyeha, Chetraj Pandey, Berkay Aydin
Date: 2024-11-27 00:00:00
Tasks: Solar Flare Prediction

Accurate and reliable predictions of solar flares are essential due to their potentially significant impact on Earth and space-based infrastructure. Although deep learning models have shown notable predictive capabilities in this domain, current evaluations often focus on accuracy while neglecting interpretability and reliability--factors that are especially critical in operational settings. To address this gap, we propose a novel proximity-based framework for analyzing post hoc explanations to assess the interpretability of deep learning models for solar flare prediction. Our study compares two models trained on full-disk line-of-sight (LoS) magnetogram images to predict $\geq$M-class solar flares within a 24-hour window. We employ the Guided Gradient-weighted Class Activation Mapping (Guided Grad-CAM) method to generate attribution maps from these models, which we then analyze to gain insights into their decision-making processes. To support the evaluation of explanations in operational systems, we introduce a proximity-based metric that quantitatively assesses the accuracy and relevance of local explanations when regions of interest are known. Our findings indicate that the models' predictions align with active region characteristics to varying degrees, offering valuable insights into their behavior. This framework enhances the evaluation of model interpretability in solar flare forecasting and supports the development of more transparent and reliable operational systems.
GLS: Geometry-aware 3D Language Gaussian Splatting
Authors: Jiaxiong Qiu, Liu Liu, Zhizhong Su, Tianwei Lin
Date: 2024-11-27 00:00:00
Tasks: 3DGS, Segmentation, Surface Reconstruction

Recently, 3D Gaussian Splatting (3DGS) has achieved significant performance on indoor surface reconstruction and open-vocabulary segmentation. This paper presents GLS, a unified framework of surface reconstruction and open-vocabulary segmentation based on 3DGS. GLS extends two fields by exploring the correlation between them. For indoor surface reconstruction, we introduce surface normal prior as a geometric cue to guide the rendered normal, and use the normal error to optimize the rendered depth. For open-vocabulary segmentation, we employ 2D CLIP features to guide instance features and utilize DEVA masks to enhance their view consistency. Extensive experiments demonstrate the effectiveness of jointly optimizing surface reconstruction and open-vocabulary segmentation, where GLS surpasses state-of-the-art approaches of each task on MuSHRoom, ScanNet++, and LERF-OVS datasets. Code will be available at https://github.com/JiaxiongQ/GLS.