pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Scale Feature Fusion using Channel Transformers for Guided Thermal Image Super Resolution
Authors: Raghunath Sai Puttagunta, Birendra Kathariya, Zhu Li, George York
Date: 2024-06-18 00:00:00
Proceeding: CVPR Workshop 2024 6
Tasks: Image Super-Resolution, Infrared And Visible Image Fusion, Infrared image super-resolution, Super-Resolution

Thermal imaging, leveraging the infrared spectrum, offers a compelling alternative to visible spectrum (VIS) imagery in challenging environmental conditions like low-light, occlusions, and adverse weather. However, its widespread adoption in computer vision tasks is hampered by lower spatial resolution.

We address this challenge by proposing a novel framework titled Multi-Scale Feature Fusion using Channel Transformers (MSFFCT) for Guided Thermal Image Super-Resolution (GTISR).

GTISR tackles the resolution limitations of thermal imagery. It leverages high-resolution RGB information as a guide to reconstruct high-resolution thermal imagery from low-resolution thermal inputs.

At the core of MSFFCT lies a novel deep learning architecture that combines the strengths of two powerful approaches: channel-based transformers and multi-scale fusion.

MSFFCT overcomes inherent limitations of Convolutional Neural Networks (CNNs) typically used in super-resolution tasks. CNNs often suffer from restricted receptive fields, limiting their ability to capture long-range dependencies within the image. Additionally, computational cost grows significantly with larger inputs.

MSFFCT addresses these shortcomings by enabling efficient processing of global information and offering superior scalability.

MSFFCT achieved state-of-the-art results on the ×8 and ×16 GTISR tasks of the 2024 Perception Beyond Visual Spectrum (PBVS) challenge, winning 2nd place in both tasks and demonstrating its effectiveness in real-world scenarios.
UniFa: A unified feature hallucination framework for any-shot object detection
Authors: Hui Nie, Ruiping Wang, Xilin Chen
Date: 2025-03-01 00:00:00
Proceeding: journal 2025 3
Tasks: Generalized Zero-Shot Object Detection, Hallucination, object-detection, Object Detection, Zero-Shot Object Detection

Any-shot object detection seeks to simultaneously detect base (many-shot), few-shot and zero-shot categories. The primary challenge lies in insufficient visual data for rare (few-shot and zero-shot) categories, hindering effective training. Existing methods use visual feature generation to alleviate it, but the quality of the generated features is low and limited to zero-shot object detection task (i.e., only including zero-shot categories). This mainly arises from semantic information for feature generation trained on unimodal data lacking visual-awareness, and the significant distinctness of generated features across categories. To tackle these issues, we introduce the Unified Feature Hallucination (UniFa) framework, which generates high-quality features for two rare categories. Utilizing CLIP’s text encoder, we transform category names into visual-aware semantic information for generating visual features, facilitating better visual-semantic alignment. A semantically blended feature enhancer is utilized to merge features from any two categories, producing denser and more realistic features. The effectiveness of our approach is confirmed through extensive experiments on MSCOCO datasets.
Actuarial Learning for Pension Fund Mortality Forecasting
Authors: Eduardo Fraga L. de Melo, Helton Graziadei, Rodrigo Targino
Date: 2025-04-08 00:00:00
Tasks: Management

For the assessment of the financial soundness of a pension fund, it is necessary to take into account mortality forecasting so that longevity risk is consistently incorporated into future cash flows. In this article, we employ machine learning models applied to actuarial science ({\it actuarial learning}) to make mortality predictions for a relevant sample of pension funds' participants. Actuarial learning represents an emerging field that involves the application of machine learning (ML) and artificial intelligence (AI) techniques in actuarial science. This encompasses the use of algorithms and computational models to analyze large sets of actuarial data, such as regression trees, random forest, boosting, XGBoost, CatBoost, and neural networks (eg. FNN, LSTM, and MHA). Our results indicate that some ML/AI algorithms present competitive out-of-sample performance when compared to the classical Lee-Carter model. This may indicate interesting alternatives for consistent liability evaluation and effective pension fund risk management.
Differential Adjusted Parity for Learning Fair Representations
Authors: Bucher Sahyouni, Matthew Vowels, Liqun Chen, Simon Hadfield
Date: 2025-02-13 00:00:00
Tasks: Fairness

The development of fair and unbiased machine learning models remains an ongoing objective for researchers in the field of artificial intelligence. We introduce the Differential Adjusted Parity (DAP) loss to produce unbiased informative representations. It utilises a differentiable variant of the adjusted parity metric to create a unified objective function. By combining downstream task classification accuracy and its inconsistency across sensitive feature domains, it provides a single tool to increase performance and mitigate bias. A key element in this approach is the use of soft balanced accuracies. In contrast to previous non-adversarial approaches, DAP does not suffer a degeneracy where the metric is satisfied by performing equally poorly across all sensitive domains. It outperforms several adversarial models on downstream task accuracy and fairness in our analysis. Specifically, it improves the demographic parity, equalized odds and sensitive feature accuracy by as much as 22.5\%, 44.1\% and 40.1\%, respectively, when compared to the best performing adversarial approaches on these metrics. Overall, the DAP loss and its associated metric can play a significant role in creating more fair machine learning models.
SCINet: Spatial and Contrast Interactive Super-Resolution Assisted Infrared UAV Target Detection
Authors: Houzhang Fang, Lan Ding, Xiaolin Wang, Yi Chang, Luxin Yan, Li Liu, Jinrui Fang
Date: 2024-10-01 00:00:00
Proceeding: IEEE Transactions on Geoscience and Remote Sensing 2024 10
Tasks: Super-Resolution

Unmanned aerial vehicle (UAV) detection based on
thermal infrared imaging has been one of the most important
sensing technologies in the anti-UAV system. However, the
technical limitations and long-range detection of thermal sensors
often lead to acquiring low-resolution (LR) infrared images,
thereby bringing great challenges for the subsequent target
detection task. In this article, we propose a novel spatial
and contrast interactive super-resolution network (SCINet) for
assisting infrared UAV target detection. The network consists
of two main subnetworks: a spatial enhancement branch (SEB)
and a contrast enhancement branch (CEB). The SEB embeds
the lightweight convolution module and attention mechanism
to highlight the spatial structure detail features of infrared
UAV targets. The proposed CEB incorporates the centeroriented
contrast-aware module and multibranch collapsible
module, which can provide local contrast priors to reconstruct
the super-resolved UAV target image. The spatial features
of the intermediate layers from the SEB are integrated
into the CEB as a rich gradient prior. Besides, the output
features of the CEB are aggregated into those of the SEB
for further supplementing the contrast of spatial features in
return. Dual-branch feature interaction (DBFI) of the SEB
and CEB can further enhance the spatial details and target
saliency of the targets. In addition, we also introduce an
infrared UAV detection network via a new dual-dimensional
feature calibration module (DFCM) for boosting the detection
performance. Extensive experiments demonstrate that the SCINet
outperforms the state-of-the-art (SOTA) SR methods on real
infrared UAV sequences and improves the detection performance
of infrared small UAV targets. The code is available at
https://github.com/IVPLaboratory/SCINet.
Infrared Small UAV Target Detection Based on Depthwise Separable Residual Dense Network and Multiscale Feature Fusion
Authors: Houzhang Fang; Lan Ding; Liming Wang; Yi Chang; Luxin Yan; Jinhui Han
Date: 2022-08-11 00:00:00
Proceeding: IEEE Transactions on Instrumentation and Measurement 2022 8
Tasks: Decoder

Unmanned aerial vehicles (UAVs) have been widely
applied in military and civilian fields, but they also pose great
threats to restricted areas, such as densely populated areas and
airports. Thermal infrared (IR) imaging technology is capable
of monitoring UAVs at a long range in both day and night
conditions. Therefore, the anti-UAV technology based on thermal
IR imaging has attracted growing attention. However, the images
acquired by IR sensors often suffer from small and dim targets,
as well as heavy background clutter and noise. Conventional
detection methods usually have a high false alarm rate and low
detection accuracy. This article proposes a detection method that
formulates the UAV detection as predicting the residual image
(i.e., background, clutter, and noise) by learning the nonlinear
mapping from the input image to the residual image. The
UAV target image is obtained by subtracting the residual image
from the input IR image. The constructed end-to-end U-shaped
network exploits the depthwise separable residual dense blocks in
the encoder stage to extract the abundant hierarchical features.
Besides, the multiscale feature fusion and representation block
is introduced to fully aggregate multiscale features from the
encoder layers and intermediate connection layers at the same
scale, as well as the decoder layers at different scales, to better
reconstruct the residual image in the decoder stage. In addition,
the global residual connection is adopted in the proposed network
to provide long-distance information compensation and promote
gradient backpropagation, which further improves the performance
in reconstructing the image. The experimental results
show that the proposed method achieves favorable detection
performance in real-world IR images and outperforms other
state-of-the-art methods in terms of quantitative and qualitative
evaluation metrics.
Differentiated Attention Guided Network Over Hierarchical and Aggregated Features for Intelligent UAV Surveillance
Authors: Houzhang Fang, Zikai Liao, Xuhua Wang, Yi Chang, and Luxin Yan
Date: 2023-01-17 00:00:00
Proceeding: IEEE Transactions on Industrial Informatics 2023 1

Intelligent unmanned aerial vehicle (UAV)
surveillance based on infrared imaging has wide applications
in the anti-UAV system for protecting urban security
and aerial safety. However, weak target features and
complex background distraction pose great challenges for
the accurate detection of UAVs. To address this issue, we
propose a novel differentiated attention guided network to
adaptively strengthen the discriminative features between
UAV targets and complex background. First, a novel spatialaware
channel attention (SCA) is introduced into deep layers
via preserving critical spatial features and leveraging
channel interdependencies to focus on the large-scale targets.
The channel-modulated deformable spatial attention
is introduced into shallow layers via refining channel context
and dynamically perceiving the spatial features for focusing
on the small-scale targets. A combination of the
above two attention mechanisms is employed in intermediate
layers of the network for concentrating on the mediumscale
targets. Then, we embed a feature aggregator at the
detection branches to guide the information exchange of
high-level feature maps and low-level feature maps with a
bottom-up context modulation, and integrate an SCA at the
end to further boost the distinctive feature representation
for task-awareness. The above design can adaptively enhance
multiscale UAV target features and suppress complex
background interferences, leading to better detection
performance, especially for small targets. Extensive experiments
on real infrared UAV datasets reveal that the proposed
method outperforms the baseline object detectors by a large margin, validating its feasibility in real-world
infrared UAV detection. The source code can be found at
https://github.com/KALEIDOSCOPEIP/DAGNet.
On the Design of Quantum Graph Convolutional Neural Network in the NISQ-Era and Beyond
Authors: Zhirui Hu, Jinyang Li, Zhenyu Pan, Shanglin Zhou, Lei Yang, Caiwen Ding, Omer Khan, Tong Geng, Weiwen Jiang
Date: 2022-12-19 00:00:00
Proceeding: IEEE 40th International Conference on Computer Design (ICCD) 2022 12
Tasks: CPU, GPU, Node Classification

The rapid growth in the size of Graph Convolutional Neural Networks (GCNs) encounters both computational- and memory-wall on classical computing platforms (e.g., CPU, GPU, FPGA, etc.). Quantum computing, on the other hand, provides extremely high parallelism for computation. Although quantum neural networks have been recently studied, the research on quantum graph neural networks is still in its infancy. The key challenge here is how to integrate both the graph topology information and the learning ability of GCNs into quantum circuits. In this work, we leverage the Givens rotations and its quantum implementation to encode graph information; in addition, we employ the widely used variational quantum circuit to bring the learnable parameters. On top of these, we present a full-quantum design of Graph Convolutional Neural Networks, namely "QuGCN", for semi-supervised learning on graph-structured data. Experiment results show our design is competitive with classical GCNs in terms of node classification accuracy on Cora sub-dataset. More importantly, we show the potential advantages that can be achieved by the proposed quantum GCN design when the number of features grows.
Low Rank Learning for Offline Query Optimization
Authors: Zixuan Yi, Yao Tian, Zachary G. Ives, Ryan Marcus
Date: 2025-04-08 00:00:00
Tasks: Active Learning

Recent deployments of learned query optimizers use expensive neural networks and ad-hoc search policies. To address these issues, we introduce \textsc{LimeQO}, a framework for offline query optimization leveraging low-rank learning to efficiently explore alternative query plans with minimal resource usage. By modeling the workload as a partially observed, low-rank matrix, we predict unobserved query plan latencies using purely linear methods, significantly reducing computational overhead compared to neural networks. We formalize offline exploration as an active learning problem, and present simple heuristics that reduces a 3-hour workload to 1.5 hours after just 1.5 hours of exploration. Additionally, we propose a transductive Tree Convolutional Neural Network (TCNN) that, despite higher computational costs, achieves the same workload reduction with only 0.5 hours of exploration. Unlike previous approaches that place expensive neural networks directly in the query processing ``hot'' path, our approach offers a low-overhead solution and a no-regressions guarantee, all without making assumptions about the underlying DBMS. The code is available in \href{https://github.com/zixy17/LimeQO}{https://github.com/zixy17/LimeQO}.
SPIRe: Boosting LLM Inference Throughput with Speculative Decoding
Authors: Sanjit Neelam, Daniel Heinlein, Vaclav Cvicek, Akshay Mishra, Reiner Pope
Date: 2025-04-08 00:00:00

Speculative decoding (SD) has been shown to reduce the latency of autoregressive decoding (AD) by 2-3x for small batch sizes. However, increasing throughput and therefore reducing the cost per token requires decoding with large batch sizes. Recent work shows that SD can accelerate decoding with large batch sizes too if the context is sufficiently long and the draft model's KV cache is sparse. We introduce SPIRe, a draft model that combines static sparse attention, pruned initialization, and feedback memory to increase the modeled throughput of speculative decoding by over 100% compared to speculation with a much smaller draft model and by over 35% compared to the strong baseline of sparse self-speculation. Our approach is particularly effective when context lengths vary significantly across requests.
Polygon: Symbolic Reasoning for SQL using Conflict-Driven Under-Approximation Search
Authors: Pinhan Zhao, Yuepeng Wang, Xinyu Wang
Date: 2025-04-09 00:00:00

We present a novel symbolic reasoning engine for SQL which can efficiently generate an input $I$ for $n$ queries $P_1, \cdots, P_n$, such that their outputs on $I$ satisfy a given property (expressed in SMT). This is useful in different contexts, such as disproving equivalence of two SQL queries and disambiguating a set of queries. Our first idea is to reason about an under-approximation of each $P_i$ -- that is, a subset of $P_i$'s input-output behaviors. While it makes our approach both semantics-aware and lightweight, this idea alone is incomplete (as a fixed under-approximation might miss some behaviors of interest). Therefore, our second idea is to perform search over an expressive family of under-approximations (which collectively cover all program behaviors of interest), thereby making our approach complete. We have implemented these ideas in a tool, Polygon, and evaluated it on over 30,000 benchmarks across two tasks (namely, SQL equivalence refutation and query disambiguation). Our evaluation results show that Polygon significantly outperforms all prior techniques.
Domain Generalization through Attenuation of Domain-Specific Information
Authors: Reiji Saito, Kazuhiro Hotta
Date: 2025-04-09 00:00:00
Tasks: Domain Generalization, Semantic Segmentation

In this paper, we propose a new evaluation metric called Domain Independence (DI) and Attenuation of Domain-Specific Information (ADSI) which is specifically designed for domain-generalized semantic segmentation in automotive images. DI measures the presence of domain-specific information: a lower DI value indicates strong domain dependence, while a higher DI value suggests greater domain independence. This makes it roughly where domain-specific information exists and up to which frequency range it is present. As a result, it becomes possible to effectively suppress only the regions in the image that contain domain-specific information, enabling feature extraction independent of the domain. ADSI uses a Butterworth filter to remove the low-frequency components of images that contain inherent domain-specific information such as sensor characteristics and lighting conditions. However, since low-frequency components also contain important information such as color, we should not remove them completely. Thus, a scalar value (ranging from 0 to 1) is multiplied by the low-frequency components to retain essential information. This helps the model learn more domain-independent features. In experiments, GTA5 (synthetic dataset) was used as training images, and a real-world dataset was used for evaluation, and the proposed method outperformed conventional approaches. Similarly, in experiments that the Cityscapes (real-world dataset) was used for training and various environment datasets such as rain and nighttime were used for evaluation, the proposed method demonstrated its robustness under nighttime conditions.
TAGC: Optimizing Gradient Communication in Distributed Transformer Training
Authors: Igor Polyakov, Alexey Dukhanov, Egor Spirin
Date: 2025-04-08 00:00:00

The increasing complexity of large language models (LLMs) necessitates efficient training strategies to mitigate the high computational costs associated with distributed training. A significant bottleneck in this process is gradient synchronization across multiple GPUs, particularly in the zero-redundancy parallelism mode. In this paper, we introduce Transformer-Aware Gradient Compression (TAGC), an optimized gradient compression algorithm designed specifically for transformer-based models. TAGC extends the lossless homomorphic compression method by adapting it for sharded models and incorporating transformer-specific optimizations, such as layer-selective compression and dynamic sparsification. Our experimental results demonstrate that TAGC accelerates training by up to 15% compared to the standard Fully Sharded Data Parallel (FSDP) approach, with minimal impact on model quality. We integrate TAGC into the PyTorch FSDP framework, the implementation is publicly available at https://github.com/ipolyakov/TAGC.
FlexChunk: Enabling 100M×100M Out-of-Core SpMV (~1.8 min, ~1.7 GB RAM) with Near-Linear Scaling
Authors: Daniil Strizhov
Date: 2025-04-05 00:00:00
Proceeding: LessWrong 2025 4
Tasks: Chunking, Nature-Inspired Optimization Algorithm

Handling large-scale sparse matrices is a fundamental task in many scientific and engineering domains, yet standard in-memory approaches often hit the limitations of available RAM. This paper introduces FlexChunk, an algorithm employing a chunking strategy and disk caching for efficient sparse matrix-vector multiplication (SpMV) specifically designed for matrices exceeding available memory. Our experiments demonstrate that FlexChunk achieves near-linear O(N) time complexity and linear memory consumption scaling up to matrix sizes of 100M×100M, processing the largest case in 1 minute 47 seconds with a peak memory footprint of 1.7 GB. A comparison with the optimized SciPy library reveals a key trade-off: while SciPy offers significantly faster computation for in-memory matrices, FlexChunk excels in scenarios requiring disk I/O due to its substantially lower data loading times. The primary contribution is demonstrating FlexChunk as a scalable, memory-efficient solution enabling SpMV operations on problem sizes previously intractable due to memory constraints.
Detection-Friendly Nonuniformity Correction: A Union Framework for Infrared UAVTarget Detection
Authors: Houzhang Fang, Xiaolin Wang, Zengyang Li, Lu Wang, Qingshan Li, Yi Chang, Luxin Yan
Date: 2025-04-05 00:00:00
Tasks: parameter estimation

Infrared unmanned aerial vehicle (UAV) images captured using thermal detectors are often affected by temperature dependent low-frequency nonuniformity, which significantly reduces the contrast of the images. Detecting UAV targets under nonuniform conditions is crucial in UAV surveillance applications. Existing methods typically treat infrared nonuniformity correction (NUC) as a preprocessing step for detection, which leads to suboptimal performance. Balancing the two tasks while enhancing detection beneficial information remains challenging. In this paper, we present a detection-friendly union framework, termed UniCD, that simultaneously addresses both infrared NUC and UAV target detection tasks in an end-to-end manner. We first model NUC as a small number of parameter estimation problem jointly driven by priors and data to generate detection-conducive images. Then, we incorporate a new auxiliary loss with target mask supervision into the backbone of the infrared UAV target detection network to strengthen target features while suppressing the background. To better balance correction and detection, we introduce a detection-guided self-supervised loss to reduce feature discrepancies between the two tasks, thereby enhancing detection robustness to varying nonuniformity levels. Additionally, we construct a new benchmark composed of 50,000 infrared images in various nonuniformity types, multi-scale UAV targets and rich backgrounds with target annotations, called IRBFD. Extensive experiments on IRBFD demonstrate that our UniCD is a robust union framework for NUC and UAV target detection while achieving real-time processing capabilities. Dataset can be available at https://github.com/IVPLaboratory/UniCD.