Abstract

Text as signs, labels, or instructions is a critical element of real-world scenes as they can convey important contextual information. 3D representations such as 3D Gaussian Splatting (3DGS) struggle to preserve fine-grained text details, while achieving high visual fidelity. Small errors in textual element reconstruction can lead to significant semantic loss. We propose STRinGS, a text-aware, selective refinement framework to address this issue for 3DGS reconstruction. Our method treats text and non-text regions separately, refining text regions first and merging them with non-text regions later for full-scene optimization. STRinGS produces sharp, readable text even in challenging configurations. We introduce a text readability measure OCR Character Error Rate (CER) to evaluate the efficacy on text regions. STRinGS results in a 63.6% relative improvement over 3DGS at just 7K iterations. We also introduce a curated dataset STRinGS-360 with diverse text scenarios to evaluate text readability in 3D reconstruction. Our method and dataset together push the boundaries of 3D scene understanding in text-rich environments, paving the way for more robust text-aware reconstruction methods.

Abstract

Scene understanding and reasoning has been a fundamental problem in 3D computer vision, requiring models to identify objects, their properties, and spatial or comparative relationships among the objects. Existing approaches enable this by creating scene graphs using multiple inputs such as 2D images, depth maps, object labels, and annotated relationships from specific reference view. However, these methods often struggle with generalization and produce inaccurate spatial relationships like "left/right", which become inconsistent across different viewpoints. To address these limitations, we propose Viewpoint-Invariant ZerO-shot scene graph generation for 3D scene Reasoning (VIZOR). VIZOR is a training-free, end-to-end framework that constructs dense, viewpoint-invariant 3D scene graphs directly from raw 3D scenes. The generated scene graph is unambiguous, as spatial relationships are defined relative to each object’s front-facing direction, making them consistent regardless of the reference view. Furthermore, it infers open-vocabulary relationships that describe spatial and proximity relationships among scene objects without requiring annotated training data. We conduct extensive quantitative and qualitative evaluations to assess the effectiveness of VIZOR on scene graph generation and downstream tasks, such as query-based object grounding. VIZOR outperforms state-of-the-art methods, showing clear improvements in scene graph generation and achieving 22% and 4.81% gains in zero-shot grounding accuracy on the Replica and Nr3D datasets, respectively.

Abstract

Generating realistic human motions that naturally respond to both spoken language and physical objects is crucial for interactive digital experiences. Current methods, however, address speech-driven gestures or object interactions independently, limiting real-world applicability due to a lack of integrated, comprehensive datasets. To overcome this, we introduce InteracTalker, a novel framework that seamlessly integrates prompt-based object-aware interactions with co-speech gesture generation. We achieve this by employing a multi-stage training process to learn a unified motion, speech, and prompt embedding space. To support this, we curate a rich human-object interaction dataset, formed by augmenting an existing text-to-motion dataset with detailed object interaction annotations. Our framework utilizes a Generalized Motion Adaptation Module that enables independent training, adapting to the corresponding motion condition, which is then dynamically combined during inference. To address the imbalance between heterogeneous conditioning signals, we propose an adaptive fusion strategy, which dynamically reweights the conditioning signals during diffusion sampling. InteracTalker successfully unifies these previously separate tasks, outperforming prior methods in both co-speech gesture generation and object-interaction synthesis, outperforming gesture-focused diffusion methods, yielding highly realistic, object-aware full-body motions with enhanced realism, flexibility, and control.(https://sreeharirajan.github.io/projects/InteracTalker/)

Abstract

Early-stage fruit yield prediction plays a key role in supporting timely agronomic decisions, enhancing market planning, and empowering farmers with data-driven insights. Over the years, most approaches to yield estimation have focused on fruit counting techniques, typically performed just before harvest. While these methods have proven useful, they often come into play late in the cultivation cycle, limiting their impact on early planning and resource optimization. In this work, we introduce a comprehensive baseline framework for predicting mango yield at an earlier stage - during flowering - using image-based learning. Our contributions are twofold. (i) Our approach combines a SegFormer-based segmentation model with a regression pipeline to estimate yield from images, while also exploring the role of contextual features such as weather and scale. (ii) This work introduces a novel benchmark and an enriched dataset, paving the way for scalable, automated tools that can assist farmers and stakeholders in making proactive decisions throughout the mango growing season. Our work demonstrates that for multi-modal yield prediction, integrating features that complement visual representations (like scale) can be more impactful than using features with a stronger standalone linear correlation (like weather). Our single-image model, based on the SegFormer-B1 encoder, achieved a mean absolute error (MAE) of 7.68, R² of 0.76, and mean squared error (MSE) of 115.48. These results highlight the promise of vision-based models for yield estimation from early-stage flowering cues. To the best of our knowledge, this is the first work to address the prediction of mango yield using images from the flowering stage and weather data.

Abstract

Temporal Graph Neural Networks (TGNNs) are increasingly used in high-stakes domains, such as financial forecasting, recommendation systems, and fraud detection. However, their susceptibility to poisoning attacks poses a critical security risk. We introduce LORETTA (Low Resource Twophase Temporal Attack), a novel adversarial framework on Continuous-Time Dynamic Graphs, which degrades TGNN performance by an average of 29.47% across 4 widely benchmark datasets and 4 State-of-the-Art (SotA) models. LORETTA operates through a two-stage approach: (1) sparsify the graph by removing high-impact edges using any of the 16 tested temporal importance metrics, (2) strategically replace removed edges with adversarial negatives via LORETTA’s novel degree-preserving negative sampling algorithm. Our plug-and-play design eliminates the need for expensive surrogate models while adhering to realistic unnoticeability constraints. LORETTA degrades performance by upto 42.0% on MOOC, 31.5% on Wikipedia, 28.8% on UCI, and 15.6%on Enron. LORETTA outperforms 11 attack baselines, remains undetectable to 4 leading anomaly detection systems, and is robust to 4 SotA adversarial defense training methods, establishing its effectiveness, unnoticeability, and robustness.

Abstract

The paper details a system-level design of a nanosecond-range pulsed laser diode driver with adjustable current and high pulse repetition rate, optimised for low-power laser diodes. The design incorporates the summing of two independently adjustable constant current sources, one provides DC bias, while the second one produces current pulses (via externally modulated GaN FET). The circuit is simulated in LTspice with two laser diode SPICE models, and simulation results are analysed to evaluate system performance and stability.

Abstract

This study investigates how well computational embeddings align with human semantic judgments in the processing of English compound words. We compare static word vectors (GloVe) and contextualized embeddings (BERT) against human ratings of lexeme meaning dominance (LMD) and semantic transparency (ST) drawn from a psycholinguistic dataset. Using measures of association strength (Edinburgh Associative Thesaurus), frequency (BNC), and predictability (LaDEC), we compute embedding-derived LMD and ST metrics and assess their relationships with human judgments via Spearmans correlation and regression analyses. Our results show that BERT embeddings better capture compositional semantics than GloVe, and that predictability ratings are strong predictors of semantic transparency in both human and model data. These findings advance computational psycholinguistics by clarifying the factors that drive compound word processing and offering insights into embedding-based semantic modeling.

Abstract

Breast cancer, the most common malignancy among women, requires precise detection and classification for effective treatment. Among immunohistochemistry (IHC) biomarkers, HER2 plays a critical role in guiding therapy decisions. In particular, a recent clinical trial has shown that 3-way classification of HER2 (0, low, and high) using IHC is essential for identifying patients with HER2 low expression who may benefit from new targeted therapies. However, traditional IHC classification relies on the expertise of pathologists, making it labor-intensive and prone to significant inter-observer variability. To address these challenges, this study introduces the India Pathology Breast Cancer Dataset, comprising HER2 IHC slides from 500 patients, with a primary focus on automating 3-way HER2 classification. Evaluation of multiple deep learning models revealed that an end-to-end ConvNeXt network using low-resolution IHC images achieved an F1 score of 83.52%, representing an improvement of 5.35% over patch-based methods. Class-wise F1 scores were 75.6% for HER2-0, 82.4% for HER2-low, and 91.5% for HER2-high, indicating the challenge in distinguishing HER2-0 and HER2-low cases. This study highlights the potential of simple yet effective deep learning techniques to significantly improve accuracy and reproducibility in breast cancer classification, supporting their integration into clinical workflows for better patient outcomes.

Abstract

Stuttering is a complex speech disorder that challenges both ASR systems and clinical assessment. We propose a multimodal stuttering detection and classification model that integrates acoustic and linguistic features through a two-stage fusion mechanism. Fine-tuned Wav2Vec 2.0 and HuBERT extract acoustic embeddings, which are early fused with MFCC features to capture fine-grained spectral and phonetic variations, while Llama-2 embeddings from Whisper ASR transcriptions provide linguistic context. To enhance robustness against out-of-distribution speech patterns, we incorporate Retrieval-Augmented Generation or adaptive classification. Our model achieves state-of-the-art performance on SEP-28k and FluencyBank, demonstrating significant improvements in detecting challenging stuttering events. Additionally, our analysis highlights the complementary nature of acoustic and linguistic modalities, reinforcing the need for multimodal approaches in speech disorder detection.

Abstract

This study evaluated the performance of 36 Coupled Model Inter-comparison Project Phase 6 (CMIP6) Global Climate Models (GCMs) in simulating extreme precipitation conditions across India using an integrated bias correction and multi-criteria decision-making framework. Daily precipitation data spanning 1975-2014 from CMIP6 GCMs were bias-corrected against Indian Meteorological Department (IMD) gridded observations at 0.25° spatial resolution using three distinct methods: (i) monthly quantile mapping applying empirical cumulative distribution function transformation on monthly aggregated data, (ii) daily quantile mapping using complete daily time series with linear interpolation for intermediate quantiles, and (iii) ISIMIP3b parametric trend-preserving quantile mapping employing gamma distribution fitting with mixed trend preservation strategy. Three extreme precipitation indices were calculated from bias-corrected data: total annual precipitation on wet days (PRCPTOTAL), 99th percentile of daily precipitation (R99p), frequency of days exceeding 99th percentile (R99pf). Performance evaluation employed Taylor Skill Score incorporating correlation and variance ratios, Pearson correlation coefficient (PCC), root-mean-square error (RMSE), and normalised standard deviation against IMD reference data. For the R99p index, PCC values ranged from -0.045 to 0.628, RMSE varied from 29.86 to 58.23 mm, and Taylor Skill Scores ranged from 0.72 to 0.17 across all GCMs. For R99p frequency, PCC ranged from 0.242 to 0.663, RMSE varied from 0.526 to 1.380, and Taylor Skill Scores ranged from 0.83 to 0.59. Multi-criteria decision-making integration used Analytic Hierarchy Process (AHP) with expert judgment-based pairwise comparison matrices for criteria weighting, followed by Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and VlseKriterijumska Optimizacija Kompromisno Resenje (VIKOR) ranking methods with entropy-based objective weighting. The comprehensive ranking revealed EC-Earth3-Veg as the best performing GCM with a combined TOPSIS score of 0.890, followed by EC-Earth3-CC (0.880) and EC-Earth3 (0.875). MIROC6 achieved the highest Taylor Skill Score (0.717) for R99p intensity simulation, while GFDL-CM4 demonstrated consistent performance across both intensity and frequency metrics.