arXiv subjects

Thinesh Thiyakesan Ponbagavathi

Publications and source records attributed to Thinesh Thiyakesan Ponbagavathi.

5 recordsLinked to original sources

arXiv2026

Structured Relational Reasoning for Group Activity Assessment

Group Activity Detection (GAD) involves recognizing social groups and their collective behaviors in videos. Vision Foundation Models (VFMs), like DINOv2, offer excellent features but are pretrained on object-centric data. We find that naively substituting them into existing GAD pipelines actually degrades performance, exposing structured group-aware decoding as the true bottleneck. We introduce ProGraD, a structured relational-reasoning framework for GAD built on top of frozen VFMs. At its core is a lightweight two-layer GroupContext Transformer that explicitly models actor-group associations and aggregates global context to infer collective behavior. Learnable group prompts serve as a minimal conditioning mechanism to guide the frozen backbone toward socially relevant representations, while the relational decoder performs the core reasoning over actors and groups. This design jointly infers group locations, memberships, and activities in a single pass using only 10M trainable parameters - less than half of prior methods. On the Cafe benchmark with multiple concurrent social groups, ProGraD improves the state-of-the-art by 6.5% Group mAP$@$1.0 and 8.2% Group mAP$@$0.5. On Social-CAD, it achieves state-of-the-art social and membership accuracy. ProGraD further produces interpretable attention maps that provide insights into actor-group reasoning.

cs.CV↗

arXiv2026

Order Matters: On Parameter-Efficient Image-to-Video Probing for Recognizing Nearly Symmetric Actions

Fine-grained understanding of human actions is essential for safe and intuitive human--robot interaction. We study the challenge of recognizing nearly symmetric actions, such as picking up vs. placing down a tool or opening vs. closing a drawer. These actions are common in close human-robot collaboration, yet they are rare and largely overlooked in mainstream vision frameworks. Pretrained vision foundation models (VFMs) are often adapted using probing, valued in robotics for its efficiency and low data needs, or parameter-efficient fine-tuning (PEFT), which adds temporal modeling through adapters or prompts. However, our analysis shows that probing is permutation-invariant and blind to frame order, while PEFT is prone to overfitting on smaller HRI datasets, and less practical in real-world robotics due to compute constraints. To address this, we introduce STEP (Self-attentive Temporal Embedding Probing), a lightweight extension to probing that models temporal order via frame-wise positional encodings, a global CLS token, and a simplified attention block. Compared to conventional probing, STEP improves accuracy by 4--10% on nearly symmetric actions and 6--15% overall across action recognition benchmarks in human-robot-interaction, industrial assembly, and driver assistance. Beyond probing, STEP surpasses heavier PEFT methods and even outperforms fully fine-tuned models on all three benchmarks, establishing a new state-of-the-art. Code and models will be made publicly available: https://github.com/th-nesh/STEP.

cs.CV↗

arXiv2026

Frame2Freq: Spectral Adapters for Fine-Grained Video Understanding

Adapting image-pretrained backbones to video typically relies on time-domain adapters tuned to a single temporal scale. Our experiments show that these modules pick up static image cues and very fast flicker changes, while overlooking medium-speed motion. Capturing dynamics across multiple time-scales is, however, crucial for fine-grained temporal analysis (i.e., opening vs. closing bottle). To address this, we introduce Frame2Freq -- a family of frequency-aware adapters that perform spectral encoding during image-to-video adaptation of pretrained Vision Foundation Models (VFMs), improving fine-grained action recognition. Frame2Freq uses Fast Fourier Transform (FFT) along time and learns frequency-band specific embeddings that adaptively highlight the most discriminative frequency ranges. Across five fine-grained activity recognition datasets, Frame2Freq outperforms prior PEFT methods and even surpasses fully fine-tuned models on four of them. These results provide encouraging evidence that frequency analysis methods are a powerful tool for modeling temporal dynamics in image-to-video transfer. Code is available at https://github.com/th-nesh/Frame2Freq.

cs.CV↗

arXiv2025

T-MASK: Temporal Masking for Probing Foundation Models across Camera Views in Driver Monitoring

Changes of camera perspective are a common obstacle in driver monitoring. While deep learning and pretrained foundation models show strong potential for improved generalization via lightweight adaptation of the final layers ('probing'), their robustness to unseen viewpoints remains underexplored. We study this challenge by adapting image foundation models to driver monitoring using a single training view, and evaluating them directly on unseen perspectives without further adaptation. We benchmark simple linear probes, advanced probing strategies, and compare two foundation models (DINOv2 and CLIP) against parameter-efficient fine-tuning (PEFT) and full fine-tuning. Building on these insights, we introduce T-MASK -- a new image-to-video probing method that leverages temporal token masking and emphasizes more dynamic video regions. Benchmarked on the public Drive&Act dataset, T-MASK improves cross-view top-1 accuracy by $+1.23\%$ over strong probing baselines and $+8.0\%$ over PEFT methods, without adding any parameters. It proves particularly effective for underrepresented secondary activities, boosting recognition by $+5.42\%$ under the trained view and $+1.36\%$ under cross-view settings. This work provides encouraging evidence that adapting foundation models with lightweight probing methods like T-MASK has strong potential in fine-grained driver observation, especially in cross-view and low-data settings. These results highlight the importance of temporal token selection when leveraging foundation models to build robust driver monitoring systems. Code and models will be made available at https://github.com/th-nesh/T-MASK to support ongoing research.

cs.CV↗

arXiv2024

Probing Fine-Grained Action Understanding and Cross-View Generalization of Foundation Models

Foundation models (FMs) are large neural networks trained on broad datasets, excelling in downstream tasks with minimal fine-tuning. Human activity recognition in video has advanced with FMs, driven by competition among different architectures. However, high accuracies on standard benchmarks can draw an artificially rosy picture, as they often overlook real-world factors like changing camera perspectives. Popular benchmarks, mostly from YouTube or movies, offer diverse views but only coarse actions, which are insufficient for use-cases needing fine-grained, domain-specific actions. Domain-specific datasets (e.g., for industrial assembly) typically use data from limited static perspectives. This paper empirically evaluates how perspective changes affect different FMs in fine-grained human activity recognition. We compare multiple backbone architectures and design choices, including image- and video- based models, and various strategies for temporal information fusion, including commonly used score averaging and more novel attention-based temporal aggregation mechanisms. This is the first systematic study of different foundation models and specific design choices for human activity recognition from unknown views, conducted with the goal to provide guidance for backbone- and temporal- fusion scheme selection. Code and models will be made publicly available to the community.

cs.CV↗