Dr Sauradip Nag

Garment pattern design aims to convert a 3D garment to the corresponding 2D panels and their sewing structure. Existing methods rely either on template fitting with heuristics and prior assumptions, or on model learning with complicated shape parameterization. Importantly, both approaches do not allow for personalization of the output garment, which today has increasing demands. To fill this demand, we introduce PersonalTailor: a personalized 2D pattern design method, where the user can input specific constraints or demands (in language or sketch) for personal 2D panel fabrication from 3D point clouds. PersonalTailor first learns a multi-modal panel embeddings based on unsupervised cross-modal association and attentive fusion. It then predicts a binary panel masks individually using a transformer encoder-decoder framework. Extensive experiments show that our PersonalTailor excels on both personalized and standard pattern fabrication tasks.

Existing Temporal Action Detection (TAD) methods typ- ically take a pre-processing step in converting an input varying-length video into a fixed-length snippet represen- tation sequence, before temporal boundary estimation and action classification. This pre-processing step would tem- porally downsample the video, reducing the inference res- olution and hampering the detection performance in the original temporal resolution. In essence, this is due to a temporal quantization error introduced during the resolu- tion downsampling and recovery. This could negatively im- pact the TAD performance, but is largely ignored by existing methods. To address this problem, in this work we intro- duce a novel model-agnostic post-processing method with- out model redesign and retraining. Specifically, we model the start and end points of action instances with a Gaussian distribution for enabling temporal boundary inference at a sub-snippet level. We further introduce an efficient Taylor- expansion based approximation, dubbed as Gaussian Ap- proximated Post-processing (GAP). Extensive experiments demonstrate that our GAP can consistently improve a wide variety of pre-trained off-the-shelf TAD models on the chal- lenging ActivityNet (+0.2%∼0.7% in average mAP) and THUMOS (+0.2%∼0.5% in average mAP) benchmarks. Such performance gains are already significant and highly comparable to those achieved by novel model designs. Also, GAP can be integrated with model training for further performance gain. Importantly, GAP enables lower tem- poral resolutions for more efficient inference, facilitating low-resource applications. The code will be available in https://github.com/sauradip/GAP

Existing action recognition methods are typically actor-specific due to the intrinsic topological and apparent differences among the actors. This requires actor-specific pose estimation (e.g., humans vs. animals), leading to cumbersome model design complexity and high maintenance costs. Moreover, they often focus on learning the visual modality alone and single-label classification whilst neglecting other available information sources (e.g., class name text) and the concurrent occurrence of multiple actions. To overcome these limitations, we propose a new approach called 'actor-agnostic multi-modal multi-label action recognition,' which offers a unified solution for various types of actors, including humans and animals. We further formulate a novel Multi-modal Semantic Query Network (MSQNet) model in a transformer-based object detection framework (e.g., DETR), characterized by leveraging visual and textual modalities to represent the action classes better. The elimination of actor-specific model designs is a key advantage, as it removes the need for actor pose estimation altogether. Extensive experiments on five publicly available benchmarks show that our MSQNet consistently outperforms the prior arts of actor-specific alternatives on human and animal single- and multi-label action recognition tasks by up to 50%. Code will be released at https://github.com/mondalanindya/MSQNet.

Sound Event Detection (SED) aims to predict the temporal boundaries of all the events of interest and their class labels, given an unconstrained audio sample. Taking either the splitand-classify (i.e., frame-level) strategy or the more principled event-level modeling approach, all existing methods consider the SED problem from the discriminative learning perspective. In this work, we reformulate the SED problem by taking a generative learning perspective. Specifically, we aim to generate sound temporal boundaries from noisy proposals in a denoising diffusion process, conditioned on a target audio sample. During training, our model learns to reverse the noising process by converting noisy latent queries to the groundtruth versions in the elegant Transformer decoder framework. Doing so enables the model generate accurate event boundaries from even noisy queries during inference. Extensive experiments on the Urban-SED and EPIC-Sounds datasets demonstrate that our model significantly outperforms existing alternatives, with 40+% faster convergence in training.