1 citations · 1 across the 11 of their papers we have counts for
15 papers
Investigating Query-Insensitive Behavior in Spatio-Temporal Video Grounding
Eryk Kołodziejczyk, Alberto Presta, Karol Szurkowski +1
Spatio-temporal video grounding (STVG) aims to localize objects or events described by natural language queries in both space and time. Existing STVG models are typically trained a…
Pocket-STVG: lightweight architecture for Spatio-Temporal Video Grounding
Alberto Presta, Michal Byra, Grzegorz Stefański +3
Spatio-Temporal Video Grounding (STVG) aims to localize the spatio-temporal tube in a video corresponding to a natural language query. While recent methods achieve strong performan…
Same path, different walk: a mechanistic comparison of looped and stacked transformer encoders on 12-lead ECG
Pawel Olszowiec, Michal Byra, Grzegorz Gruszczynski +2
Recurrent Transformers reusing their weights rather than stacking distinct layers are becoming widely adopted due to their parameter efficiency [1,2,3]. However, the exact repr…
Zero-shot video highlight detection based on text descriptions and synthetic images
Michal Byra, Alberto Presta, Grzegorz Stefanski +1
Detecting video highlights, the most informative or engaging moments in a video, is important for applications such as video summarization and content recommendation. We propose a…
Training Crossroads for Recurrent Vision Transformers: Recurrence, Neural ODEs, and Deep Supervision
Grzegorz Gruszczynski, Pawel Olszowiec, Michal Byra +2
Vision Transformers (ViTs) achieve strong image-recognition performance, but their parameter count grows linearly with depth when each block is independently parameterized. Single-…
t-gems: text-guided exit modules for decreasing clip image encoder
Alberto Presta, Grzegorz Stefanski, Michal Byra +1
Multimodal deep neural networks enhance deep comprehension by integrating diverse data modalities. Data from different modalities are typically projected into a shared latent space…
Investigating Single-Block Recurrence in Vision Transformers for Image Recognition
Michal Byra, Pawel Olszowiec, Grzegorz Stefanski +2
Vision Transformers (ViTs) implement depth by stacking independently parameterized blocks, but it remains unclear how much of this parameterization is necessary and how much can be…
Routing the Lottery: Adaptive Subnetworks for Heterogeneous Data
Grzegorz Stefanski, Alberto Presta, Michal Byra
In pruning, the Lottery Ticket Hypothesis posits that large networks contain sparse subnetworks, or winning tickets, that can be trained in isolation to match the performance of th…
Denoising Diffusion Probabilistic Model for Point Cloud Compression at Low Bit-Rates
Gabriele Spadaro, Alberto Presta, Jhony H. Giraldo +5
Efficient compression of low-bit-rate point clouds is critical for bandwidth-constrained applications. However, existing techniques mainly focus on high-fidelity reconstruction, re…
Efficient Progressive Image Compression with Variance-aware Masking
Alberto Presta, Enzo Tartaglione, Attilio Fiandrotti +2
Learned progressive image compression is gaining momentum as it allows improved image reconstruction as more bits are decoded at the receiver. We propose a progressive image compre…
STanH : Parametric Quantization for Variable Rate Learned Image Compression
Alberto Presta, Enzo Tartaglione, Attilio Fiandrotti +1
In end-to-end learned image compression, encoder and decoder are jointly trained to minimize a cost function, where controls the trade-off between rate of the quantize…
GABIC: Graph-based Attention Block for Image Compression
Gabriele Spadaro, Alberto Presta, Enzo Tartaglione +3
While standardized codecs like JPEG and HEVC-intra represent the industry standard in image compression, neural Learned Image Compression (LIC) codecs represent a promising alterna…
Can We Remove the Ground? Obstacle-aware Point Cloud Compression for Remote Object Detection
Pengxi Zeng, Alberto Presta, Jonah Reinis +3
Efficient point cloud (PC) compression is crucial for streaming applications, such as augmented reality and cooperative perception. Classic PC compression techniques encode all the…
Domain Adaptation for Learned Image Compression with Supervised Adapters
Alberto Presta, Gabriele Spadaro, Enzo Tartaglione +2
In Learned Image Compression (LIC), a model is trained at encoding and decoding images sampled from a source domain, often outperforming traditional codecs on natural images; yet i…
Detection of subclinical atherosclerosis by image-based deep learning on chest x-ray
Guglielmo Gallone, Francesco Iodice, Alberto Presta +16
Aims. To develop a deep-learning based system for recognition of subclinical atherosclerosis on a plain frontal chest x-ray. Methods and Results. A deep-learning algorithm to predi…