19 citations · 60 across the 28 of their papers we have counts for
7 papers · 1 filter
Post-Processing Temporal Action Detection
Sauradip Nag, Xiatian Zhu, Yi-Zhe Song +1
Existing Temporal Action Detection (TAD) methods typically take a pre-processing step in converting an input varying-length video into a fixed-length snippet representation sequenc…
Multi-Modal Few-Shot Temporal Action Detection
Sauradip Nag, Mengmeng Xu, Xiatian Zhu +4
Few-shot (FS) and zero-shot (ZS) learning are two different approaches for scaling temporal action detection (TAD) to new classes. The former adapts a pretrained vision model to a…
Proposal-Free Temporal Action Detection via Global Segmentation Mask Learning
Sauradip Nag, Xiatian Zhu, Yi-Zhe Song +1
Existing temporal action detection (TAD) methods rely on generating an overwhelmingly large number of proposals per video. This leads to complex model designs due to proposal gener…
Large-Scale Product Retrieval with Weakly Supervised Representation Learning
Xiao Han, Kam Woh Ng, Sauradip Nag +1
Large-scale weakly supervised product retrieval is a practically useful yet computationally challenging problem. This paper introduces a novel solution for the eBay Visual Search C…
Zero-Shot Temporal Action Detection via Vision-Language Prompting
Sauradip Nag, Xiatian Zhu, Yi-Zhe Song +1
Existing temporal action detection (TAD) methods rely on large training data including segment-level annotations, limited to recognizing previously seen classes alone during infere…
Semi-Supervised Temporal Action Detection with Proposal-Free Masking
Sauradip Nag, Xiatian Zhu, Yi-Zhe Song +1
Existing temporal action detection (TAD) methods rely on a large number of training data with segment-level annotations. Collecting and annotating such a training set is thus highl…
How Far Can I Go ? : A Self-Supervised Approach for Deterministic Video Depth Forecasting
Sauradip Nag, Nisarg Shah, Anran Qi +1
In this paper we present a novel self-supervised method to anticipate the depth estimate for a future, unobserved real-world urban scene. This work is the first to explore self-sup…