The conventional detectors tend to make imbalanced classification and suffer performance
drop, when the distribution of the training data is severely skewed. In this paper, we propose …

One-stage object detection is commonly implemented by optimizing two sub-tasks: object
classification and localization, using heads with two parallel branches, which might lead to a …

This work explored the effects of protein unfolding and cross-linking induced by lipid oxidation
(linoleic acid, OLA) on the gel water-holding capacity (WHC) of beef myofibrillar proteins (…

In this paper, we present a new cross-architecture contrastive learning (CACL) framework
for self-supervised video representation learning. CACL consists of a 3D CNN and a video …

In this paper, we present a one-stage framework TriDet for temporal action detection. Existing
methods often suffer from imprecise boundary predictions due to the ambiguous action …

The goal of this work is to establish a scalable pipeline for expanding an object detector
towards novel/unseen categories, using zero manual annotations. To achieve that, we make the …

Transformers have been successfully applied to computer vision due to its powerful modelling
capacity with self-attention. However, the good performance of transformers heavily …

This work aims at advancing temporal action detection (TAD) using an encoder-decoder
framework with action queries, similar to DETR, which has shown great success in object …

Birds are considered critical indicators of ecosystem condition. Automatic recording devices
have emerged as a trending tool to assist field observations, contributing to biodiversity …

The objective of this paper is to learn a compact representation of image sets for template-based
face recognition. We make the following contributions: first, we propose a network …