Hongye Liu, Ricardo Henao. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

The pretrain-then-finetune paradigm has been widely used in various unimodal and multimodal tasks. However, finetuning all the parameters of a pre-trained model becomes prohibitive as the model size grows exponentially. To address this issue, the adapter mechanism that freezes the pre-trained model and only finetunes a few extra parameters is introduced and delivers promising results. Most studies on adapter architectures are dedicated to unimodal or bimodal tasks, while the adapter architectures for trimodal tasks have not been investigated yet. This paper introduces a novel Long Short-Term Trimodal Adapter (LSTTA) approach for video understanding tasks involving audio, visual, and language modalities. Based on the pre-trained from the three modalities, the designed adapter module is inserted between the sequential blocks to model the dense interactions across the three modalities. Specifically, LSTTA consists of two types of complementary adapter modules, namely the long-term semantic filtering module and the short-term semantic interaction module. The long-term semantic filtering aims to characterize the temporal importance of the video frames and the short-term semantic interaction module models local interactions within short periods. Compared to previous state-of-the-art trimodal learning methods pre-trained on a large-scale trimodal corpus, LSTTA is more flexible and can inherit any powerful unimodal or bimodal models. Experimental results on four typical trimodal learning tasks show the effectiveness of LSTTA over existing state-of-the-art methods.

Recently, transformer-based methods have achieved promising progresses in object detection, as they can eliminate the post-processes like NMS and enrich the deep representations. However, these methods cannot well cope with scene text due to its extreme variance of scales and aspect ratios. In this paper, we present a simple yet effective transformer-based architecture for scene text detection. Different from previous approaches that learn robust deep representations of scene text in a holistic manner, our method performs scene text detection based on a few representative features, which avoids the disturbance by background and reduces the computational cost. Specifically, we first select a few representative features at all scales that are highly relevant to foreground text. Then, we adopt a transformer for modeling the relationship of the sampled features, which effectively divides them into reasonable groups. As each feature group corresponds to a text instance, its bounding box can be easily obtained without any post-processing operation. Using the basic feature pyramid network for feature extraction, our method consistently achieves state-of-the-art results on several popular datasets for scene text detection.

Mingzhou Xu, Longyue Wang, Derek F. Wong, Hongye Liu, Linfeng Song, Lidia S. Chao, Shuming Shi, Zhaopeng Tu. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022.

In this paper, we present a large-scale dataset and establish a baseline for prohibited item discovery in Security Inspection X-ray images. Our dataset, named SIXray, consists of 1,059,231 X-ray images, in which 6 classes of 8,929 prohibited items are manually annotated. It raises a brand new challenge of overlapping image data, meanwhile shares the same properties with existing datasets, including complex yet meaningless contexts and class imbalance. We propose an approach named class-balanced hierarchical refinement (CHR) to deal with these difficulties. CHR assumes that each input image is sampled from a mixture distribution, and that deep networks require an iterative process to infer image contents accurately. To accelerate, we insert reversed connections to different network backbones, delivering high-level visual cues to assist mid-level features. In addition, a class-balanced loss function is designed to maximally alleviate the noise introduced by easy negative samples. We evaluate CHR on SIXray with different ratios of positive/negative samples. Compared to the baselines, CHR enjoys a better ability of discriminating objects especially using mid-level features, which offers the possibility of using a weakly-supervised approach towards accurate object localization. In particular, the advantage of CHR is more significant in the scenarios with fewer positive training samples, which demonstrates its potential application in real-world security inspection.

The growing explosion in the use of surveillance cameras in public security highlights the importance of vehicle search from a large-scale image or video database. However, compared with person re-identification or face recognition, vehicle search problem has long been neglected by researchers in vision community. This paper focuses on an interesting but challenging problem, vehicle re-identification (a.k.a precise vehicle search). We propose a Deep Relative Distance Learning (DRDL) method which exploits a two-branch deep convolutional network to project raw vehicle images into an Euclidean space where distance can be directly used to measure the similarity of arbitrary two vehicles. To further facilitate the future research on this problem, we also present a carefully-organized largescale image database "VehicleID", which includes multiple images of the same vehicle captured by different realworld cameras in a city. We evaluate our DRDL method on our VehicleID dataset and another recently-released vehicle model classification dataset "CompCars" in three sets of experiments: vehicle re-identification, vehicle model verification and vehicle retrieval. Experimental results show that our method can achieve promising results and outperforms several state-of-the-art approaches.

Object detection plays a fundamental role in many content-based video systems. Often, it is still challenging to achieve both a reasonable accuracy and a fairly fast processing speed. In this paper, we propose a new object detection framework which utilizes raw RGB data from the pixel domain and some useful coding information from the compressed domain jointly. Firstly, various pixel-level detection algorithms can be embedded in our framework so as to obtain the preliminary results. Then by segmenting the moving regions from the background with the Hit-times Map (HTM), some false results can be removed and meanwhile the detection process can also be accelerated since the search area for sliding the detection window has been restricted to relatively small regions. After that, an additional regulation process is performed to further refine the preliminary detection results by employing both temporal consistency and spatial compactness in the motion vector(MV) field. The experimental results on two benchmark datasets show that the proposed method achieves a remarkable improvement both in detection accuracy and processing speed.