GenAI4ELab/papercli-papers huggingface dataset:
Far3Det: Towards Far-Field 3D Detection
Authors: Shubham Gupta; Jeet Kanjani; Mengtian Li; Francesco Ferroni; James Hays; Deva Ramanan; Shu Kong
Venue: WACV 2023 (main)

Abstract: We focus on the task of far-field 3D detection (Far3Det) of objects beyond a certain distance from an observer, e.g., >50m. Far3Det is particularly important for autonomous vehicles (AVs) operating at highway speeds, which require detections of far-field obstacles to ensure sufficient braking distances. However, contemporary AV benchmarks such as nuScenes underemphasize this problem because they evaluate performance only up to a certain distance (50m). One reason is that obtaining far-field 3D annotations is difficult, particularly for lidar sensors that produce very few point returns for far-away objects. Indeed, we find that almost 50% of far-field objects (beyond 50m) contain zero lidar points. Secondly, current metrics for 3D detection employ a "one-size-fits-all" philosophy, using the same tolerance thresholds for near and far objects, inconsistent with tolerances for both human vision and stereo disparities. Both factors lead to an incomplete analysis of the Far3Det task. For example, while conventional wisdom tells us that high-resolution RGB sensors should be vital for the 3D detection of far-away objects, lidar-based methods still rank higher compared to RGB counterparts on the current benchmark leaderboards. As a first step towards a Far3Det benchmark, we develop a method to find well-annotated scenes from the nuScenes dataset and derive a well-annotated far-field validation set. We also propose a Far3Det evaluation protocol and explore various 3D detection methods for Far3Det. Our result convincingly justifies the long held conventional wisdom that high-resolution RGB improves 3D detection in the far-field. We further propose a simple yet effective method that fuses detections from RGB and lidar detectors based on non-maximum suppression, which remarkably outperforms state-of-the-art 3D detectors in the far-field.
Towards MOOCs for Lipreading: Using Synthetic Talking Heads To Train Humans in Lipreading at Scale
Authors: Aditya Agarwal; Bipasha Sen; Rudrabha Mukhopadhyay; Vinay P. Namboodiri; C. V. Jawahar
Venue: WACV 2023 (main)

Abstract: Many people with some form of hearing loss consider lipreading as their primary mode of day-to-day communication. However, finding resources to learn or improve one's lipreading skills can be challenging. This is further exacerbated in the COVID19 pandemic due to restrictions on direct interactions with peers and speech therapists. Today, online MOOCs platforms like Coursera and Udemy have become the most effective form of training for many types of skill development. However, online lipreading resources are scarce as creating such resources is an extensive process needing months of manual effort to record hired actors. Because of the manual pipeline, such platforms are also limited in vocabulary, supported languages, accents, and speakers and have a high usage cost. In this work, we investigate the possibility of replacing real human talking videos with synthetically generated videos. Synthetic data can easily incorporate larger vocabularies, variations in accent, and even local languages and many speakers. We propose an end-to-end automated pipeline to develop such a platform using state-of-the-art talking head video generator networks, text-to-speech models, and computer vision techniques. We then perform an extensive human evaluation using carefully thought out lipreading exercises to validate the quality of our designed platform against the existing lipreading platforms. Our studies concretely point toward the potential of our approach in developing a large-scale lipreading MOOC platform that can impact millions of people with hearing loss.
Modality Mixer for Multi-Modal Action Recognition
Authors: Sumin Lee; Sangmin Woo; Yeonju Park; Muhammad Adi Nugroho; Changick Kim
Venue: WACV 2023 (main)

Abstract: In multi-modal action recognition, it is important to consider not only the complementary nature of different modalities but also global action content. In this paper, we propose a novel network, named Modality Mixer (M-Mixer) network, to leverage complementary information across modalities and temporal context of an action for multi-modal action recognition. We also introduce a simple yet effective recurrent unit, called Multi-modal Contextualization Unit (MCU), which is a core component of M-Mixer. Our MCU temporally encodes a sequence of one modality (e.g., RGB) with action content features of other modalities (e.g., depth, IR). This process encourages M-Mixer to exploit global action content and also to supplement complementary information of other modalities. As a result, our proposed method outperforms state-of-the-art methods on NTU RGB+D 60, NTU RGB+D 120, and NWUCLA datasets. Moreover, we demonstrate the effectiveness of M-Mixer by conducting comprehensive ablation studies.
Relaxing Contrastiveness in Multimodal Representation Learning
Authors: Zudi Lin; Erhan Bas; Kunwar Yashraj Singh; Gurumurthy Swaminathan; Rahul Bhotika
Venue: WACV 2023 (main)

Abstract: Multimodal representation learning for images with paired raw texts can improve the usability and generality of the learned semantic concepts while significantly reducing annotation costs. In this paper, we explore the design space of loss functions in visual-linguistic pretraining frameworks and propose a novel Relaxed Contrastive (ReCo) objective, which acts as a drop-in replacement of the widely used InfoNCE loss. The key insight of ReCo is to allow a relaxed negative space by not penalizing unpaired multimodal samples (ie, negative pairs) that are already orthogonal or negatively correlated. Unlike the widely-used InfoNCE, which keeps repelling negative pairs as long as they are not anti-correlated, ReCo by design embraces more diversity and flexibility of the learned embeddings. We conduct extensive experiments using ReCo with state-of-the-art models by pretraining on the MIMIC-CXR dataset that consists of chest radiographs and free-text radiology reports, and evaluating on the CheXpert dataset for multimodal retrieval and disease classification. Our ReCo achieves an absolute improvement of 2.9% over the InfoNCE baseline on the CheXpert Retrieval dataset in average retrieval precision and reports better or comparable performance in the linear evaluation and finetuning for classification. We further show that ReCo outperforms InfoNCE on the Flickr30K dataset by 1.7% in retrieval Recall@1, demonstrating the generalizability of our approach to natural images.
Towards Disturbance-Free Visual Mobile Manipulation
Authors: Tianwei Ni; Kiana Ehsani; Luca Weihs; Jordi Salvador
Venue: WACV 2023 (main)

Abstract: Deep reinforcement learning has shown promising results on an abundance of robotic tasks in simulation, including visual navigation and manipulation. Prior work generally aims to build embodied agents that solve their assigned tasks as quickly as possible, while largely ignoring the problems caused by collision with objects during interaction. This lack of prioritization is understandable: there is no inherent cost in breaking virtual objects. As a result, "well-trained" agents frequently collide with objects before achieving their primary goals, a behavior that would be catastrophic in the real world. In this paper, we study the problem of training agents to complete the task of visual mobile manipulation in the ManipulaTHOR environment while avoiding unnecessary collision (disturbance) with objects. We formulate disturbance avoidance as a penalty term in the reward function, but find that directly training with such penalized rewards often results in agents being unable to escape poor local optima. Instead, we propose a two-stage training curriculum where an agent is first allowed to freely explore and build basic competencies without penalization, after which a disturbance penalty is introduced to refine the agent's behavior. Results on testing scenes show that our curriculum not only avoids these poor local optima, but also leads to 10% absolute gains in success rate without disturbance, compared to our state-of-the-art baselines. Moreover, our curriculum is significantly more performant than a safe RL algorithm that casts collision avoidance as a constraint. Finally, we propose a novel disturbance-prediction auxiliary task that accelerates learning.
Exploiting Instance-Based Mixed Sampling via Auxiliary Source Domain Supervision for Domain-Adaptive Action Detection
Authors: Yifan Lu; Gurkirt Singh; Suman Saha; Luc Van Gool
Venue: WACV 2023 (main)

Abstract: We propose a novel domain adaptive action detection approach and a new adaptation protocol that leverages the recent advancements in image-level unsupervised domain adaptation (UDA) techniques and handle vagaries of instance-level video data. Self-training combined with cross-domain mixed sampling has shown remarkable performance gain in semantic segmentation in UDA (unsupervised domain adaptation) context. Motivated by this fact, we propose an approach for human action detection in videos that transfers knowledge from the source domain (annotated dataset) to the target domain (unannotated dataset) using mixed sampling and pseudo-label-based selftraining. The existing UDA techniques follow a ClassMix algorithm for semantic segmentation. However, simply adopting ClassMix for action detection does not work, mainly because these are two entirely different problems, i.e., pixel-label classification vs. instance-label detection. To tackle this, we propose a novel action instance mixed sampling technique that combines information across domains based on action instances instead of action classes. Moreover, we propose a new UDA training protocol that addresses the long-tail sample distribution and domain shift problem by using supervision from an auxiliary source domain (ASD). For the ASD, we propose a new action detection dataset with dense frame-level annotations. We name our proposed framework as domain-adaptive action instance mixing (DA-AIM). We demonstrate that DA-AIM consistently outperforms prior works on challenging domain adaptation benchmarks. The source code is available at https://github.com/wwwfan628/DA-AIM.
Graph-Based Self-Learning for Robust Person Re-Identification
Authors: Yuqiao Xian; Jinrui Yang; Fufu Yu; Jun Zhang; Xing Sun
Venue: WACV 2023 (main)

Abstract: Existing deep learning approaches for person re-identification (Re-ID) mostly rely on large-scale and well-annotated training data. However, human-annotated labels are prone to label noise in real-world applications. Previous person Re-ID works mainly focus on random label noise, which doesn't properly reflect the characteristic of label noise in practical human-annotated process. In this work, we find the visual ambiguity noise is more common and reasonable noise assumption in annotation of person Re-ID. To handle the kind of noise, we propose a simple and effective robust person Re-ID framework, namely Graph-Based Self-Learning (GBSL), to iteratively learn discriminative representation and rectify noisy labels with limited annotated samples for each identity. Meanwhile, considering the practical annotation process in person Re-ID, we further extend the visual ambiguity noise assumption and propose a type of more practical label noise in person Re-ID, namely the tracklet-level label noise (TLN). Without modifying network architecture or loss function, our approach significantly improves the robustness against label noise of the Re-ID system. Our model obtains competitive performance with training data corrupted by various types of label noise and outperforms the existing methods for robust Re-ID on public benchmarks.
SVD-NAS: Coupling Low-Rank Approximation and Neural Architecture Search
Authors: Zhewen Yu; Christos-Savvas Bouganis
Venue: WACV 2023 (main)

Abstract: The task of compressing pre-trained Deep Neural Networks has attracted wide interest of the research community due to its great benefits in freeing practitioners from data access requirements. In this domain, low-rank approximation is a promising method, but existing solutions considered a restricted number of design choices and failed to efficiently explore the design space, which lead to severe accuracy degradation and limited compression ratio achieved. To address the above limitations, this work proposes the SVD-NAS framework that couples the domains of low-rank approximation and neural architecture search. SVD-NAS generalises and expands the design choices of previous works by introducing the Low-Rank architecture space, LR-space, which is a more fine-grained design space of low-rank approximation. Afterwards, this work proposes a gradient-descent-based search for efficiently traversing the LR-space. This finer and more thorough exploration of the possible design choices results in improved accuracy as well as reduction in parameters, FLOPS, and latency of a CNN model. Results demonstrate that the SVD-NAS achieves 2.06-12.85pp higher accuracy on ImageNet than state-of-the-art methods under the data-limited problem settings. SVD-NAS is open-sourced at https://github.com/Yu-Zhewen/SVD-NAS.
Multi-Level Contrastive Learning for Self-Supervised Vision Transformers
Authors: Shentong Mo; Zhun Sun; Chao Li
Venue: WACV 2023 (main)

Abstract: Recent studies aim to establish contrastive self-supervised learning (CSL) algorithms specialized for the family of Vision Transformers (ViTs) to make them function normally as ordinary convolutional-based backbones in the training progress. Despite obtaining promising performance on related downstream tasks, one compelling property of the ViTs is ignored in those approaches. As previous studies have demonstrated, vision transformers benefit from the early stage global attention mechanics, obtaining feature representations that contain information from distant patches, even in their shallow layers. Motivated by this, we present a simple yet effective framework to facilitate the self-supervised feature learning of transformer-based vision architectures, namely, Multi-level Contrastive learning for Vision Transformers (MCVT). Specifically, we equip the vision transformers with individual-based (InfoNCE) and prototypical-based (ProtoNCE) contrastive loss in different stages of the architecture to capture low-level invariance and high-level invariance between views of samples, respectively. We conduct extensive experiments to demonstrate the effectiveness of the proposed method, using two well-known vision transformer backbones, on several vision downstream tasks, including linear classification, detection, and semantic segmentation.
Do Pre-Trained Models Benefit Equally in Continual Learning?
Authors: Kuan-Ying Lee; Yuanyi Zhong; Yu-Xiong Wang
Venue: WACV 2023 (main)

Abstract: A large part of the continual learning (CL) literature focuses on developing algorithms for models trained from scratch. While these algorithms work great with from-sc ratch trained models on widely used CL benchmarks, they show dramatic performance drops on more complex datasets (e.g., Split-CUB200). Pre-trained models, widely used to transfer knowledge to downstream tasks, could enhance these methods to be applicable in more realistic scenarios. However, surprisingly, improvements in CL algorithms from pre-training are inconsistent. For instance, while Incremental Classifier and Representation Learning (iCaRL) underperforms Supervised Contrastive Replay (SCR) when trained from scratch, it outperforms SCR when both are initialized with a pre-trained model. This indicates the paradigm current CL literature follows, where all methods are compared in from-scratch training, is not well reflective of the true CL objective and desired progress. Furthermore, we found 1) CL algorithms that exert less regularization benefit more from a pre-trained model; 2) a model pre-trained with a larger dataset (WebImageText in Contrastive Language-Image Pre-training (CLIP) vs. ImageNet) does not guarantee a better improvement. Based on these findings, we introduced a simple yet effective baseline that employs minimum regularization and leverages the more beneficial pre-trained model, which outperforms state-of-the-art methods when pre-training is applied. Our code is available at https://github.com/eric11220/pretrained-models-in-CL.
Cross-Domain Video Anomaly Detection Without Target Domain Adaptation
Authors: Abhishek Aich; Kuan-Chuan Peng; Amit K. Roy-Chowdhury
Venue: WACV 2023 (main)

Abstract: Most cross-domain unsupervised Video Anomaly Detection (VAD) works assume that at least few task-relevant target domain training data are available for adaptation from the source to the target domain. However, this requires laborious model-tuning by the end-user who may prefer to have a system that works "out-of-the-box". To address such practical scenarios, we identify a novel target domain (inference-time) VAD task where no target domain training data are available. To this end, we propose a new 'Zero-shot Cross-domain Video Anomaly Detection (zxvad)' framework that includes a future-frame prediction generative model setup. Different from prior future-frame prediction models, our model uses a novel Normalcy Classifier module to learn the features of normal event videos by learning how such features are different "relative" to features in pseudo-abnormal examples. A novel Untrained Convolutional Neural Network based Anomaly Synthesis module crafts these pseudo-abnormal examples by adding foreign objects in normal video frames with no extra training cost. With our novel relative normalcy feature learning strategy, zxvad generalizes and learns to distinguish between normal and abnormal frames in a new target domain without adaptation during inference. Through evaluations on common datasets, we show that zxvad outperforms the state-of-the-art (SOTA), regardless of whether task-relevant (i.e., VAD) source training data are available or not. Lastly, zxvad also beats the SOTA methods in inference-time efficiency metrics including the model size, total parameters, GPU energy consumption, and GMACs.
TeST: Test-Time Self-Training Under Distribution Shift
Authors: Samarth Sinha; Peter Gehler; Francesco Locatello; Bernt Schiele
Venue: WACV 2023 (main)

Abstract: Despite their recent success, deep neural networks continue to perform poorly when they encounter distribution shifts at test time. Many recently proposed approaches try to counter this by aligning the model to the new distribution prior to inference. With no labels available this requires unsupervised objectives to adapt the model on the observed test data. In this paper, we propose Test-Time Self-Training (TeST): a technique that takes as input a model trained on some source data and a novel data distribution at test time, and learns invariant and robust representations using a student-teacher framework. We find that models adapted using TeST significantly improve over baseline test-time adaptation algorithms. TeST achieves competitive performance to modern domain adaptation algorithms [4,43], while having access to 5-10x less data at time of adaption. We thoroughly evaluate a variety of baselines on two tasks: object detection and image segmentation and find that models adapted with TeST. We find that TeST sets the new state-of-the art for test-time domain adaptation algorithms.
IDD-3D: Indian Driving Dataset for 3D Unstructured Road Scenes
Authors: Shubham Dokania; A. H. Abdul Hafez; Anbumani Subramanian; Manmohan Chandraker; C. V. Jawahar
Venue: WACV 2023 (main)

Abstract: Autonomous driving and assistance systems rely on annotated data from traffic and road scenarios to model and learn the various object relations in complex real-world scenarios. Preparation and training of deploy-able deep learning architectures require the models to be suited to different traffic scenarios and adapt to different situations. Currently, existing datasets, while large-scale, lack such diversities and are geographically biased towards mainly developed cities. An unstructured and complex driving layout found in several developing countries such as India poses a challenge to these models due to the sheer degree of variations in the object types, densities, and locations. To facilitate better research toward accommodating such scenarios, we build a new dataset,  IDD-3D , which consists of multi-modal data from multiple cameras and LiDAR sensors with 12k annotated driving LiDAR frames across various traffic scenarios. We discuss the need for this dataset through statistical comparisons with existing datasets and highlight benchmarks on standard 3D object detection and tracking tasks in complex layouts.
CoNMix for Source-Free Single and Multi-Target Domain Adaptation
Authors: Vikash Kumar; Rohit Lal; Himanshu Patil; Anirban Chakraborty
Venue: WACV 2023 (main)

Abstract: This work introduces the novel task of Source-free Multi-target Domain Adaptation and proposes adaptation framework comprising of Consistency with Nuclear-Norm Maximization and MixUp knowledge distillation (CoNMix) as a solution to this problem. The main motive of this work is to solve for Single and Multi target Domain Adaptation (SMTDA) for the source-free paradigm, which enforces a constraint where the labeled source data is not available during target adaptation due to various privacy-related restrictions on data sharing. The source-free approach leverages target pseudo labels, which can be noisy, to improve the target adaptation. We introduce consistency between label preserving augmentations and utilize pseudo label refinement methods to reduce noisy pseudo labels. Further, we propose novel MixUp Knowledge Distillation (MKD) for better generalization on multiple target domains using various source-free STDA models. We also show that the Vision Transformer (VT) backbone gives better feature representation with improved domain transferability and class discriminability. Our proposed framework achieves the state-of-the-art (SOTA) results in various paradigms of source-free STDA and MTDA settings on popular domain adaptation datasets like Office-Home, Office-Caltech, and DomainNet. Project Page: https://sites.google.com/view/conmix-vcl
Temporal Feature Enhancement Dilated Convolution Network for Weakly-Supervised Temporal Action Localization
Authors: Jianxiong Zhou; Ying Wu
Venue: WACV 2023 (main)

Abstract: Weakly-supervised Temporal Action Localization (WTAL) aims to classify and localize action instances in untrimmed videos with only video-level labels. Existing methods typically use snippet-level RGB and optical flow features extracted from pre-trained extractors directly. Because of two limitations: the short temporal span of snippets and the inappropriate initial features, these WTAL methods suffer from the lack of effective use of temporal information and have limited performance. In this paper, we propose the Temporal Feature Enhancement Dilated Convolution Network (TFE-DCN) to address these two limitations. The proposed TFE-DCN has an enlarged receptive field that covers a long temporal span to observe the full dynamics of action instances, which makes it powerful to capture temporal dependencies between snippets. Furthermore, we propose the Modality Enhancement Module that can enhance RGB features with the help of enhanced optical flow features, making the overall features appropriate for the WTAL task. Experiments conducted on THUMOS'14 and ActivityNet v1.3 datasets show that our proposed approach far outperforms state-of-the-art WTAL methods.