GenAI4ELab/papercli-papers huggingface dataset:
Trace Controlled Text to Image Generation
Authors: Kun Yan; Lei Ji; Chenfei Wu; Jianmin Bao; Ming Zhou; Nan Duan; Shuai Ma
Venue: ECCV 2022 (main)

Abstract: "Text to Image generation is a fundamental and inevitable challenging task for visual linguistic modeling. The recent surge in this area such as DALL·E has shown breathtaking technical breakthroughs, however, it still lacks a precise control of the spatial relation corresponding to semantic text. To tackle this problem, mouse trace paired with text provides an interactive way, in which users can describe the imagined image with natural language while drawing traces to locate those they want. However, this brings the challenges of both controllability and compositionality of the generation. Motivated by this, we propose a Trace Controlled Text to Image Generation model (TCTIG), which takes trace as a bridge between semantic concepts and spatial conditions. Moreover, we propose a set of new technique to enhance the controllability and compositionality of generation, including trace guided re-weighting loss (TGR) and semantic aligned augmentation (SAA). In addition, we establish a solid benchmark for the trace-controlled text-to-image generation task, and introduce several new metrics to evaluate both the controllability and compositionality of the model. Upon that, we demonstrate TCTIG’s superior performance and further present the fruitful qualitative analysis of our model."
Video Question Answering with Iterative Video-Text Co-Tokenization
Authors: AJ Piergiovanni; Kairo Morton; Weicheng Kuo; Michael S. Ryoo; Anelia Angelova
Venue: ECCV 2022 (main)

Abstract: "Video question answering is a challenging task that requires understanding jointly the language input, the visual information in individual video frames, as well as the temporal information about the events occurring in the video. In this paper, we propose a novel multi-stream video encoder for video question answering that uses multiple video inputs and a new video-text iterative co-tokenization approach to answer a variety of questions related to videos. We experimentally evaluate the model on several datasets, such as MSRVTT-QA, MSVD-QA, IVQA, outperforming the previous state-of-the-art by large margins. Simultaneously, our model reduces the required GFLOPs from 150-360 to only 67, producing a highly efficient video question answering model."
Rethinking Data Augmentation for Robust Visual Question Answering
Authors: Long Chen; Yuhang Zheng; Jun Xiao
Venue: ECCV 2022 (main)

Abstract: "Data Augmentation (DA) --- generating extra training samples beyond original training set --- has been widely-used in today’s unbiased VQA models to mitigate the language biases. Current mainstream DA strategies are synthetic-based methods, which synthesize new samples by either editing some visual regions/words, or re-generating them from scratch. However, these synthetic samples are always unnatural and error-prone. To avoid this issue, a recent DA work composes new augmented samples by randomly pairing pristine images and other human-written questions. Unfortunately, to guarantee augmented samples have reasonable ground-truth answers, they manually design a set of heuristic rules for several question types, which extremely limits its generalization abilities. To this end, we propose a new Knowledge Distillation based Data Augmentation for VQA, dubbed KDDAug. Specifically, we first relax the requirements of reasonable image-question pairs, which can be easily applied to any question types. Then, we design a knowledge distillation (KD) based answer assignment to generate pseudo answers for all composed image-question pairs, which are robust to both in-domain and out-of-distribution settings. Since KDDAug is a model-agnostic DA strategy, it can be seamlessly incorporated into any VQA architectures. Extensive ablation studies on multiple backbones and benchmarks have demonstrated the effectiveness and generalization abilities of KDDAug."
Explicit Image Caption Editing
Authors: Zhen Wang; Long Chen; Wenbo Ma; Guangxing Han; Yulei Niu; Jian Shao; Jun Xiao
Venue: ECCV 2022 (main)

Abstract: "Given an image and a reference caption, the image caption editing task aims to correct the misalignment errors and generate a refined caption. However, all existing caption editing works are implicit models, ie, they directly produce the refined captions without explicit connections to the reference captions. In this paper, we introduce a new task: Explicit Caption Editing (ECE). ECE models explicitly generate a sequence of edit operations, and this edit operation sequence can translate the reference caption into a refined one. Compared to the implicit editing, ECE has multiple advantages: 1) Explainable: it can trace the whole editing path. 2) Editing Efficient: it only needs to modify a few words. 3) Human-like: it resembles the way that humans perform caption editing, and tries to keep original sentence structures. To solve this new task, we propose the first ECE model: TIger. TIger is a non-autoregressive transformer-based model, consisting of three modules: Tagger_del, Tagger_add, and Inserter. Specifically, Tagger_del decides whether each word should be preserved or not, Tagger_add decides where to add new words, and Inserter predicts the specific word for adding. To further facilitate ECE research, we propose two new ECE benchmarks by re-organizing two existing datasets, dubbed COCO-EE and Flickr30K-EE, respectively. Extensive ablations on both two benchmarks have demonstrated the effectiveness of TIger."
Can Shuffling Video Benefit Temporal Bias Problem: A Novel Training Framework for Temporal Grounding
Authors: Jiachang Hao; Haifeng Sun; Pengfei Ren; Jingyu Wang; Qi Qi; Jianxin Liao
Venue: ECCV 2022 (main)

Abstract: "Temporal grounding aims to locate a target video moment that semantically corresponds to the given sentence query in an untrimmed video. However, recent works find that existing methods suffer a severe temporal bias problem. These methods do not reason the target moment locations based on the visual-textual semantic alignment but over-rely on the temporal biases of queries in training sets. To this end, this paper proposes a novel training framework for grounding models to use shuffled videos to address temporal bias problem without losing grounding accuracy. Our framework introduces two auxiliary tasks, cross-modal matching and temporal order discrimination, to promote the grounding model training. The cross-modal matching task leverages the content consistency between shuffled and original videos to force the grounding model to mine visual contents to semantically match queries. The temporal order discrimination task leverages the difference in temporal order to strengthen the understanding of long-term temporal contexts. Extensive experiments on Charades-STA and ActivityNet Captions demonstrate the effectiveness of our method for mitigating the reliance on temporal biases and strengthening the model’s generalization ability against the different temporal distributions. Code is available at https://github.com/haojc/ShufflingVideosForTSG."
Reliable Visual Question Answering: Abstain Rather Than Answer Incorrectly
Authors: Spencer Whitehead; Suzanne Petryk; Vedaad Shakib; Joseph Gonzalez; Trevor Darrell; Anna Rohrbach; Marcus Rohrbach
Venue: ECCV 2022 (main)

Abstract: Machine learning has advanced dramatically, narrowing the accuracy gap to humans in multimodal tasks like visual question answering (VQA). However, while humans can say "I don’t know" when they are uncertain (i.e., abstain from answering a question), such ability has been largely neglected in multimodal research, despite the importance of this problem to the usage of VQA in real settings. In this work, we promote a problem formulation for reliable VQA, where we prefer abstention over providing an incorrect answer. We first enable abstention capabilities for several VQA models, and analyze both their coverage, the portion of questions answered, and risk, the error on that portion. For that, we explore several abstention approaches. We find that although the best performing models achieve over 71% accuracy on the VQA v2 dataset, introducing the option to abstain by directly using a model’s softmax scores limits them to answering less than 8% of the questions to achieve a low risk of error (i.e., 1%). This motivates us to utilize a multimodal selection function to directly estimate the correctness of the predicted answers, which we show can increase the coverage by, for example, 2.4x from 6.8% to 16.3% at 1% risk. While it is important to analyze both coverage and risk, these metrics have a trade-off which makes comparing VQA models challenging. To address this, we also propose an Effective Reliability metric for VQA that places a larger cost on incorrect answers compared to abstentions. This new problem formulation, metric, and analysis for VQA provide the groundwork for building effective and reliable VQA models that have the self-awareness to abstain if and only if they don’t know the answer.
GRIT: Faster and Better Image Captioning Transformer Using Dual Visual Features
Authors: Van-Quang Nguyen; Masanori Suganuma; Takayuki Okatani
Venue: ECCV 2022 (main)

Abstract: "Current state-of-the-art methods for image captioning employ region-based features, as they provide object-level information that is essential to describe the content of images; they are usually extracted by an object detector such as Faster R-CNN. However, they have several issues, such as lack of contextual information, the risk of inaccurate detection, and the high computational cost. The first two could be resolved by additionally using grid-based features. However, how to extract and fuse these two types of features is uncharted. This paper proposes a Transformer-only neural architecture, dubbed GRIT (Grid- and Region-based Image captioning Transformer), that effectively utilizes the two visual features to generate better captions. GRIT replaces the CNN-based detector employed in previous methods with a DETR-based one, making it computationally faster. Moreover, its monolithic design consisting only of Transformers enables end-to-end training of the model. This innovative design and the integration of the dual visual features bring about significant performance improvement. The experimental results on several image captioning benchmarks show that GRIT outperforms previous methods in inference accuracy and speed."
Selective Query-Guided Debiasing for Video Corpus Moment Retrieval
Authors: Sunjae Yoon; Ji Woo Hong; Eunseop Yoon; Dahyun Kim; Junyeong Kim; Hee Suk Yoon; Chang D. Yoo
Venue: ECCV 2022 (main)

Abstract: "Video moment retrieval (VMR) aims to localize target moments in untrimmed videos pertinent to a given textual query. Existing retrieval systems tend to rely on retrieval bias as a shortcut and thus, fail to sufficiently learn multi-modal interactions between query and video. This retrieval bias stems from learning frequent co-occurrence patterns between query and moments, which spuriously correlate objects (e.g., a pencil) referred in the query with moments (e.g., scene of writing with a pencil) where the objects frequently appear in the video, such that they converge into biased moment predictions. Although recent debiasing methods have focused on removing this retrieval bias, we argue that these biased predictions sometimes should be preserved because there are many queries where biased predictions are rather helpful. To conjugate this retrieval bias, we propose a Selective Query-guided Debiasing network (SQuiDNet), which incorporates the following two main properties: (1) Biased Moment Retrieval that intentionally uncovers the biased moments inherent in objects of the query and (2) Selective Query-guided Debiasing that performs selective debiasing guided by the meaning of the query. Our experimental results on three moment retrieval benchmarks (i.e., TVR, ActivityNet, DiDeMo) show the effectiveness of SQuiDNet and qualitative analysis shows improved interpretability."
Spatial and Visual Perspective-Taking via View Rotation and Relation Reasoning for Embodied Reference Understanding
Authors: Cheng Shi; Sibei Yang
Venue: ECCV 2022 (main)

Abstract: "Embodied Reference Understanding studies the reference understanding in an embodied fashion, where a receiver requires to locate a target object referred to by both language and gesture of the sender in a shared physical environment. Its main challenge lies in how to make the receiver with the egocentric view access spatial and visual information relative to the sender to judge how objects are oriented around and seen from the sender, i.e., spatial and visual perspective-taking. In this paper, we propose a REasoning from your Perspective (REP) method to tackle the challenge by modeling relations between the receiver and the sender as well as the sender and the objects via the proposed novel view rotation and relation reasoning. Specifically, view rotation first rotates the receiver to the position of the sender by constructing an embodied 3D coordinate system with the position of the sender as the origin. Then, it changes the orientation of the receiver to the orientation of the sender by encoding the body orientation and gesture of the sender. Relation reasoning models both the nonverbal and verbal relations between the sender and the objects by multi-modal cooperative reasoning in gesture, language, visual content, and spatial position. Experiment results demonstrate the effectiveness of REP, which consistently surpasses all existing state-of-the-art algorithms by a large margin, i.e., +5.22% absolute accuracy in terms of Prec@0.5 on YouRefIt. Code is available at https://github.com/ChengShiest/REP-ERU"
Object-Centric Unsupervised Image Captioning
Authors: Zihang Meng; David Yang; Xuefei Cao; Ashish Shah; Ser-Nam Lim
Venue: ECCV 2022 (main)

Abstract: "Image captioning is a longstanding problem in the field of computer vision and natural language processing. To date, researchers have produced impressive state-of-the-art performance in the age of deep learning. Most of these state-of-the-art, however, requires large volume of annotated image-caption pairs in order to train their models. When given an image dataset of interests, practitioner needs to annotate the caption for each image in the training set and this process needs to happen for each newly collected image dataset. In this paper, we explore the task of unsupervised image captioning which utilizes unpaired images and texts to train the model so that the texts can come from different sources than the images. A main school of research on this topic that has been shown to be effective is to construct pairs from the images and texts in the training set according to their overlap of objects. Unlike in the supervised setting, these constructed pairings are however not guaranteed to have fully overlapping set of objects. Our work in this paper overcomes this by harvesting objects corresponding to a given sentence from the training set, even if they don’t belong to the same image. When used as input to a transformer, such mixture of objects enable larger if not full object coverage, and when supervised by the corresponding sentence, produced results that outperform current state of the art unsupervised methods by a significant margin. Building upon this finding, we further show that (1) additional information on relationship between objects and attributes of objects also helps in boosting performance; and (2) our method also extends well to non-English image captioning, which usually suffers from a scarcer level of annotations. Our findings are supported by strong empirical results. Our code is available at \href{https://github.com/zihangm/obj-centric-unsup-caption}{https://github.com/zihangm/obj-centric-unsup-caption}"
Contrastive Vision-Language Pre-training with Limited Resources
Authors: Quan Cui; Boyan Zhou; Yu Guo; Weidong Yin; Hao Wu; Osamu Yoshie; Yubo Chen
Venue: ECCV 2022 (main)

Abstract: "Pioneering dual-encoder pre-training works (e.g., CLIP and ALIGN) have revealed the potential of aligning multi-modal representations with contrastive learning. However, these works require a tremendous amount of data and computational resources (e.g., billion-level web data and hundreds of GPUs), which prevent researchers with limited resources from reproduction and further exploration. To this end, we propose a stack of novel methods, which significantly cut down the heavy resource dependency and allow us to conduct dual-encoder multi-modal representation alignment with limited resources. Besides, we provide a reproducible baseline of competitive results, namely ZeroVL, with only 14M publicly accessible academic datasets and 8 V100 GPUs. Additionally, we collect 100M web data for pre-training, and achieve comparable or superior results than state-of-the-art methods, further proving the effectiveness of our methods on large-scale data. We hope that this work will provide useful data points and experience for future research in contrastive vision-language pre-training. Code is available at https://github.com/zerovl/ZeroVL."
Learning Linguistic Association towards Efficient Text-Video Retrieval
Authors: Sheng Fang; Shuhui Wang; Junbao Zhuo; Xinzhe Han; Qingming Huang
Venue: ECCV 2022 (main)

Abstract: "Text-video retrieval attracts growing attention recently. A dominant approach is to learn a common space for aligning two modalities. However, video deliver richer content than text in general situations and captions usually miss certain events or details in the video. The information imbalance between two modalities makes it difficult to align their representations. In this paper, we propose a general framework, LINguistic ASsociation (LINAS), which utilizes the complementarity between captions corresponding to the same video. Concretely, we first train a teacher model taking extra relevant captions as inputs, which can aggregate language semantics for obtaining more comprehensive text representations. Since the additional captions are inaccessible during inference, Knowledge Distillation is employed to train a student model with a single caption as input. We further propose Adaptive Distillation strategy, which allows the student model to adaptively learn the knowledge from the teacher model. This strategy also suppresses the spurious relations introduced during the linguistic association. Extensive experiments demonstrate the effectiveness and efficiency of LINAS with various baseline architectures on benchmark datasets."
ASSISTER: Assistive Navigation via Conditional Instruction Generation
Authors: Zanming Huang; Zhongkai Shangguan; Jimuyang Zhang; Gilad Bar; Matthew Boyd; Eshed Ohn-Bar
Venue: ECCV 2022 (main)

Abstract: "We introduce a novel vision-and-language navigation (VLN) task of learning to provide real-time guidance to a blind follower situated in complex dynamic navigation scenarios. Towards exploring real-time information needs and fundamental challenges in our novel modeling task, we first collect a multi-modal real-world benchmark with in-situ Orientation and Mobility (O&M) instructional guidance. Subsequently, we leverage the real-world study to inform the design of a larger-scale simulation benchmark, thus enabling comprehensive analysis of limitations in current VLN models. Motivated by how sighted O&M guides seamlessly and safely support the awareness of individuals with visual impairments when collaborating on navigation tasks, we present ASSISTER, an imitation-learned agent that can embody such effective guidance. The proposed assistive VLN agent is conditioned on navigational goals and commands for generating instructional sentences that are coherent with the surrounding visual scene, while also carefully accounting for the immediate assistive navigation task. Altogether, our introduced evaluation and training framework takes a step towards scalable development of the next generation of seamless, human-like assistive agents."
X-DETR: A Versatile Architecture for Instance-Wise Vision-Language Tasks
Authors: Zhaowei Cai; Gukyeong Kwon; Avinash Ravichandran; Erhan Bas; Zhuowen Tu; Rahul Bhotika; Stefano Soatto
Venue: ECCV 2022 (main)

Abstract: "In this paper, we study the challenging instance-wise vision-language tasks, where the free-form language is required to align with the objects instead of the whole image. To address these tasks, we propose X-DETR, whose architecture has three major components: an object detector, a language encoder, and vision-language alignment. The vision and language streams are independent until the end and they are aligned using an efficient dot-product operation. The whole network is trained end-to-end, such that the detector is optimized for the vision-language tasks instead of an off-the-shelf component. To overcome the limited size of paired object-language annotations, we leverage other weak types of supervision to expand the knowledge coverage. This simple yet effective architecture of X-DETR shows good accuracy and fast speeds for multiple instance-wise vision-language tasks, e.g., 16.4 AP on LVIS detection of 1.2K categories at 20 frames per second without using any LVIS annotation during training. The code is available at https://github.com/amazon-research/cross-modal-detr."
Learning Disentanglement with Decoupled Labels for Vision-Language Navigation
Authors: Wenhao Cheng; Xingping Dong; Salman Khan; Jianbing Shen
Venue: ECCV 2022 (main)

Abstract: "Vision-and-Language Navigation (VLN) requires an agent to follow complex natural language instructions and perceive the visual environment for real-world navigation. Intuitively, we find that instruction disentanglement for each viewpoint along the agent’s path is critical for accurate navigation. However, most methods only utilize the whole complex instruction or inaccurate sub-instructions due to the lack of accurate disentanglement as an intermediate supervision stage. To address this problem, we propose a new Disentanglement framework with Decoupled Labels (DDL) for VLN. Firstly, we manually extend the benchmark dataset Room-to-Room with landmark- and action-aware labels in order to provide fine-grained information for each viewpoint. Furthermore, to enhance the generalization ability, we propose a Decoupled Label Speaker module to generate pseudo-labels for augmented data and reinforcement training. To fully use the proposed fine-grained labels, we design a Disentangled Decoding Module to guide discriminative feature extraction and help alignment of multi-modalities. To reveal the generality of our proposed method, we apply it on a LSTM-based model and two recent Transformer-based models. Extensive experiments on two VLN benchmarks (i.e., R2R and R4R) demonstrate the effectiveness of our approach, achieving better performance than previous state-of-the-art methods."