United States

Previous works on emotion recognition in conversation (ERC) follow a two-step paradigm, which can be summarized as first producing context-independent features via fine-tuning pretrained language models (PLMs) and then analyzing contextual information and dialogue structure information among the extracted features. However, we discover that this paradigm has several limitations. Accordingly, we propose a novel paradigm, i.e., exploring contextual information and dialogue structure information in the fine-tuning step, and adapting the PLM to the ERC task in terms of input text, classification structure, and training strategy. Furthermore, we develop our model BERT-ERC according to the proposed paradigm, which improves ERC performance in three aspects, namely suggestive text, fine-grained classification module, and two-stage training. Compared to existing methods, BERT-ERC achieves substantial improvement on four datasets, indicating its effectiveness and generalization capability. Besides, we also set up the limited resources scenario and the online prediction scenario to approximate real-world scenarios. Extensive experiments demonstrate that the proposed paradigm significantly outperforms the previous one and can be adapted to various scenes.

AAAI 2023

BERT-ERC: Fine-tuning BERT is Enough for Emotion Recognition in Conversation

conversational ai/dialogue systems

text classification

language models

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

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AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

poster

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

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The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

As textual attributes like font are core design elements of document format and page style, automatic attributes recognition favor comprehensive practical applications. Existing approaches already yield satisfactory performance in differentiating disparate attributes, but they still suffer in distinguishing similar attributes with only subtle difference. Moreover, their performance drop severely in real-world scenarios where unexpected and obvious imaging distortions appear. In this paper, we aim to tackle these problems by proposing TaCo, a contrastive framework for textual attribute recognition tailored toward the most common document scenes. Specifically, TaCo leverages contrastive learning to dispel the ambiguity trap arising from vague and open-ended attributes. 
To realize this goal, we design the learning paradigm from three perspectives: 1) generating attribute views, 2) extracting subtle but crucial details, and 3) exploiting valued view pairs for learning, to fully unlock the pre-training potential. 
Extensive experiments show that TaCo surpasses the supervised counterparts and advances the state-of-the-art remarkably on multiple attribute recognition tasks. Online services of TaCo will be made available.

TaCo: Textual Attribute Recognition via Contrastive Learning

Evaluating neural network performance is critical to deep neural network design but a costly procedure. Neural predictors provide an efficient solution by treating architectures as samples and learning to estimate their performance on a given task. However, existing predictors are task-dependent, predominantly estimating neural network performance on image classification benchmarks. They are also search-space dependent; each predictor is designed to make predictions for a specific architecture search space with predefined topologies and set of operations. In this paper, we propose a novel All-in-One Predictor (AIO-P), which aims to pretrain neural predictors on architecture examples from multiple, separate
computer vision (CV) task domains and multiple architecture spaces, and then transfer to unseen downstream CV tasks or neural architectures. We describe our proposed techniques for general graph representation, efficient predictor pretraining and knowledge infusion techniques, as well as methods to transfer to downstream tasks/spaces. Extensive experimental results show that AIO-P can achieve Mean Absolute Error (MAE) and Spearman’s Rank Correlation (SRCC) below 1% and above 0.5, respectively, on a breadth of target downstream CV tasks with or without fine-tuning, outperforming a number of baselines. Moreover, AIO-P can directly transfer to new architectures not seen during training, accurately rank them and serve as an effective performance estimator when
paired with an algorithm designed to preserve performance while reducing FLOPs.


AIO-P: Expanding Neural Performance Predictors Beyond Image Classification

We study the formation of stable outcomes via simple dynamics in cardinal hedonic games, where the utilities of agents change over time depending on the history of the coalition formation process. Specifically, we analyze situations where members of a coalition decrease their utility for a leaving agent (resent) or increase their utility for a joining agent (appreciation). We show that, in contrast to classical dynamics, for resentful or appreciative agents, dynamics are guaranteed to converge under mild conditions for various stability concepts. Thereby, we establish that both resent and appreciation are strong stability driving forces.

Causes of Stability in Dynamic Coalition Formation

Massive collection and explosive growth of biomedical data, demands effective compression for efficient storage, transmission and sharing. Readily available visual data compression techniques have been studied extensively but tailored for natural images/videos, and thus show limited performance on biomedical data which are of different features and larger diversity. Emerging implicit neural representation (INR) is gaining momentum and demonstrates high promise for fitting diverse visual data in target-data-specific manner, but a general compression scheme covering diverse biomedical data is so far absent. To address this issue, we firstly derive a mathematical explanation for INR's spectrum concentration property and an analytical insight on the design of INR based compressor. Further, we propose a Spectrum Concentrated Implicit neural compression (SCI) which adaptively partitions the complex biomedical data into blocks matching INR's concentrated spectrum envelop, and design a funnel shaped neural network capable of representing each block with a small number of parameters. Based on this design, we conduct compression via optimization under given budget and allocate the available parameters with high representation accuracy. The experiments show SCI's superior performance to state-of-the-art methods including commercial compressors, data-driven ones, and INR based counterparts on diverse biomedical data. The source code can be found at https://github.com/RichealYoung/ImplicitNeuralCompression.git.

SCI: A Spectrum Concentrated Implicit Neural Compression for Biomedical Data

Over the past few years, the research on vision-and-language navigation (VLN) has made tremendous progress. Many previous works attempted to improve the performance from different aspects like training strategy, data augmentation, pre-training, etc. This work focuses on a rarely-explored aspect in VLN, namely the trajectory organization and encoding during the navigation. Most of existing state-of-the-art VLN models adopt a vanilla sequential strategy for encoding the trajectories. Such strategy takes the whole trajectory as a single sequence to estimate the current state,  no matter whether the agent moved smoothly or perhaps made mistakes and backtracked in the past. We show that the sequential encoding may largely lose this kind of fine-grained structure in the trajectory, which could hamper the later state estimation and decision making. In order to solve this problem, this work proposes a novel tree-structured trajectory encoding strategy. The whole trajectory is organized as a tree rooted from the starting position, and encoded using our Tree-Transformer module to fully extract the fine-grained historical information. Besides, as the spatial topology could be easily embedded in the trajectory tree, we further design a tree-based action space to allow the agent making long-range error-correction in one decision. We implement the holistic agent based on cross-modal transformer and train it with a newly-proposed Tree-nDTW reward. On the benchmark dataset R2R, our model achieves a surpassing success rate (SR) of 68\% on \texttt{val-unseen} and 66\% on \texttt{test}. We further conduct extensive ablation studies and analyses to provide more insights for the effectiveness our designs.

Tree-Structured Trajectory Encoding for Vision-and-Language Navigation

The present paper introduces sparsely supervised instance segmentation, with the datasets being fully annotated bounding boxes and sparsely annotated masks. A direct solution to this task is self-training, which is not fully explored for instance segmentation yet. In this paper, we propose MaskBooster for sparsely supervised instance segmentation (SpSIS) with comprehensive usage of pseudo masks. MaskBooster is featured with (1) dynamic and progressive pseudo masks from an online updating teacher model, (2) refining binary pseudo masks with the help of bounding box prior, (3) learning inter-class prediction distribution via knowledge distillation for soft pseudo masks. As an end-to-end and universal self-training framework, MaskBooster can empower fully supervised algorithms and boost their segmentation performance on SpSIS. Abundant experiments are conducted on COCO and BDD100K datasets and validate the effectiveness of MaskBooster. Specifically, on COCO 0.1\%/1\%/10\% protocols and BDD100K, we surpass sparsely supervised baseline by a large margin for both Mask RCNN and ShapeProp. MaskBooster on SpSIS also outperforms weakly and semi-supervised instance segmentation state-of-the-art on the datasets with similar annotation budgets.


MaskBooster: End-to-End Self-Training for Sparsely Supervised Instance Segmentation

Contrastive learning based methods, such as unsup-SimCSE, have achieved state-of-the-art (SOTA) performances in learning unsupervised sentence embeddings.
However, in previous studies, each embedding used for contrastive learning only derived from one sentence instance, and we call these embeddings \textbf{instance-level} embeddings. In other words, each embedding is regarded as a unique class of its own, which may hurt the generalization performance. In this study, we propose IS-CSE (\underline{i}nstance \underline{s}moothing \underline{c}ontrastive \underline{s}entence \underline{e}mbedding) to smooth the boundaries of embedding in the feature space. Specifically, we retrieve embeddings from a dynamic memory buffer according to the semantic similarity to get a positive embedding group. Then embeddings in the group are aggregated by a self-attention operation to produce a \textbf{smoothed instance} embedding for further analysis. We evaluate our method on standard semantic text similarity (STS) tasks and achieve an average of  $78.30\%$,  $79.47\%$,  $77.73\%$ and  $79.42\%$ Spearman's correlation on the base of BERT-base, BERT-large, RoBERTa-base, and RoBERTa-large respectively, a $2.05\%$,  $1.06\%$,  $1.16\%$ and  $0.52\%$ improvement compared to unsup-SimCSE. 

Instance Smoothed Contrastive Learning for Unsupervised Sentence Embedding

  CountSketch and Feature Hashing  (the ``hashing trick'')  are popular randomized dimensionality reduction methods that support recovery of $\ell_2$-heavy hitters (keys $i$ where $v_i^2 > \epsilon \|\boldsymbol{v}\|_2^2$) and approximate inner products. When the inputs are {\em not adaptive} (do not depend on prior outputs), classic estimators applied to a sketch of size $O(\ell/\epsilon)$ are accurate for a number of queries that is exponential in $\ell$.  When inputs are adaptive, however, an adversarial input can be constructed after $O(\ell)$ queries with the classic estimator and the best known robust estimator only supports $\tilde{O}(\ell^2)$ queries.  In this work we show that this quadratic dependence is in a sense inherent: We design an attack that after $O(\ell^2)$ queries produces an adversarial input vector whose sketch is highly biased. Our attack uses ``natural'' non-adaptive inputs (only the final adversarial input is chosen adaptively) and universally applies  with any correct estimator, including one that is unknown to the attacker. In that, we expose inherent vulnerability of this fundamental method.

Tricking the Hashing Trick: A Tight Lower Bound on the Robustness of CountSketch to Adaptive Inputs

Sophisticated data augmentations can greatly boost the efficiency of Graph Contrastive Learning (GCL). However, there are works investigating the feature augmentation for unsupervised contrastive learning, although (class-conditional) feature augmentation has been proven useful in supervised learning. Thus, we present a novel/efficient spectral feature argumentation for contrastive learning on graphs (and images). To this end, from two data views, respectively, we estimate a low-rank approximation per feature map and subtract that approximation from the map to obtain its complement. This is achieved by the proposed herein so-called incomplete power iteration, a non-standard power iteration regime which enjoys two valuable byproducts (under the low number of iterations only): (i) relieve the unbalancing spectrum of the feature map, and (ii) injecting the noise into the singular values of the feature map. For two views, we align these complement feature maps as the spectrum-balanced alignment step focuses on less dominant singular values of spectra of both views, whereas the spectral augmentation of singular values does not affect the spectral angle alignment (singular vectors are left intact). We derive the analytical form for the incomplete power iteration to capture its spectrum-balancing effect and the variance of singular values implicitly augmented by the noise. We also show that this augmentation leads to a lower generalization bound. Experiments on graph and image datasets demonstrate the proposed spectral feature augmentation outperforms baselines, and is complementary with other graph augmentation approaches and compatible with various contrastive losses.

Spectral Feature Augmentation for Graph Contrastive Learning and Beyond

Pruning has been an effective solution to reduce the number of computations and the memory requirement in deep learning.
The pruning unit plays an important role in exploiting the GPU resources efficiently. 
The filter is proposed as a simple pruning unit of structured pruning.
However, since the filter is quite large as pruning unit, the accuracy drop is considerable with a high pruning ratio.
GPU rearranges the weight and input tensors into tiles (blocks) for efficient computation. 
To fully utilize GPU resources, this tile structure should be considered, which is the goal of block pruning. 
However, previous block pruning prunes both row vectors and column vectors. 
Pruning of row vectors in a tile corresponds to filter pruning, and it also interferes with column-wise block pruning of the following layer.
In contrast, column vectors are much smaller than row vectors and can achieve lower accuracy drop.
Additionally, if the pruning ratio for each tile is different,
GPU utilization can be limited by imbalanced workloads by irregular-sized blocks.
The same pruning ratio for the weight tiles processed in parallel enables the actual inference process to fully utilize the resources without idle time.
This paper proposes balanced column-wise block pruning, named BCBP, to satisfy two conditions: the column-wise minimal size of the pruning unit and balanced workloads. 
We demonstrate that BCBP is superior to previous pruning methods through comprehensive experiments.

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TaCo: Textual Attribute Recognition via Contrastive Learning

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