Papers with image recognition
Aspect-Based Emotion Analysis and Multimodal Coreference: A Case Study of Customer Comments on Adidas Instagram Posts (2022.lrec-1)
Copied to clipboard
| Challenge: | Aspect-based sentiment analysis of user-generated content has been relatively unexplored in recent years. |
| Approach: | They present a multimodal dataset for Aspect-Based Emotion Analysis (ABEA) they take the first steps in investigating the utility of multimodal coreference resolution in an ABEA framework. |
| Outcome: | The proposed dataset consists of 4,900 comments on 175 images and is annotated with aspect and emotion categories and the emotional dimensions of valence and arousal. |
Fine-grained Interest Matching for Neural News Recommendation (2020.acl-main)
Copied to clipboard
| Challenge: | Existing studies represent each user as a single vector and then match the candidate news vector, which may lose fine-grained information for recommendation. |
| Approach: | They propose a Fine-grained interest matching method for neural news recommendation based on multi-level representations and fine-grain matching between segment pairs of each browsed news and the candidate news at each semantic level. |
| Outcome: | The proposed model can capture more fine-grained interest matching signals by performing interactions between each pair of news at multi-level semantic granularities. |
See More, Store Less: Memory-Efficient Resolution for Video Moment Retrieval (2026.findings-eacl)
Copied to clipboard
| Challenge: | Existing video moment retrieval methods rely on sparse frame sampling, risking information loss. |
| Approach: | a new video-based framework enhances memory efficiency while maintaining high information resolution . SMORE uses query-guided captions to encode semantics aligned with user intent . |
| Outcome: | a new framework improves memory efficiency while maintaining high information resolution . it achieves state-of-the-art performance on QVHighlights, Charades-STA, and ActivityNet-Captions benchmarks . |
Testing the Depth of ChatGPT’s Comprehension via Cross-Modal Tasks Based on ASCII-Art: GPT3.5’s Abilities in Regard to Recognizing and Generating ASCII-Art Are Not Totally Lacking (2024.findings-eacl)
Copied to clipboard
| Challenge: | In the months since its release, ChatGPT and its underlying model, GPT3.5, have garnered massive attention due to their potent mix of capability and accessibility. |
| Approach: | They examine GPT3.5's aptitude for visual tasks using ASCII-art without overt distillation into a lingual summary. |
| Outcome: | The proposed model performs well on image recognition and generation tasks. |
Multimedia Event Extraction with LLM Knowledge Editing (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing multimodal event extraction methods focus on weakly aligning features from wellpretrained unimodal encoders, resulting in redundant feature perception. |
| Approach: | They propose a multimodal event extraction strategy with a redundant feature selection mechanism that enhances event understanding ability of multimodal large language models. |
| Outcome: | The proposed method outperforms the state-of-the-art (SOTA) baselines on the M2E2 benchmark. |
Soft-Prompting with Graph-of-Thought for Multi-modal Representation Learning (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing approaches to learn multi-modal tasks are based on chain-of-thought . however, human thought processes are non-linear and employ dynamic adjustment and updating mechanisms. |
| Approach: | They propose a chain-of-thought technique that adjusts the length of the chain to improve the performance of generated prompts. |
| Outcome: | The proposed model improves multi-modal representation learning in visual, visual, and audio-visual tasks and also has good domain generalization performance due to better reasoning. |