6 papers
Mitigating Hallucination in Vision-Language Models through Barrier-Regulated Adaptive Closed-form Steering
Soumyadeep Jana, Pulkit Mittal, Sanasam Ranbir Singh
Large vision-language models (LVLMs) often hallucinate objects that are not present in the input image, largely because visual grounding weakens as decoding progresses. Existing in…
Moment-KV: Momentum-Based Decode-Time KV Cache Compression for Long Generation
Soumyadeep Jana, Sagar Nishad, Sanasam Ranbir Singh
Key-Value (KV) cache remains a major bottleneck for deploying Large Language Models (LLMs) in long-generation tasks. Prior work often applies uniform compression across both prefil…
Teaching Sarcasm: Few-Shot Multimodal Sarcasm Detection via Distillation to a Parameter-Efficient Student
Soumyadeep Jana, Sanasam Ranbir Singh
Multimodal sarcasm detection is challenging, especially in low-resource settings where subtle image-text contradictions are hard to learn due to scarce annotated data, which hinder…
Adapter-state Sharing CLIP for Parameter-efficient Multimodal Sarcasm Detection
Soumyadeep Jana, Sahil Danayak, Sanasam Ranbir Singh
The growing prevalence of multimodal image-text sarcasm on social media poses challenges for opinion mining systems. Existing approaches rely on full fine-tuning of large models, m…
Think Twice Before You Judge: Mixture of Dual Reasoning Experts for Multimodal Sarcasm Detection
Soumyadeep Jana, Abhrajyoti Kundu, Sanasam Ranbir Singh
Multimodal sarcasm detection has attracted growing interest due to the rise of multimedia posts on social media. Understanding sarcastic image-text posts often requires external co…
Dual Modality-Aware Gated Prompt Tuning for Few-Shot Multimodal Sarcasm Detection
Soumyadeep Jana, Abhrajyoti Kundu, Sanasam Ranbir Singh
The widespread use of multimodal content on social media has heightened the need for effective sarcasm detection to improve opinion mining. However, existing models rely heavily on…